Online Tutorial Welcome and Introduction

Overview

Teaching: 5 min
Exercises: 0 min

Questions

• What should I expect in participating in this Tutorial?

Objectives

• Introduce instructors and mentors.

• Provide overview of the modules

• Spotlight helpful network provided by Slack channel.

DUNE Computing Consortium

The DUNE Computing Consortium works to establish a global computing network that will handle the massive data streams produced by distributing these across the computing grid. Selected consortia members coordinate DUNE computing activities to new members to acquaint them with the specific and DUNE software and resources.

DUNE Computing Consortium Coordinator: Michael Kirby (Brookhave National Laboratory)

Schedule

The May 2023 DUNE computing training spanned two days: Indico site.

Instructional Crew

Organizers:

• Heidi Schellman (Oregon State University /FNAL)
• David DeMuth (Valley City State University)

Module Authors (in order of appearance in the schedule):

• Michael Kirby (FNAL): storage spaces
• Steven Timm (FNAL): data management
• Tom Junk (FNAL): art and LArSoft
• Kenneth Herner (FNAL): grid and batch job submission

Mentors

• Amit Bashyal (ANL)
• Aaron Higuera (Rice University)
• Pengfei Ding (FNAL)
• Barnali Chowdury (ANL)
• Lino Gerlach (BNL)
• Nilay Bostan (University of Iowa)

Support

You must be on the DUNE Collaboration member list and have a valid FNAL or CERN account. See the old Indico Requirement page for more information. Windows users are invited to review the Windows Setup page.

You should join the DUNE Slack instance and look in #computing-training-basics for help with this tutorial

go to https://atwork.dunescience.org/tools/ scroll down to Slack and request an invite. Please do not do this if you are already in DUNE Slack.

Key Points

• This tutorial is brought to you by the DUNE Computing Consortium.

• The goals are to give you the computing basis to work on DUNE.

Storage Spaces (2024)

Overview

Teaching: 45 min
Exercises: 0 min

Questions

• What are the types and roles of DUNE’s data volumes?

• What are the commands and tools to handle data?

Objectives

• Understanding the data volumes and their properties

• Displaying volume information (total size, available size, mount point, device location)

• Differentiating the commands to handle data between grid accessible and interactive volumes

This is an updated version of the 2023 training

Introduction

There are four types of storage volumes that you will encounter at Fermilab (or CERN):

• local hard drives
• network attached storage
• large-scale, distributed storage
• Rucio Storage Elements (RSE’s)

Each has its own advantages and limitations, and knowing which one to use when isn’t all straightforward or obvious. But with some amount of foresight, you can avoid some of the common pitfalls that have caught out other users.

Vocabulary

What is POSIX? A volume with POSIX access (Portable Operating System Interface Wikipedia) allow users to directly read, write and modify using standard commands, e.g. using bash scripts, fopen(). In general, volumes mounted directly into the operating system.

What is meant by ‘grid accessible’? Volumes that are grid accessible require specific tool suites to handle data stored there. Grid access to a volume is NOT POSIX access. This will be explained in the following sections.

What is immutable? A file that is immutable means that once it is written to the volume it cannot be modified. It can only be read, moved, or deleted. This property is in general a restriction imposed by the storage volume on which the file is stored. Not a good choice for code or other files you want to change.

Interactive storage volumes (mounted on dunegpvmXX.fnal.gov)

Home area is similar to the user’s local hard drive but network mounted

• access speed to the volume very high, on top of full POSIX access
• network volumes are NOT safe to store certificates and tickets
• important: users have a single home area at FNAL used for all experiments
• not accessible from grid worker nodes
• not for code developement (size of less than 2 GB)
• at Fermilab, need a valid Kerberos ticket in order to access files in your Home area
• periodic snapshots are taken so you can recover deleted files. (/nashome/.snapshot)

Locally mounted volumes are physical disks, mounted directly on the computer

• physically inside the computer node you are remotely accessing
• mounted on the machine through the motherboard (not over network)
• used as temporary storage for infrastructure services (e.g. /var, /tmp,)
• can be used to store certificates and tickets. (These are saved there automatically with owner-read enabled and other permissions disabled.)
• usually very small and should not be used to store data files or for code development
• files on these volumes are not backed up

Network Attached Storage (NAS) element behaves similar to a locally mounted volume.

• functions similar to services such as Dropbox or OneDrive
• fast and stable POSIX access to these volumes
• volumes available only on a limited number of computers or servers
• not available on larger grid computing (FermiGrid, Open Science Grid, etc.)
• /exp/dune/app/….yourdir has periodic snapshots in /exp/dune/app/… yourdir/.snap, but /exp/dune/data do NOT

Grid-accessible storage volumes

At Fermilab, an instance of dCache+Enstore is used for large-scale, distributed storage with capacity for more than 100 PB of storage and O(10000) connections. Whenever possible, these storage elements should be accessed over xrootd (see next section) as the mount points on interactive nodes are slow, unstable, and can cash the node to become unusable. Here are the different dCache volumes:

Persistent dCache: the data in the file is actively available for reads at any time and will not be removed until manually deleted by user There is now a second persistent dCache volume that is dedicated for DUNE Physics groups and managed by the respective physics conveners of those physics group. https://wiki.dunescience.org/wiki/DUNE_Computing/Using_the_Physics_Groups_Persistent_Space_at_Fermilab gives more details on how to get access to these groups. In general if you need to store more than 5TB in persistent dCache you should be working with the Physics Groups areas.

Scratch dCache: large volume shared across all experiments. When a new file is written to scratch space, old files are removed in order to make room for the newer file. removal is based on Least Recently Utilized (LRU) policy.

Tape-backed dCache: disk based storage areas that have their contents mirrored to permanent storage on Enstore tape.
Files are not available for immediate read on disk, but needs to be ‘staged’ from tape first (see video of a tape storage robot).

Resilient dCache: NOTE: DIRECT USAGE is being phased out and if the Rapid Code Distribution function in POMS/jobsub does not work for you, consult with the FIFE team for a solution (handles custom user code for their grid jobs, often in the form of a tarball. Inappropriate to store any other files here (NO DATA OR NTUPLES)).

Rucio Storage Elements: Rucio Storage Elements (or RSEs) are storage elements provided by collaborating institution for official DUNE datasets. Data stored in DUNE RSE’s must be fully cataloged in the [metacat][metacat] catalog and is managed by the DUNE data management team. This is where you find the big official data samples.

Note - When reading from dcache always use the root: syntax, not direct /pnfs

The Fermilab dcache areas have NFS mounts. These are for your convenience, they allow you to look at the directory structure and, for example, remove files. However, NFS access is slow and can hang the machine if I/O heavy processes use it. Always use the xroot root://<site> … when reading/accessing files instead of /pnfs/ directly. Once you have your dune environment set up the pnfs2xrootd command can do the conversion to root: format for you.

Summary on storage spaces

Full documentation: Understanding Storage Volumes

Monitoring and Usage

Remember that these volumes are not infinite, and monitoring your and the experiment’s usage of these volumes is important to smooth access to data and simulation samples. To see your persistent usage visit here (bottom left):

And to see the total volume usage at Rucio Storage Elements around the world:

Resource DUNE Rucio Storage

Note - do not blindly copy files from personal machines to DUNE systems.

You may have files on your personal machine that contain personal information, licensed software or (god forbid) malware or pornography. Do not transfer any files from your personal machine to DUNE machines unless they are directly related to work on DUNE. You must be fully aware of any file’s contents. We have seen it all and we do not want to.

Commands and tools

This section will teach you the main tools and commands to display storage information and access data.

ifdh

Another useful data handling command you will soon come across is ifdh. This stands for Intensity Frontier Data Handling. It is a tool suite that facilitates selecting the appropriate data transfer method from many possibilities while protecting shared resources from overload. You may see ifdhc, where c refers to client.

Note

ifdh is much more efficient than NSF file access. Please use it and/or xroot when accessing remote files.

Here is an example to copy a file. Refer to the Mission Setup for the setting up the DUNELAR_VERSION.

Note

For now do this in the Apptainer

/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash \
-B /cvmfs,/exp,/nashome,/pnfs/dune,/opt,/run/user,/etc/hostname,/etc/hosts,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest

once in the Apptainer

#source ~/dune_presetup_2024.sh
#dune_setup
export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
kx509
export ROLE=Analysis
voms-proxy-init -rfc -noregen -voms=dune:/dune/Role=$ROLE -valid 120:00
setup ifdhc
ifdh cp root://fndcadoor.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/physics/full-reconstructed/2023/mc/out1/MC_Winter2023_RITM1592444_reReco/54/05/35/65/NNBarAtm_hA_BR_dune10kt_1x2x6_54053565_607_20220331T192335Z_gen_g4_detsim_reco_65751406_0_20230125T150414Z_reReco.root /dev/null

Note, if the destination for an ifdh cp command is a directory instead of filename with full path, you have to add the “-D” option to the command line.

Resource: idfh commands

Exercise 1

Using the ifdh command, complete the following tasks:

• create a directory in your dCache scratch area (/pnfs/dune/scratch/users/${USER}/) called “DUNE_tutorial_2024”
• copy /exp/dune/app/users/${USER}/my_first_login.txt file to that directory
• copy the my_first_login.txt file from your dCache scratch directory (i.e. DUNE_tutorial_2024) to /dev/null
• remove the directory DUNE_tutorial_2024
• create the directory DUNE_tutorial_2024_data_file

Note, if the destination for an ifdh cp command is a directory instead of filename with full path, you have to add the “-D” option to the command line. Also, for a directory to be deleted, it must be empty.

ifdh mkdir /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024
ifdh cp -D /exp/dune/app/users/${USER}/my_first_login.txt /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024
ifdh cp /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024/my_first_login.txt /dev/null
ifdh rm /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024/my_first_login.txt
ifdh rmdir /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024
ifdh mkdir /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024_data_file

xrootd

The eXtended ROOT daemon is software framework designed for accessing data from various architectures and in a complete scalable way (in size and performance).

XRootD is most suitable for read-only data access. XRootD Man pages

Issue the following command. Please look at the input and output of the command, and recognize that this is a listing of /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024. Try and understand how the translation between a NFS path and an xrootd URI could be done by hand if you needed to do so.

xrdfs root://fndca1.fnal.gov:1094/ ls /pnfs/fnal.gov/usr/dune/scratch/users/${USER}/

Note that you can do

lar -c <input.fcl> <xrootd_uri> 

to stream into a larsoft module configured within the fhicl file. As well, it can be implemented in standalone C++ as

TFile * thefile = TFile::Open(<xrootd_uri>)

or PyROOT code as

thefile = ROOT.TFile.Open(<xrootd_uri>)

What is the right xroot path for a file.

If a file is in /pnfs/dune/tape_backed/dunepro/protodune-sp/reco-recalibrated/2021/detector/physics/PDSPProd4/00/00/51/41/np04_raw_run005141_0003_dl9_reco1_18127219_0_20210318T104440Z_reco2_51835174_0_20211231T143346Z.root

the command

pnfs2xrootd /pnfs/dune/tape_backed/dunepro/protodune-sp/reco-recalibrated/2021/detector/physics/PDSPProd4/00/00/51/41/np04_raw_run005141_0003_dl9_reco1_18127219_0_20210318T104440Z_reco2_51835174_0_20211231T143346Z.root

will return the correct xrootd uri:

root://fndca1.fnal.gov:1094//pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/reco-recalibrated/2021/detector/physics/PDSPProd4/00/00/51/41/np04_raw_run005141_0003_dl9_reco1_18127219_0_20210318T104440Z_reco2_51835174_0_20211231T143346Z.root

you can then

root -l <that long root: path>

to open the root file.

This even works if the file is in Europe - which you cannot do with a direct /pnfs!

root -l root://dune.dcache.nikhef.nl:1094/pnfs/nikhef.nl/data/dune/generic/rucio/usertests/b4/49/np04hd_raw_run026420_0000_dataflow0_datawriter_0_20240524T170759_20240604T204802_keepup_hists.root

See the next section on data management for instructions on finding files worldwide.

Note Files in /tape_backed/ may not be immediately accessible, those in /persistent/ and /scratch/ are.

Let’s practice

Exercise 2

Using a combination of ifdh and xrootd commands discussed previously:

• Use ifdh locateFile <file> root to find the directory for this file PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root
• Use xrdcp to copy that file to /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_2024_data_file
• Using xrdfs and the ls option, count the number of files in the same directory as PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root

Note that redirecting the standard output of a command into the command wc -l will count the number of lines in the output text. e.g. ls -alrth ~/ | wc -l

ifdh locateFile PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root root
xrdcp root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/01/67/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/scratch/users/${USER}/DUNE_tutorial_2024_data_file/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_off_43352322_0_20210427T162252Z.root
xrdfs root://fndca1.fnal.gov:1094/ ls /pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/01/67/ | wc -l

Is my file available or stuck on tape?

/tape_backed/ storage at Fermilab is migrated to tape and may not be on disk? You can check this by doing the following in an AL9 window

gfal-xattr  <xrootpath> user.status

if it is on disk you get

ONLINE

if it is only on tape you get

NEARLINE

(This command doesn’t work on SL7 so use an AL9 window)

The df command

To find out what types of volumes are available on a node can be achieved with the command df. The -h is for human readable format. It will list a lot of information about each volume (total size, available size, mount point, device location).

df -h

Exercise 3

From the output of the df -h command, identify:

1. the home area
2. the NAS storage spaces
3. the different dCache volumes

Quiz

Question 01

Which volumes are directly accessible (POSIX) from grid worker nodes?

1. /exp/dune/data
2. DUNE CVMFS repository
3. /pnfs/dune/scratch
4. /pnfs/dune/persistent
5. None of the Above

Answer

The correct answer is B - DUNE CVMFS repository.

Comment here

Question 02

Which data volume is the best location for the output of an analysis-user grid job?

1. dCache scratch (/pnfs/dune/scratch/users/${USER}/)
2. dCache persistent (/pnfs/dune/persistent/users/${USER}/)
3. Enstore tape (/pnfs/dune/tape_backed/users/${USER}/)
4. user’s home area (`~${USER}`)
5. NFS data volume (/dune/data or /dune/app)

Answer

The correct answer is A, dCache scratch (/pnfs/dune/scratch/users/${USER}/).

Comment here

Question 03

You have written a shell script that sets up your environment for both DUNE and another FNAL experiment. Where should you put it?

1. DUNE CVMFS repository
2. /pnfs/dune/scratch/
3. /exp/dune/app/
4. Your GPVM home area
5. Your laptop home area

Answer

The correct answer is D - Your GPVM home area.

Comment here

Question 04

What is the preferred way of reading a file interactively?

1. Read it across the nfs mount on the GPVM
2. Download the whole file to /tmp with xrdcp
3. Open it for streaming via xrootd
4. None of the above
5. None of the Above

Answer

The correct answer is C - Open it for streaming via xrootd. Use pnfs2xrootd to generate the streaming path.

Comment here

Useful links to bookmark

• ifdh commands (redmine)
• Understanding storage volumes (redmine)
• How DUNE storage works: pdf

Key Points

• Home directories are centrally managed by Computing Division and meant to store setup scripts, do NOT store certificates here.

• Network attached storage (NAS) /dune/app is primarily for code development.

• The NAS /dune/data is for store ntuples and small datasets.

• dCache volumes (tape, resilient, scratch, persistent) offer large storage with various retention lifetime.

• The tool suites idfh and XRootD allow for accessing data with appropriate transfer method and in a scalable way.

Data Management (2024 updated for metacat/justin/rucio)

Overview

Teaching: 40 min
Exercises: 0 min

Questions

• What are the data management tools and software for DUNE?

Objectives

• Learn how to access data from DUNE Data Catalog.

• Learn a bit about the JustIN workflow system for submitting batch jobs.

Introduction

DUNE data is stored around the world and the storage elements are not always organized in a way that they can be easily inspected. For this purpose we use the metacat data catalog to describe the data and collections and rucio to determine where replicas of files are. There is also a legacy SAM data access system that can be used for older files.

What is metacat

Metacat is a file catalog - it allows you to search for files that have particular attributes and understand their provenance, including details on all of their processing steps.

You can find extensive documentation on metacat at:

General metacat documentation

DUNE metacat examples

Find a file in metacat

DUNE runs multiple experiments (far detectors, protodune-sp, protodune-dp hd-protodune, vd-protodune, iceberg, coldboxes… ) and produces various kinds of data (mc/detector) and process them through different phases.

To find your data you need to specify at the minimum

• core.run_type (the experiment)
• core.file_type (mc or detecor)
• core.data_tier (the level of processing raw, full-reconstructed, root-tuple)

and when searching for specific types of data

• core.data_stream (physics, calibration, cosmics)
• core.runs[any]=<runnumber>

Here is an example of a metacat query that gets you raw files from a recent ‘hd-protodune’ cosmics run.

First get metacat if you have not already done so

setup metacat # if using SL7
spack load metacat # if using Alma9
metacat auth login -m password $USER  # use your services password to authenticate
export METACAT_AUTH_SERVER_URL=https://metacat.fnal.gov:8143/auth/dune
export METACAT_SERVER_URL=https://metacat.fnal.gov:9443/dune_meta_prod/app 

Note: other means of authentication

Check out the metacat documentation for kx509 and token authentication.

then do queries to find particular sets of files.

metacat query "files from dune:all where core.file_type=detector \
 and core.run_type=hd-protodune and core.data_tier=raw \
 and core.data_stream=cosmics and core.runs[any]=27296 limit 2"

should give you 2 files:

hd-protodune:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
hd-protodune:np04hd_raw_run027296_0000_dataflow0_datawriter_0_20240619T110330.hdf5

the string before the ‘:’ is the namespace and the string after is the filename.

You can find out more about your file by doing:

metacat file show -m -l hd-protodune:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5

which gives you a lot of information:

checksums:
    adler32   : 6a191436
created_timestamp   :	2024-06-19 11:08:24.398197+00:00
creator             :	dunepro
fid                 :	83302138
name                :	np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
namespace           :	hd-protodune
retired             :	False
retired_by          :	None
retired_timestamp   :	None
size                :	4232017188
updated_by          :	None
updated_timestamp   :	1718795304.398197
metadata:
    core.data_stream    : cosmics
    core.data_tier      : raw
    core.end_time       : 1718795024.0
    core.event_count    : 35
    core.events         : [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47, 51, 55, 59, 63, 67, 71, 75, 79, 83, 87, 91, 95, 99, 103, 107, 111, 115, 119, 123, 127, 131, 135, 139]
    core.file_content_status: good
    core.file_format    : hdf5
    core.file_type      : detector
    core.first_event_number: 3
    core.last_event_number: 139
    core.run_type       : hd-protodune
    core.runs           : [27296]
    core.runs_subruns   : [2729600001]
    core.start_time     : 1718795010.0
    dune.daq_test       : False
    retention.class     : physics
    retention.status    : active
children:
   hd-protodune-det-reco:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330_reco_stage1_20240621T175057_keepup_hists.root (eywzUgkZRZ6llTsU)
   hd-protodune-det-reco:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330_reco_stage1_reco_stage2_20240621T175057_keepup.root (GHSm3owITS20vn69)

look in the glossary to see what those fields mean.

find out how much raw data there is in a run using the summary option

metacat query -s "files from dune:all where core.file_type=detector \
 and core.run_type=hd-protodune and core.data_tier=raw \
 and core.data_stream=cosmics and core.runs[any]=27296"

Files:        963
Total size:   4092539942264 (4.093 TB)

To look at all the files in that run you need to use XRootD - DO NOT TRY TO COPY 4 TB to your local area!!!*

What is(was) SAM?

Sequential Access with Metadata (SAM) is/was a data handling system developed at Fermilab. It is designed to tracklocations of files and other file metadata. It has been replaced by metacat.

What is Rucio?

Rucio is the next-generation Data Replica service and is part of DUNE’s new Distributed Data Management (DDM) system that is currently in deployment. Rucio has two functions:

1. A rule-based system to get files to Rucio Storage Elements around the world and keep them there.
2. To return the “nearest” replica of any data file for use either in interactive or batch file use. It is expected that most DUNE users will not be regularly using direct Rucio commands, but other wrapper scripts that calls them indirectly.

As of the date of the June 2024 tutorial:

• The Rucio client is available in CVMFS
• Most DUNE users are now enabled to use it. New users may not automatically be added.

Let’s find a file

If you haven’t already done this earlier in setup

• On sl7 type setup rucio
• On al9 type spack load rucio-clients@33.3.0 # may need to update that version #

# first get a kx509 proxy, then

export RUCIO_ACCOUNT=$USER


rucio list-file-replicas hd-protodune:np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5 --pfns --protocols=root

returns 3 locations:

root://eospublic.cern.ch:1094//eos/experiment/neutplatform/protodune/dune/hd-protodune/e5/57/np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro//hd-protodune/raw/2024/detector/cosmics/None/00/02/72/96/np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5
root://eosctapublic.cern.ch:1094//eos/ctapublic/archive/neutplatform/protodune/rawdata/np04//hd-protodune/raw/2024/detector/cosmics/None/00/02/72/96/np04hd_raw_run027296_0000_dataflow3_datawriter_0_20240619T110330.hdf5

which is the locations of the file on disk and tape. We can use this to copy the file to our local disk or access the file via xroot.

Finding files by characteristics using metacat

To list raw data files for a given run:

metacat query "files where core.file_type=detector \
 and core.run_type='protodune-sp' and core.data_tier=raw \
 and core.data_stream=physics and core.runs[any] in (5141)"

• core.run_type tells you which of the many DAQ’s this came from.
• core.file_type tells detector from mc
• core.data_tier could be raw, full-reconstructed, root-tuple. Same data different formats.

protodune-sp:np04_raw_run005141_0013_dl7.root
protodune-sp:np04_raw_run005141_0005_dl3.root
protodune-sp:np04_raw_run005141_0003_dl1.root
protodune-sp:np04_raw_run005141_0004_dl7.root
...
protodune-sp:np04_raw_run005141_0009_dl7.root
protodune-sp:np04_raw_run005141_0014_dl11.root
protodune-sp:np04_raw_run005141_0007_dl6.root
protodune-sp:np04_raw_run005141_0011_dl8.root

Note the presence of both a namespace and a filename

What about some files from a reconstructed version?

metacat query "files from dune:all where core.file_type=detector \
 and core.run_type='protodune-sp' and core.data_tier=full-reconstructed  \
 and core.data_stream=physics and core.runs[any] in (5141) and dune.campaign=PDSPProd4 limit 10" 

pdsp_det_reco:np04_raw_run005141_0013_dl10_reco1_18127013_0_20210318T104043Z.root
pdsp_det_reco:np04_raw_run005141_0015_dl4_reco1_18126145_0_20210318T101646Z.root
pdsp_det_reco:np04_raw_run005141_0008_dl12_reco1_18127279_0_20210318T104635Z.root
pdsp_det_reco:np04_raw_run005141_0002_dl2_reco1_18126921_0_20210318T103516Z.root
pdsp_det_reco:np04_raw_run005141_0002_dl14_reco1_18126686_0_20210318T102955Z.root
pdsp_det_reco:np04_raw_run005141_0015_dl5_reco1_18126081_0_20210318T122619Z.root
pdsp_det_reco:np04_raw_run005141_0017_dl10_reco1_18126384_0_20210318T102231Z.root
pdsp_det_reco:np04_raw_run005141_0006_dl4_reco1_18127317_0_20210318T104702Z.root
pdsp_det_reco:np04_raw_run005141_0007_dl9_reco1_18126730_0_20210318T102939Z.root
pdsp_det_reco:np04_raw_run005141_0011_dl7_reco1_18127369_0_20210318T104844Z.root

To see the total number of files that match a certain query expression, then add the -s option to metacat query.

See the metacat documentation for more information about queries. DataCatalogDocs and check out the glossary of common fields at: MetaCatGlossary

Accessing data for use in your analysis

To access data without copying it, XRootD is the tool to use. However it will work only if the file is staged to the disk.

You can stream files worldwide if you have a DUNE VO certificate as described in the preparation part of this tutorial.

To learn more about using Rucio and Metacat to run over large data samples go here:

Full Justin/Rucio/Metacat Tutorial

The Justin/Rucio/Metacat Tutorial and justin tutorial

Exercise 1

• Use metacat to find a file from a particular experiment/run/processing stage
• Use metacat file show -m namespace:filename to get metadata for this file. Note that --json gives the output in json format.

When we are analyzing large numbers of files in a group of batch jobs, we use a metacat dataset to describe the full set of files that we are going to analyze and use the JustIn system to run over that dataset. Each job will then come up and ask metacat and rucio to give it the next file in the list. It will try to find the nearest copy. For instance if you are running at CERN and analyzing this file it will automatically take it from the CERN storage space EOS.

Exercise 2

FIXME Need to make an example of looking at a dataset

Resources:

• DataCatalogDocs
• The Justin/Rucio/Metacat Tutorial
• justin tutorial

Quiz

Question 01

What is file metadata?

1. Information about how and when a file was made
2. Information about what type of data the file contains
3. Conditions such as liquid argon temperature while the file was being written
4. Both A and B
5. All of the above

Answer

The correct answer is D - Both A and B.

Comment here

Question 02

How do we determine a DUNE data file location?

1. Do `ls -R` on /pnfs/dune and grep
2. Use `rucio list-file-replicas` (namespace:filename) --pnfs --protocols=root
3. Ask the data management group
4. None of the Above

Answer

The correct answer is B - use rucio list-file-replicas (namespace:filename).

Comment here

Useful links to bookmark

• Metacat: https://dune.github.io/DataCatalogDocs
• Pre-2024 Official dataset definitions: dune-data.fnal.gov
• UPS reference manual
• UPS documentation (redmine)
• UPS qualifiers: About Qualifiers (redmine)
• mrb reference guide (redmine)
• CVMFS on DUNE wiki: Access files in CVMFS

Key Points

• SAM and Rucio are data handling systems used by the DUNE collaboration to retrieve data.

• Staging is a necessary step to make sure files are on disk in dCache (as opposed to only on tape).

• Xrootd allows user to stream data files.

The old UPS code management system

Overview

Teaching: 30 min
Exercises: 0 min

Questions

• How are different software versions handled?

Objectives

• Understand the roles of the tools UPS (and Spack)

What is UPS and why do we need it?

Note

UPS is going away and only works on SL7 but we do not yet have a fully functional replacement. You need to be in the Apptainer to use it. UPS is being replaced by a new [spack][Spack Documentation] system for Alma9. We will be adding a Spack tutorial soon but for now, you need to use SL7/UPS to use the full DUNE code stack.

Go back and look at the SL7/Apptainer instructions to get an SL7 container for this section.

An important requirement for making valid physics results is computational reproducibility. You need to be able to repeat the same calculations on the data and MC and get the same answers every time. You may be asked to produce a slightly different version of a plot for example, and the data that goes into it has to be the same every time you run the program.

This requirement is in tension with a rapidly-developing software environment, where many collaborators are constantly improving software and adding new features. We therefore require strict version control; the workflows must be stable and not constantly changing due to updates.

DUNE must provide installed binaries and associated files for every version of the software that anyone could be using. Users must then specify which version they want to run before they run it. All software dependencies must be set up with consistent versions in order for the whole stack to run and run reproducibly.

The Unix Product Setup (UPS) is a tool to handle the software product setup operation.

UPS is set up when you setup DUNE:

Launch the Apptainer and then:

 export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
 source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
 export DUNELAR_VERSION=v09_90_01d00
 export DUNELAR_QUALIFIER=e26:prof
 setup dunesw $DUNELAR_VERSION -q $DUNELAR_QUALIFIER

dunesw: product name
$DUNELAR_VERSION version tag
$DUNELAR_QUALIFIER are “qualifiers”. Qualifiers are separated with colons and may be specified in any order. The e26 qualifier refers to a specific version of the gcc compiler suite, and prof means select the installed product that has been compiled with optimizations turned on. An alternative to prof is the debug qualifier. All builds of LArSoft and dunesw are compiled with debug symbols turned on, but the “debug” builds are made with optimizations turned off. Both kinds of software can be debugged, but it is easier to debug the debug builds (code executes in the proper order and variables aren’t optimized away so they can be inspected).

Another specifier of a product install is the “flavor”. This refers to the operating system the program was compiled for. These days we only support SL7, but in the past we used to also support SL6 and various versions of macOS. The flavor is automatically selected when you set up a product using setup (unless you override it which is usually a bad idea). Some product are “unflavored” because they do not contain anything that depends on the operating system. Examples are products that only contain data files or text files.

Setting up a UPS product defines many environment variables. Most products have an environment variable of the form <productname>_DIR, where <productname> is the name of the UPS product in all capital letters. This is the top-level directory and can be used when searching for installed source code or fcl files for example. <productname>_FQ_DIR is the one that specifies a particular qualifier and flavor.

Exercise 3

• show all the versions of dunesw that are currently available by using the ups list -aK+ dunesw command
• pick one version and substitute that for DUNELAR_VERSION and DUNELAR_QUALIFIER above and set up dunesw

Many products modify the following search path variables, prepending their pieces when set up. These search paths are needed by art jobs.

PATH: colon-separated list of directories the shell uses when searching for programs to execute when you type their names at the command line. The command which tells you which version of a program is found first in the PATH search list. Example:

which lar

will tell you where the lar command you would execute is if you were to type lar at the command prompt. The other paths are needed by art for finding plug-in libraries, fcl files, and other components, like gdml files.
CET_PLUGIN_PATH
LD_LIBRARY_PATH
FHICL_FILE_PATH
FW_SEARCH_PATH

Also the PYTHONPATH describes where Python modules will be loaded from.

Try

which root

to see the version of root that dunesw sets up. Try it out!

UPS basic commands

Exercise 4

• show all the dependencies of dunesw by using “ups depend dunesw $DUNELAR_VERSION -q $DUNELAR_QUALIFIER”

UPS Documentation Links

• UPS reference manual
• UPS documentation
• UPS qualifiers

Key Points

• The Unix Product Setup (UPS) is a tool to ensure consistency between different software versions and reproducibility.

• CVMFS distributes software and related files without installing them on the target computer (using a VM, Virtual Machine).

CVMFS distributed file system

Overview

Teaching: 10 min
Exercises: 0 min

Questions

• What is cvmfs

Objectives

• Understand the roles of the CVMFS.

CVMFS

What is CVMFS and why do we need it?
DUNE has a need to distribute precompiled code to many different computers that collaborators may use. Installed products are needed for four things:

1. Running programs interactively
2. Running programs on grid nodes
3. Linking programs to installed libraries
4. Inspection of source code and data files

Results must be reproducible, so identical code and associated files must be distributed everywhere. DUNE does not own any batch resources – we use CPU time on computers that participating institutions donate to the Open Science Grid. We are not allowed to install our software on these computers and must return them to their original state when our programs finish running so they are ready for the next job from another collaboration.

CVMFS is a perfect tool for distributing software and related files. It stands for CernVM File System (VM is Virtual Machine). Local caches are provided on each target computer, and files are accessed via the /cvmfs mount point. DUNE software is in the directory /cvmfs/dune.opensciencegrid.org, and LArSoft code is in /cvmfs/larsoft.opensciencegrid.org. These directories are auto-mounted and need to be visible when one executes ls /cvmfs for the first time. Some software is also in /cvmfs/fermilab.opensciencegrid.org.

CVMFS also provides a de-duplication feature. If a given file is the same in all 100 releases of dunetpc, it is only cached and transmitted once, not independently for every release. So it considerably decreases the size of code that has to be transferred.

When a file is accessed in /cvmfs, a daemon on the target computer wakes up and determines if the file is in the local cache, and delivers it if it is. If not, the daemon contacts the CVMFS repository server responsible for the directory, and fetches the file into local cache. In this sense, it works a lot like AFS. But it is a read-only filesystem on the target computers, and files must be published on special CVMFS publishing servers. Files may also be cached in a layer between the CVMFS host and the target node in a squid server, which helps facilities with many batch workers reduce the network load in fetching many copies of the same file, possibly over an international connection.

CVMFS also has a feature known as “Stashcache” or “xCache”. Files that are in /cvmfs/dune.osgstorage.org are not actually transmitted in their entirety, only pointers to them are, and then they are fetched from one of several regional cache servers or in the case of DUNE from Fermilab dCache directly. DUNE uses this to distribute photon library files, for instance.

CVMFS is by its nature read-all so code is readable by anyone in the world with a CVMFS client. CVMFS clients are available for download to desktops or laptops. Sensitive code can not be stored in CVMFS.

More information on CVMFS is available here

Exercise 6

• cd /cvmfs and do an ls at top level
• What do you see–do you see the four subdirectories (dune.opensciencegrid.org, larsoft.opensciencegrid.org, fermilab.opensciencegrid.org, and dune.osgstorage.org)
• cd dune.osgstorage.org/pnfs/fnal.gov/usr/dune/persistent/stash/PhotonPropagation/LibraryData

Useful links to bookmark

• CVMFS on DUNE wiki: Access files in CVMFS

Key Points

• CVMFS distributes software and related files without installing them on the target computer (using a VM, Virtual Machine).

Introduction to art and LArSoft (2024 - Apptainer version)

Overview

Teaching: 95 min
Exercises: 0 min

Questions

• Why do we need a complicated software framework? Can’t I just write standalone code?

Objectives

• Learn what services the art framework provides.

• Learn how the LArSoft tookit is organized and how to use it.

Introduction to art

Art is the framework used for the offline software used to process LArTPC data from the far detector and the ProtoDUNEs. It was chosen not only because of the features it provides, but also because it allows DUNE to use and share algorithms developed for other LArTPC experiments, such as ArgoNeuT, LArIAT, MicroBooNE and ICARUS. The section below describes LArSoft, a shared software toolkit. Art is also used by the NOvA and mu2e experiments. The primary language for art and experiment-specific plug-ins is C++.

The art wiki page is here: https://cdcvs.fnal.gov/redmine/projects/art/wiki. It contains important information on command-line utilities, how to configure an art job, how to define, read in and write out data products, how and when to use art modules, services, and tools.

Art features:

1. Defines the event loop
2. Manages event data storage memory and prevents unintended overwrites
3. Input file interface – allows ganging together input files
4. Schedules module execution
5. Defines a standard way to store data products in art-formatted ROOT files
6. Defines a format for associations between data products (for example, tracks have hits, and associations between tracks and hits can be made via art’s association mechanism.
7. Provides a uniform job configuration interface
8. Stores job configuration information in art-formatted root files.
9. Output file control – lets you define output filenames based on parts of the input filename.
10. Message handling
11. Random number control
12. Exception handling

The configuration storage is particularly useful if you receive a data file from a colleague, or find one in a data repository and you want to know more about how it was produced, with what settings.

Getting set up to try the tools

Log in to a dunegpvm*.fnal.gov machine and set up your environment (This script is defined in Exercise 5 of https://dune.github.io/computing-training-basics/setup.html)

Note

For now do this in the Apptainer. Due to the need to set up the container separately on the build nodes and the gpvms due to /pnfs mounts being different, and the need to keep your environment clean for use on other experiments, it is best to define aliases in your .profile or .bashrc or other login script you use to define aliases. A set of convenient aliases is

alias dunesl7="/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash -B /cvmfs,/exp,/nashome,/pnfs/dune,/opt,/run/user,/etc/hostname,/etc/hosts,/etc/krb5.conf --ipc --pid /cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest"

alias dunesl7build="/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash -B /cvmfs,/exp,/build,/nashome,/opt,/run/user,/etc/hostname,/etc/hosts,/etc/krb5.conf --ipc --pid /cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest"

alias dunesetups="source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh"

Then you can use the appropriate alias to start the SL7 container on either the build node or the gpvms. Starting a container gives you a very bare environment – it does not source your .profile for you; you have to do that yourself. The examples below assume you put the aliases above in your .profile or in a script sourced by your .profile. I always set the prompt variable PS1 in my profile so I can tell that I’ve sourced it.

PS1="<`hostname`> "; export PS1

Then when you log in, you can type these commands to set up your environment in a container:

dunesl7
source .profile
dunesetups

export DUNELAR_VERSION=v10_00_04d00
export DUNELAR_QUALIFIER=e26:prof
setup dunesw $DUNELAR_VERSION -q $DUNELAR_QUALIFIER

setup_fnal_security

# define a sample file
export SAMPLE_FILE=root://fndcadoor.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/physics/full-reconstructed/2023/mc/out1/MC_Winter2023_RITM1592444_reReco/54/05/35/65/NNBarAtm_hA_BR_dune10kt_1x2x6_54053565_607_20220331T192335Z_gen_g4_detsim_reco_65751406_0_20230125T150414Z_reReco.root

The examples below will refer to files in dCache at Fermilab which can best be accessed via xrootd.

For those with no access to Fermilab computing resources but with a CERN account:
Copies are stored in /afs/cern.ch/work/t/tjunk/public/jan2023tutorialfiles/.

The follow-up of this tutorial provides help on how to find data and MC files in storage.

You can list available versions of dunesw installed in CVMFS with this command:

ups list -aK+ dunesw

The output is not sorted, although portions of it may look sorted. Do not depend on it being sorted. The string indicating the version is called the version tag (v09_72_01d00 here). The qualifiers are e26 and prof. Qualifiers can be entered in any order and are separated by colons. “e26” corresponds to a specific version of the GNU compiler – v9.3.0. We also compile with clang – the compiler qualifier for that is “c7”.

“prof” means “compiled with optimizations turned on.” “debug” means “compiled with optimizations turned off”. More information on qualifiers is here.

In addition to the version and qualifiers, UPS products have “flavors”. This refers to the operating system type and version. Older versions of DUNE software supported SL6 and some versions of macOS. Currently only SL7 and the compatible CentOS 7 are supported. The flavor of a product is automatically selected to match your current operating system when you set up a product. If a product does not have a compatible flavor, you will get an error message. “Unflavored” products are ones that do not depend on the operating-system libraries. They are listed with a flavor of “NULL”.

There is a setup command provided by the operating system – you usually don’t want to use it (at least not when developing DUNE software). If you haven’t yet sourced the setup_dune.sh script in CVMFS above but type setup xyz anyway, you will get the system setup command, which will ask you for the root password. Just control-C out of it, source the setup_dune.sh script, and try again.

UPS’s setup command (find out where it lives with this command):

type setup

will not only set up the product you specify (in the instructions above, dunesw), but also all dependent products with corresponding versions so that you get a consistent software environment. You can get a list of everything that’s set up with this command

 ups active

It is often useful to pipe the output through grep to find a particular product.

 ups active | grep geant4

for example, to see what version of geant4 you have set up.

Art command-line tools

All of these command-line tools have online help. Invoke the help feature with the --help command-line option. Example:

config_dumper --help

Docmentation on art command-line tools is available on the art wiki page.

config_dumper

Configuration information for a file can be printed with config_dumper.

config_dumper -P <artrootfile>

Try it out:

config_dumper -P $SAMPLE_FILE

The output is an executable fcl file, sent to stdout. We recommend redirecting the output to a file that you can look at in a text editor:

Try it out:

config_dumper -P $SAMPLE_FILE > tmp.fcl

Your shell may be configured with noclobber, meaning that if you already have a file called tmp.fcl, the shell will refuse to overwrite it. Just rm tmp.fcl and try again.

The -P option to config_dumper is needed to tell config_dumper to print out all processing configuration fcl parameters. The default behavior of config_dumper prints out only a subset of the configuration parameters, and is most notably missing art services configuration.

Quiz

Quiz questions from the output of the above run of config_dumper:

1. What generators were used? What physics processes are simulated in this file?
2. What geometry is used? (hint: look for “GDML” or “gdml”)
3. What electron lifetime was assumed?
4. What is the readout window size?

fhicl-dump

You can parse a FCL file with fhicl-dump.

Try it out:

fhicl-dump protoDUNE_refactored_g4_stage2.fcl

See the section below on FCL files for more information on what you’re looking at.

count_events

Try it out:

count_events $SAMPLE_FILE

product_sizes_dumper

You can get a peek at what’s inside an artROOT file with product_sizes_dumper.

Try it out:

product_sizes_dumper -f 0 $SAMPLE_FILE

It is also useful to redirect the output of this command to a file so you can look at it with a text editor and search for items of interest. This command lists the sizes of the TBranches in the Events TTree in the artROOT file. There is one TBranch per data product, and the name of the TBranch is the data product name, an “s” is appended (even if the plural of the data product name doesn’t make sense with just an “s” on the end), an underscore, then the module label that made the data product, an underscore, the instance name, an underscore, and the process name and a period.

Quiz questions, looking at the output from above.

Quiz

Questions:

1. What is the name of the data product that takes up the most space in the file?
2. What the module label for this data product?
3. What is the module instance name for this data product? (This question is tricky. You have to count underscores here).
4. How many different modules produced simb::MCTruth data products? What are their module labels?
5. How many different modules produced recob::Hit data products? What are their module labels?

You can open up an artROOT file with ROOT and browse the TTrees in it with a TBrowser. Not all TBranches and leaves can be inspected easily this way, but enough can that it can save a lot of time programming if you just want to know something simple about a file such as whether it contains a particular data product and how many there are.

Try it out

root $SAMPLE_FILE

then at the root prompt, type:

new TBrowser

This will be faster with VNC. Navigate to the Events TTree in the file that is automatically opened, navigate to the TBranch with the Argon 39 MCTruths (it’s near the bottom), click on the branch icon simb::MCTruths_ar39__SinglesGen.obj, and click on the NParticles() leaf (It’s near the bottom. Yes, it has a red exclamation point on it, but go ahead and click on it). How many events are there? How many 39Ar decays are there per event on average?

Art is not constrained to using ROOT files – some effort has already been underway to use HDF5-formatted files for some purposes.

The art main executable program is a very short stub that interprets command-line options, reads in the configuration document (a FHiCL file which usually includes other FHiCL files), and loads shared libraries, initializes software components, and schedules execution of modules. Most code we are interested in is in the form of art plug-ins – modules, services, and tools. The generic executable for invoking art is called art, but a LArSoft-customized one is called lar. No additional customization has yet been applied so in fact, the lar executable has identical functionality to the art executable.

There is online help:

 lar --help

All programs in the art suite have a --help command-line option.

Most art job invocations take the form

lar -n <nevents> -c fclfile.fcl artrootfile.root

where the input file specification is just on the command line without a command-line option. Explicit examples follow below. The -n <nevents> is optional – it specifies the number of events to process. If omitted, or if <nevents> is bigger than the number of events in the input file, the job processes all of the events in the input file. -n <nevents> is important for the generator stage. There’s also a handy --nskip <nevents_to_skip> argument if you’d like the job to start processing partway through the input file. You can steer the output with

lar -c fclfile.fcl artrootfile.root -o outputartrootfile.root -T outputhistofile.root

The outputhistofile.root file contains ROOT objects that have been declared with the TFileService service in user-supplied art plug-in code (i.e. your code).

Job configuration with FHiCL

The Fermilab Hierarchical Configuration Language, FHiCL is described here https://cdcvs.fnal.gov/redmine/documents/327.

FHiCL is not a Turing-complete language: you cannot write an executable program in it. It is meant to declare values for named parameters to steer job execution and adjust algorithm parameters (such as the electron lifetime in the simulation and reconstruction). Look at .fcl files in installed job directories, like $DUNESW_DIR/fcl for examples. Fcl files are sought in the directory seach path FHICL_FILE_PATH when art starts up and when #include statements are processed. A fully-expanded fcl file with all the #include statements executed is referred to as a fhicl “document”.

Parameters may be defined more than once. The last instance of a parameter definition wins out over previous ones. This makes for a common idiom in changing one or two parameters in a fhicl document. The generic pattern for making a short fcl file that modifies a parameter is:

#include "fcl_file_that_does_almost_what_I_want.fcl"
block.subblock.parameter: new_value

To see what block and subblock a parameter is in, use fhcl-dump on the parent fcl file and look for the curly brackets. You can also use

lar -c fclfile.fcl --debug-config tmp.txt --annotate

which is equivalent to fhicl-dump with the –annotate option and piping the output to tmp.txt.

Entire blocks of parameters can be substituted in using @local and @table idioms. See the examples and documentation for guidance on how to use these. Generally they are defined in the PROLOG sections of fcl files. PROLOGs must precede all non-PROLOG definitions and if their symbols are not subsequently used they do not get put in the final job configuration document (that gets stored with the data and thus may bloat it). This is useful if there are many alternate configurations for some module and only one is chosen at a time.

Try it out:

fhicl-dump protoDUNE_refactored_g4_stage2.fcl > tmp.txt

Look for the parameter ModBoxA. It is one of the Modified Box Model ionization parameters. See what block it is in. Here are the contents of a modified g4 stage 2 fcl file that modifies just that parameter:

#include "protoDUNE_refactored_g4_stage2.fcl"
services.LArG4Parameters.ModBoxA: 7.7E-1

Exercise

Do a similar thing – modify the stage 2 g4 fcl configuration to change the drift field from 486.7 V/cm to 500 V/cm. Hint – you will find the drift field in an array of fields which also has the fields between wire planes listed.

Types of Plug-Ins

Plug-ins each have their own .so library which gets dynamically loaded by art when referenced by name in the fcl configuration.

Producer Modules
A producer module is a software component that writes data products to the event memory. It is characterized by produces<> and consumes<> statements in the class constructor, and art::Event::put() calls in the produces() method. A producer must produce the data product collection it says it produces, even if it is empty, or art will throw an exception at runtime. art::Event::put() transfers ownership of memory (use std::move so as not to copy the data) from the module to the art event memory. Data in the art event memory will be written to the output file unless output commands in the fcl file tell art not to do that. Documentation on output commands can be found in the LArSoft wiki here. Producer modules have methods that are called on begin job, begin run, begin subrun, and on each event, as well as at the end of processing, so you can initialize counters or histograms, and finish up summaries at the end. Source code must be in files of the form: modulename_module.cc, where modulename does not have any underscores in it.

Analyzer Modules
Analyzer modules read data products from the event memory and produce histograms or TTrees, or other output. They are typically scheduled after the producer modules have been run. Producer modules have methods that are called on begin job, begin run, begin subrun, and on each event, as well as at the end of processing, so you can initialize counters or histograms, and finish up summaries at the end. Source code must be in files of the form: modulename_module.cc, where modulename does not have any underscores in it.

Source Modules
Source modules read data from input files and reformat it as need be, in order to put the data in art event data store. Most jobs use the art-provided RootInput source module which reads in art-formatted ROOT files. RootInput interacts well with the rest of the framework in that it provides lazy reading of TTree branches. When using the RootInput source, data are not actually fetched from the file into memory when the source executes, but only when GetHandle or GetValidHandle or other product get methods are called. This is useful for art jobs that only read a subset of the TBranches in an input file. Code for sources must be in files of the form: modulename_source.cc, where modulename does not have any underscores in it. Monte Carlo generator jobs use the input source called EmptyEvent.

Services
These are singleton classes that are globally visible within an art job. They can be FHiCL configured like modules, and they can schedule methods to be called on begin job, begin run, begin event, etc. They are meant to help supply configuration parameters like the drift velocity, or more complicated things like geometry functions, to modules that need them. Please do not use services as a back door for storing event data outside of the art event store. Source code must be in files of the form: servicename_service.cc, where servicename does not have any underscores in it.

Tools
Tools are FHiCL-configurable software components that are not singletons, like services. They are meant to be swappable by FHiCL parameters which tell art which .so libraries to load up, configure, and call from user code. See the Art Wiki Page for more information on tools and other plug-ins.

You can use cetskelgen to make empty skeletons of art plug-ins. See the art wiki for documentation, or use

cetskelgen --help

for instructions on how to invoke it.

Ordering of Plug-in Execution

The constructors for each plug-in are called at job-start time, after the shared object libraries are loaded by the image activater after their names have been discovered from the fcl configuration. Producer, analyzer and service plug-ins have BeginJob, BeginRun, BeginSubRun, EndSubRun, EndRun, EndJob methods where they can do things like book histograms, write out summary information, or clean up memory.

When processing data, the input source always gets executed first, and it defines the run, subrun and event number of the trigger record being processed. The producers and filters in trigger_paths then get executed for each event. The analyzers and filters in end_paths then get executed. Analyzers cannot be added to trigger_paths, and producers cannot be added to end_paths. This ordering ensures that data products are all produced by the time they are needed to be analyzed. But it also forces high memory usage for the same reason.

Services and tools are visible to other plug-ins at any stage of processing. They are loaded dynamically from names in the fcl configurations, so a common error is to use in code a service that hasn’t been mentioned in the job configuration. You will get an error asking you to configure the service, even if it is just an empty configuration with the service name and no parameters set.

Non-Plug-In Code

You are welcome to write standard C++ code – classes and C-style functions are no problem. In fact, to enhance the portability of code, the art team encourages the separation of algorithm code into non-framework-specific source files, and to call these functions or class methods from the art plug-ins. Typically, source files for standalone algorithm code have the extension .cxx while art plug-ins have .cc extensions. Most directories have a CMakeLists.txt file which has instructions for building the plug-ins, each of which is built into a .so library, and all other code gets built and put in a separate .so library.

Retrieving Data Products

In a producer or analyzer module, data products can be retrieved from the art event store with getHandle() or getValidHandle() calls, or more rarely getManyByType or other calls. The arguments to these calls specify the module label and the instance of the data product. A typical TBranch name in the Events tree in an artROOT file is

simb::MCParticles_largeant__G4Stage1.

here, simb::MCParticle is the name of the class that defines the data product. The “s” after the data product name is added by art – you have no choice in this even if the plural of your noun ought not to just add an “s”. The underscore separates the data product name from the module name, “largeant”. Another underscore separates the module name and the instance name, which in this example is the empty string – there are two underscores together there. The last string is the process name and usually is not needed to be specified in data product retrieval. You can find the TBranch names by browsing an artroot file with ROOT and using a TBrowser, or by using product_sizes_dumper -f 0.

Art documentation

There is a mailing list – art-users@fnal.gov where users can ask questions and get help.

There is a workbook for art available at https://art.fnal.gov/art-workbook/ Look for the “versions” link in the menu on the left for the actual document. It is a few years old and is missing some pieces like how to write a producer module, but it does answer some questions. I recommend keeping a copy of it on your computer and using it to search for answers.

There was an art/LArSoft course in 2015. While it, too is a few years old, the examples are quite good and it serves as a useful reference.

Gallery

Gallery is a lightweight tool that lets users read art-formatted root files and make plots without having to write and build art modules. It works well with interpreted and compiled ROOT macros, and is thus ideally suited for data exploration and fast turnaround of making plots. It lacks the ability to use art services, however, though some LArSoft services have been split into services and service providers. The service provider code is intended to be able to run outside of the art framework and linked into separate programs.

Gallery also lacks the ability to write data products to an output file. You are of course free to open and write files of your own devising in your gallery programs. There are example gallery ROOT scripts in duneexamples/duneexamples/GalleryScripts. They are only in the git repository but do not get installed in the UPS product.

More documentation: https://art.fnal.gov/gallery/

LArSoft

Introductory Documentation

LArSoft’s home page: larsoft.org

The LArSoft wiki is here: larsoft-wiki.

Software structure

The LArSoft toolkit is a set of software components that simulate and reconstruct LArTPC data, and also it provides tools for accessing raw data from the experiments. LArSoft contains an interface to GEANT4 (art does not list GEANT4 as a dependency) and the GENIE generator. It contains geometry tools that are adapted for wire-based LArTPC detectors.

A recent graph of the UPS products in a full stack starting with dunesw is available here (dunesw). You can see the LArSoft pieces under dunesw, as well as GEANT4, GENIE, ROOT, and a few others.

LArSoft Data Products

A very good introduction to data products such as raw digits, calibrated waveforms, hits and tracks, that are created and used by LArSoft modules and usable by analyzers was given by Tingjun Yang at the 2019 ProtoDUNE analysis workshop (larsoft-data-products).

There are a number of data product dumper fcl files. A non-exhaustive list of useful examples is given below:

 dump_mctruth.fcl
 dump_mcparticles.fcl
 dump_simenergydeposits.fcl
 dump_simchannels.fcl
 dump_simphotons.fcl
 dump_rawdigits.fcl
 dump_wires.fcl
 dump_hits.fcl
 dump_clusters.fcl
 dump_tracks.fcl
 dump_pfparticles.fcl
 eventdump.fcl
 dump_lartpcdetector_channelmap.fcl
 dump_lartpcdetector_geometry.fcl

Some of these may require some configuration of input module labels so they can find the data products of interest.

Some of these may require some configuration of input module labels so they can find the data products of interest. Try one of these yourself:

lar -n 1 -c dump_mctruth.fcl $SAMPLE_FILE

This command will make a file called DumpMCTruth.log which you can open in a text editor. Reminder: MCTruth are particles made by the generator(s), and MCParticles are those made by GEANT4, except for those owned by the MCTruth data products. Due to the showering nature of LArTPCs, there are usually many more MCParticles than MCTruths.

Examples and current workflows

The page with instructions on how to find and look at ProtoDUNE data has links to standard fcl configurations for simulating and reconstructing ProtoDUNE data: https://wiki.dunescience.org/wiki/Look_at_ProtoDUNE_SP_data.

Try it yourself! The workflow for ProtoDUNE-SP MC is given in the Simulation Task Force web page.

Running on a dunegpvm machine at Fermilab

 export USER=`whoami`
 mkdir -p /exp/dune/data/users/$USER/tutorialtest
 cd /exp/dune/data/users/$USER/tutorialtest
 source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh

 export DUNELAR_VERSION=v10_00_04d00
 export DUNELAR_QUALIFIER=e26:prof
 setup dunesw $DUNELAR_VERSION -q $DUNELAR_QUALIFIER

 TMPDIR=/tmp lar -n 1 -c mcc12_gen_protoDune_beam_cosmics_p1GeV.fcl -o gen.root
 lar -n 1 -c protoDUNE_refactored_g4_stage1.fcl gen.root -o g4_stage1.root
 lar -n 1 -c protoDUNE_refactored_g4_stage2_sce_datadriven.fcl g4_stage1.root -o g4_stage2.root
 lar -n 1 -c protoDUNE_refactored_detsim_stage1.fcl g4_stage2.root -o detsim_stage1.root
 lar -n 1 -c protoDUNE_refactored_detsim_stage2.fcl detsim_stage1.root -o detsim_stage2.root
 lar -n 1 -c protoDUNE_refactored_reco_35ms_sce_datadriven_stage1.fcl detsim_stage2.root -o reco_stage1.root
 lar -c eventdump.fcl reco_stage1.root >& eventdump_output.txt
 config_dumper -P reco_stage1.root >& config_output.txt
 product_sizes_dumper -f 0 reco_stage1.root >& productsizes.txt

Note added November 22, 2023: The construct “TMPDIR=/tmp lar …” defines the environment variable TMPDIR only for the duration of the subsequent command on the line. This is needed for the tutorial example because the mcc12 gen stage copies a 2.9 GB file (see below – it’s the one we had to copy over to CERN) to /var/tmp using ifdh’s default temporary location. But the dunegpvm machines as of November 2023 seem to rarely have 2.9 GB of space in /var/tmp and you get a “no space left on device” error. The newer prod4 versions of the fcls point to a newer version of the beam particle generator that can stream this file using XRootD instead of copying it with ifdh. But the streaming flag is turned off by default in the prod4 fcl for the version of dunesw used in this tutorial, and so this is the minimal solution. Note for the next iteration: the Prod4 fcls are here: https://wiki.dunescience.org/wiki/ProtoDUNE-SP_Production_IV

Running at CERN

This example puts all files in a subdirectory of your home directory. There is an input file for the ProtoDUNE-SP beamline simulation that is copied over and you need to point the generation job at it. The above sequence of commands will work at CERN if you have a Fermilab grid proxy, but not everyone signed up for the tutorial can get one of these yet, so we copied the necessary file over and adjusted a fcl file to point at it. It also runs faster with the local copy of the input file than the above workflow which copies it.

The apptainer command is slightly different as the mounts are different. Here we assume you are logged into an lxplus node running Alma9.

Note

CERN Apptainer variant

/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --she
ll=/bin/bash \
-B /cvmfs,/afs,/opt,/run/user,/etc/hostname,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest

Make a fcl file:

#include "mcc12_gen_protoDune_beam_cosmics_p1GeV.fcl"
physics.producers.generator.FileName: "/afs/cern.ch/work/t/tjunk/public/may2023tutorialfiles/H4_v34b_1GeV_-27.7_10M_1.root"

 cd ~
 mkdir 2024Tutorial
 cd 2024Tutorial
 export UPS_OVERRIDE="-H Linux64bit+3.10-2.17"
 source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh

 export DUNELAR_VERSION=v10_00_04
 export LARSOFT_VERSION=${DUNELAR_VERSION}
 export DUNELAR_QUALIFIER=e26:prof
 setup dunesw $DUNELAR_VERSION -q $DUNELAR_QUALIFIER

 #cat > tmpgen.fcl << EOF
 ##include "mcc12_gen_protoDune_beam_cosmics_p1GeV.fcl"
 #physics.producers.generator.FileName: "/afs/cern.ch/work/t/tjunk/public/may2023tutorialfiles/H4_v34b_1GeV_-27.7_10M_1.root"
 #EOF
 lar -n 1 -c tmpgen.fcl -o gen.root
 lar -n 1 -c protoDUNE_refactored_g4_stage1.fcl gen.root -o g4_stage1.root
 lar -n 1 -c protoDUNE_refactored_g4_stage2_sce_datadriven.fcl g4_stage1.root -o g4_stage2.root
 lar -n 1 -c protoDUNE_refactored_detsim_stage1.fcl g4_stage2.root -o detsim_stage1.root
 lar -n 1 -c protoDUNE_refactored_detsim_stage2.fcl detsim_stage1.root -o detsim_stage2.root
 lar -n 1 -c protoDUNE_refactored_reco_35ms_sce_datadriven_stage1.fcl detsim_stage2.root -o reco_stage1.root
 lar -c eventdump.fcl reco_stage1.root >& eventdump_output.txt
 config_dumper -P reco_stage1.root >& config_output.txt
 product_sizes_dumper -f 0 reco_stage1.root >& productsizes.txt

You can also browse the root files with a TBrowser or run other dumper fcl files on them. The dump example commands above redirect their outputs to text files which you can edit with a text editor or run grep on to look for things.

You can run the event display with

lar -c evd_protoDUNE.fcl reco_stage1.root

but it will run very slowly over a tunneled X connection. A VNC session will be much faster. Tips: select the “Reconstructed” radio button at the bottom and click on “Unzoom Interest” on the left to see the reconstructed objects in the three views.

DUNE software documentation and how-to’s

The following legacy wiki page provides information on how to check out, build, and contribute to dune-specific larsoft plug-in code.

https://cdcvs.fnal.gov/redmine/projects/dunetpc/wiki

The follow-up part of this tutorial gives hands-on exercises for doing these things.

Contributing to LArSoft

The LArSoft git repositories are hosted on GitHub and use a pull-request model. LArSoft’s github link is https://github.com/larsoft. DUNE repositories, such as the dunesw stack, protoduneana and garsoft are also on GitHub but at the moment (not for long however), allow users to push code.

To work with pull requests, see the documentation at this link: https://larsoft.github.io/LArSoftWiki/Developing_With_LArSoft

There are bi-weekly LArSoft coordination meetings https://indico.fnal.gov/category/405/ at which stakeholders, managers, and users discuss upcoming releases, plans, and new features to be added to LArSoft.

##w Useful tip: check out an inspection copy of larsoft

A good old-fashioned grep -r or a find command can be effective if you are looking for an example of how to call something but I do not know where such an example might live. The copies of LArSoft source in CVMFS lack the CMakeLists.txt files and if that’s what you’re looking for to find examples, it’s good to have a copy checked out. Here’s a script that checks out all the LArSoft source and DUNE LArSoft code but does not compile it. Warning: it deletes a directory called “inspect” in your app area. Make sure /exp/dune/app/users/<yourusername> exists first:

Note

Remember the Apptainer! You can use your dunesl7 alias defined at the top of this page.

 #!/bin/bash
 USERNAME=`whoami`
 source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
 cd /exp/dune/app/users/${USERNAME}
 rm -rf inspect
 mkdir inspect
 cd inspect
 mrb newDev
 source /exp/dune/app/users/${USERNAME}/inspect/localProducts*/setup
 cd srcs
 mrb g larsoft_suite
 mrb g larsoftobj_suite
 mrb g larutils
 mrb g larbatch
 mrb g dune_suite
 mrb g -d dune_raw_data dune-raw-data

Putting it to use: A very common workflow in developing software is to look for an example of how to do something similar to what you want to do. Let’s say you want to find some examples of how to use FindManyP – it’s an art method for retrieving associations between data products, and the art documentation isn’t as good as the examples for learning how to use it. You can use a recursive grep through your checked-out version, or you can even look through the installed source in CVMFS. This example looks through the duneprototype product’s source files for FindManyP:

 cd $DUNEPROTOTYPES_DIR/source/duneprototypes
 grep -r -i findmanyp *

It is good to use the -i option to grep which tells it to ignore the difference between uppercase and lowercase string matches, in case you misremembered the case of what you are looking for. The list of matches is quite long – you may want to pipe the output of that grep into another grep

 grep -r -i findmanyp * | grep recob::Hit

The checked-out versions of the software have the advantage of providing some files that don’t get installed in CVMFS, notably CMakeLists.txt files and the UPS product_deps files, which you may want to examine when looking for examples of how to do things.

GArSoft

GArSoft is another art-based software package, designed to simulate the ND-GAr near detector. Many components were copied from LArSoft and modified for the pixel-based TPC with an ECAL. You can find installed versions in CVMFS with the following command:

ups list -aK+ garsoft

and you can check out the source and build it by following the instructions on the GArSoft wiki.

Quiz

Question 01

Enter Question here

1. .
2. .
3. .
4. .
5. None of the Above

Answer

The correct answer is .

Comment here

Key Points

• Art provides the tools physicists in a large collaboration need in order to contribute software to a large, shared effort without getting in each others’ way.

• Art helps us keep track of our data and job configuration, reducing the chances of producing mystery data that no one knows where it came from.

• LArSoft is a set of simulation and reconstruction tools shared among the liquid-argon TPC collaborations.

End of the basics lesson - Continue to learn how to build code and submit batch jobs

Overview

Teaching: 5 min
Exercises: 0 min

Questions

• How do I learn more about building code

Objectives

• Learn how to modify LArSoft the old-fashioned way on SL7

• Get pointers to the new batch submission system – justIn

You can continue on with these additional modules.

Key Points

• Currently we use ups/mrb to modify LArSoft. They will be replaced with spack modules soon but if you have work to do now, this is how to do it.

• Check out the justIn tutorials

Bonus episode -- Code-makeover on how to code for better efficiency

Overview

Teaching: 50 min
Exercises: 0 min

Questions

• How to write the most efficient code?

Objectives

• Learn good tips and tools to improve your code.

Session Video

The session will be captured on video a placed here after the workshop for asynchronous study.

Live Notes

Code Make-over

How to improve your code for better efficiency

DUNE simulation, reconstruction and analysis jobs take a lot of memory and CPU time. This owes to the large size of the Far Detector modules as well as the many channels in the Near Detectors. Reading out a large volume for a long time with high granularity creates a lot of data that needs to be stored and processed.

CPU optimization:

Run with the “prof” build when launching big jobs. While both the “debug” and “prof” builds have debugging and profiling information included in the executables and shared libraries, the “prof” build has a high level of compiler optimization turned on while “debug” has optimizations disabled. Debugging with the “prof” build can be done, but it is more difficult because operations can be reordered and some variables get put in CPU registers instead of inspectable memory. The “debug” builds are generally much slower, by a factor of four or more. Often this difference is so stark that the time spent repeatedly waiting for a slow program to chug through the first trigger record in an interactive debugging session is more costly than the inconvenience of not being able to see some of the variables in the debugger. If you are not debugging, then there really is (almost) no reason to use the “debug” builds. If your program produces a different result when run with the debug build and the prof build (and it’s not just the random seed), then there is a bug to be investigated.

Compile your interactive ROOT scripts instead of running them in the interpreter At the ROOT prompt, use .L myprogram.C++ (even though its filename is myprogram.C). Also .x myprogram.C++ will compile and then execute it. This will force a compile. .L myprogram.C+ will compile it only if necessary.

Run gprof or other profilers like valgrind’s callgrind: You might be surprised at what is actually taking all the time in your program. There is abundant documentation on the web, and also the valgrind online documentation. There is no reason to profile a “debug” build and there is no need to hand-optimize something the compiler will optimize anyway, and which may even hurt the optimality of the compiler-optimized version.

The Debugger can be used as a simple profiler: If your program is horrendously slow (and/or it used to be fast), pausing it at any time is likely to pause it while it is doing its slow thing. Run your program in the debugger, pause it when you think it is doing its slow thing (i.e. after initialization), and look at the call stack. This technique can be handy because you can then inspect the values of variables that might give a clue if there’s a bug making your program slow. (e.g. looping over 10¹⁵ wires in the Far Detector, which would indicate a bug, such as an uninitialized loop counter or an unsigned loop counter that is initialized with a negative value.

Don’t perform calculations or do file i/o that will only later be ignored. It’s just a waste of time. If you need to pre-write some code because in future versions of your program the calculation is not ignored, comment it out, or put a test around it so it doesn’t get executed when it is not needed.

Extract constant calculations out of loops.

The example above also takes advantage of the fact that floating-point multiplies generally have significantly less latency than floating-point divides (this is still true, even with modern CPUs).

Use sqrt(): Don’t use pow() or TMath::Power when a multiplication or sqrt() function can be used.

The reason is that TMath::Power (or the C math library’s pow()) function must take the logarithm of one of its arguments, multiply it by the other argument, and exponentiate the result. Modern CPUs have a built-in SQRT instruction. Modern versions of pow() or Power may check the power argument for 2 and 0.5 and instead perform multiplies and SQRT, but don’t count on it.

If the things you are squaring above are complicated expressions, use TMath::Sq() to eliminate the need for typing them out twice or creating temporary variables. Or worse, evaluating slow functions twice. The optimizer cannot optimize the second call to that function because it may have side effects like printing something out to the screen or updating some internal variable and you may have intended for it to be called twice.

Don’t call sqrt() if you don’t have to.

Use binary search features in the STL rather than a step-by-step lookup.

std::vector<int> my_vector;
(fill my_vector with stuff)

size_t indexfound = 0;
bool found = false;
for (size_t i=0; i<my_vector.size(); ++i)
{
  if (my_vector.at(i) == desired_value)
    {
	indexfound = i;
	found = true;
    }
}

If you have to search through a list of items many times, it is best to sort it and use std::lower_bound; see the example here. std::map is sorted, and std::unordered_map uses a quicker hash table. Generally looking things up in maps is O(log(n)) and in a std::unordered_map is O(1) in CPU time, while searching for it from the beginning is O(n). The bad example above can be sped up by an average factor of 2 by putting a break statement after found=true; if you want to find the first instance of an object. If you want to find the last instance, just count backwards and stop at the first one you find; or use std::upper_bound.

Don’t needlessly mix floats and doubles.

Minimize conversions between int and float or double

The up-conversion from int to float takes time, and the down-conversion from float to int loses precision and also takes time. Sometimes you want the precision loss, but sometimes it’s a mistake.

Check for NaN and Inf. While your program will still function if an intermediate result is NaN or Inf (and it may even produce valid output, especially if the NaN or Inf is irrelevant), processing NaNs and Infs is slower than processing valid numbers. Letting a NaN or an Inf propagate through your calculations is almost never the right thing to do - check functions for domain validity (square roots of negative numbers, logarithms of zero or negative numbers, divide by zero, etc.) when you execute them and decide at that point what to do. If you have a lengthy computation and the end result is NaN, it is often ambiguous at what stage the computation failed.

Pass objects by reference. Especially big ones. C and C++ call semantics specify that objects are passed by value by default, meaning that the called method gets a copy of the input. This is okay for scalar quantities like int and float, but not okay for a big vector, for example. The thing to note then is that the called method may modify the contents of the passed object, while an object passed by value can be expected not to be modified by the called method.

Use references to receive returned objects created by methods That way they don’t get copied. The example below is from the VD coldbox channel map. Bad, inefficient code courtesy of Tom Junk, and good code suggestion courtesy of Alessandro Thea. The infotohcanmap object is a map of maps of maps: std::unordered_map<int,std::unordered_map<int,std::unordered_map<int,int> > > infotochanmap;

Minimize cloning TH1’s. It is really slow.

Minimize formatted I/O. Formatting strings for output is CPU-consuming, even if they are never printed to the screen or output to your logfile. MF_LOG_INFO calls for example must prepare the string for printing even if it is configured not to output it.

Avoid using caught exceptions as part of normal program operation While this isn’t an efficiency issue or even a code readability issue, it is a problem when debugging programs. Most debuggers have a feature to set a breakpoint on thrown exceptions. This is sometimes necessary to use in order to track down a stubborn bug. Bugs that stop program execution like segmentation faults are sometimes easer to track down than caught exceptions (which often aren’t even bugs but sometimes they are). If many caught exceptions take place before the buggy one, then the breakpoint on thrown exceptions has limited value.

Use sparse matrix tools where appropriate. This also saves memory.

Minimize database access operations. Bundle the queries together in blocks if possible. Do not pull more information than is needed out of the database. Cache results so you don’t have to repeat the same data retrieval operation.

Use std::vector::reserve() in order to size your vector right if you know in advance how big it will be. std::vector() will, if you push_back() to expand it beyond its current size in memory, allocate twice the memory of the existing vector and copy the contents of the old vector to the new memory. This operation will be repeated each time you start with a zero-size vector and push_back a lot of data. Factorize your program into parts that do i/o and compute. That way, if you don’t need to do one of them, you can switch it off without having to rewrite everything. Example: Say you read data in from a file and make a histogram that you are sometimes interested in looking at but usually not. The data reader should not always make the histogram by default but it should be put in a separate module which can be steered with fcl so the computations needed to calculate the items to fill the histogram can be saved.

Memory optimization:

Use valgrind. Its default operation checks for memory leaks and invalid accesses. Search the output for the words “invalid” and “lost”. Valgrind is a UPS product you can set up along with everything else. It is set up as part of the dunesw stack.

setup valgrind
valgrind --leak-check=yes --suppressions=$ROOTSYS/etc/valgrind-root.supp myprog arg1 arg2

More information is available here. ROOT-specific suppressions are described here. You can omit them, but your output file will be cluttered up with messages about things that ROOT does routinely that are not bugs.

Use massif. massif is a heap checker, a tool provided with valgrind; see documentation here.

Free up memory after use. Don’t hoard it after your module’s exited.

Don’t constantly re-allocate memory if you know you’re going to use it again right away.

Use STL containers instead of fixed-size arrays, to allow for growth in size. Back in the bad old days (Fortran 77 and earlier), fixed-size arrays had to be declared at compile time that were as big as they possibly could be, both wasting memory on average and creating artificial cutoffs on the sizes of problems that could be handled. This behavior is very easy to replicate in C++. Don’t do it.

Be familiar with the structure and access idioms. These include std::vector, std::map, std::unordered_map, std::set, std::list.

Minimize the use of new and delete to reduce the chances of memory leaks. If your program doesn’t leak memory now, that’s great, but years from now after maintenance has been transferred, someone might introduce a memory leak.

Use move semantics to transfer data ownership without copying it.

Do not store an entire event’s worth of raw digits in memory all at once. Find some way to process the data in pieces.

Consider using more compact representations in memory. A float takes half the space of a double. A size_t is 64 bits long (usually). Often that’s needed, but sometimes it’s overkill.

Optimize the big uses and don’t spend a lot of time on things that don’t matter. If you have one instance of a loop counter that’s a size_t and it loops over a million vector entries, each of which is an int, look at the entries of the vector, not the loop counter (which ought to be on the stack anyway).

Rebin histograms. Some histograms, say binned in channels x ticks or channels x frequency bins for a 2D FFT plot, can get very memory hungry.

I/O optimization:

Do as much calculation as you can per data element read. You can spin over a TTree once per plot, or you can spin through the TTree once and make all the plots. ROOT compresses data by default on write and uncompresses it on readin, so this is both an I/O and a CPU issue, to minimize the data that are read.

Read only the data you need ROOT’s TTree access methods are set up to give you only the requested TBranches. If you use TTree::MakeClass to write a template analysis ROOT macro script, it will generate code that reads in all TBranches and leaves. It is easy to trim out the extras to speed up your workflow.

Saving compressed data reduces I/O time and storage needs. Even though compressing data takes CPU, a slow disk or network can mean your workflow is in fact faster to trade CPU time instead of the disk read time.

Stream data with xrootd You will wait less for your first event than if you copy the file, put less stress on the data storage elements, and have more reliable i/o with dCache.

Build time optimization:

Minimize the number of #included files. If you don’t need an #include, don’t use it. It takes time to find these files in the search path and include them.

Break up very large source files into pieces. g++’s analysis and optimization steps take an amount of time that grows faster than linearly with the number of source lines.

Use ninja instead of make Instructions are here

Workflow optimization:

Pre-stage your datasets It takes a lot of time to wait for a tape (sometimes hours!). CPUs are accounted by wall-clock time, whether you’re using them or not. So if your jobs are waiting for data, they will run slowly even if you optimized the CPU usage. Pre-stage your data!

Run a test job If you have a bug, you will save time by not submitting large numbers of jobs that might not work.

Write out your variables in your own analysis ntuples (TTrees) You will likely have to run over the same MC and data events repeatedly, and the faster this is the better. You will have to adjust your cuts, tune your algorithms, estimate systematic uncertainties, train your deep-learning functions, debug your program, and tweak the appearance of your plots. Ideally, if the data you need to do these operatios is available interctively, you will be able to perform these tasks faster. Choose a minimal set of variables to put in your ntuples to save on storage space.

Write out histograms to ROOTfiles and decorate them in a separate script You may need to experiment many times with borders, spacing, ticks, fonts, colors, line widths, shading, labels, titles, legends, axis ranges, etc. Best not to have to re-compute the contents when you’re doing this, so save the histograms to a file first and read it in to touch it up for presentation.

Software readability and maintainability:

Keep the test suite up to date dunesw and larsoft have many examples of unit tests and integration tests. A colleague’s commit to your code or even to a different piece of code or even a data file might break your code in unexpected, difficult-to-diagnose ways. The continuous integration (CI) system is there to catch such breakage, and even small changes in run time, memory consumption, and data product output.

Keep your methods short If you have loaded up a lot of functionality in a method, it may become hard to reuse the components to do similar things. A long method is probably doing a lot of different things that can be given meaningful names.

Update the comments when code changes Not many things are more confusing than an out-of-date-comment that refers to how code used to work long ago.

Update names when meaning changes As software evolves, the meaning of the variables may shift. It may be a quick fix to change the contents of a variable without changing its name, but some variables may then contain contents that is the opposite of what the variable name implies. While the code will run, future maintainers will get confused.

Use const frequently The const keyword prevents overwriting variables unintentionally. Constness is how art protects the data in its event memory. This mechanism is exposed to the user in that pointers to const memory must be declared as pointers to consts, or you will get obscure error messages from the compiler. Const can also protect you from yourself and your colleagues when you know that the contents of a variable ought not to change.

Use simple constructs even if they are more verbose Sometimes very clever, terse expressions get the job done, but they can be difficult for a human to understand if and when that person must make a change. There is an obfuscated C contest if you want to see examples of difficult-to-read code (that may in fact be very efficient! But people time is important, too).

Always initialize variables when you declare them Compilers will warn about the use of uninitialized variables, so you will get used to doing this anyway. The initialization step takes a little time and it is not needed if the first use of the memory is to set the variable, which is why compilers do not automatically initialize variables.

Minimize the scope of variables Often a variable will only have a meaningful value iniside of a loop. You can declare variables as you use them. Old langauges like Fortran 77 insisted that you declare all variables at the start of a program block. This is not true in C and C++. Declaring variables inside of blocks delimiated by braces means they will go out of scope when the program exits the block, both freeing the memory and preventing you from referring to the variable after the loop is done and only considering the last value it took. Sometimes this is the desired behaviour, though, and so this is not a blanket rule.

Coding for Thread Safety

Modern CPUs often have many cores available. It is not unusual for a grid worker node to have as many as 64 cores on it, and 128 GB of RAM. Making use of the available hardware to maximize throughput is an important way to optimize our time and resources. DUNE jobs tend to be “embarrassingly parallel”, in that they can be divided up into many small jobs that do not need to communicated with one another. Therefore, making use of all the cores on a grid node is usually as easy as breaking a task up into many small jobs and letting the grid schedulers work out what jobs run where. The issue however is effective memory usage. If several small jobs share a lot of memory whose contents do not change (code libraries loaded into RAM, geometry description, calibration constants), then one can group the work together into a single job that uses multiple threads to get the work done faster. If the memory usage of a job is dominated by per-event data, then loading multiple events’ worth of data in RAM in order to keep all the cores fed with data may not provide a noticeable improvement in the utilization of CPU time relative to memory time.

Sometimes multithreading has advantages within a trigger record. Data from different wires or APAs may be processed simultaneously. One thing software managers would like to make sure is controllable is the number of threads a program is allowed to spawn. Some grid sites do not have an automatic protection against a program that creates more threads than CPUs it has requested. Instead, a human operator may notice that the load on a system is far greater than the number of cores, and track down and ban the offending job sumitter (this has already happened on DUNE). If a program contains components, some of which manage their own threawds, then it becomes hard to manage the total thread count in a program. Multithreaded art keeps track of the total thread count using TBB, or Thread Building Blocks.

See this very thorough presentation by Kyle Knoepfel at the 2019 LArSoft workshop. Several other talks at the workshop also focus on multi-threaded software. In short, if data are shared between threads and they are mutable, this is a recipe for race conditions and non-reproducible behavior of programs. Giving each thread a separate instance of each object is one way to contain possible race conditions. Alternately, private and public class members which do not change or which have synchronous access methods can also help provide thread safety.

Key Points

• CPU, memory, and build time optimizations are possible when good code practices are followed.

Multi Repository Build (mrb) system (2024)

Overview

Teaching: 10 min
Exercises: 0 min

Questions

• How are different software versions handled?

Objectives

• Understand the roles of the tool mrb

mrb

What is mrb and why do we need it?
Early on, the LArSoft team chose git and cmake as the software version manager and the build language, respectively, to keep up with industry standards and to take advantage of their new features. When we clone a git repository to a local copy and check out the code, we end up building it all. We would like LArSoft and DUNE code to be more modular, or at least the builds should reflect some of the inherent modularity of the code.

Ideally, we would like to only have to recompile a fraction of the software stack when we make a change. The granularity of the build in LArSoft and other art-based projects is the repository. So LArSoft and DUNE have divided code up into multiple repositories (DUNE ought to divide more than it has, but there are a few repositories already with different purposes). Sometimes one needs to modify code in multiple repositories at the same time for a particular project. This is where mrb comes in.

mrb stands for “multi-repository build”. mrb has features for cloning git repositories, setting up build and local products environments, building code, and checking for consistency (i.e. there are not two modules with the same name or two fcl files with the same name). mrb builds UPS products – when it installs the built code into the localProducts directory, it also makes the necessasry UPS table files and .version directories. mrb also has a tool for making a tarball of a build product for distribution to the grid. The software build example later in this tutorial exercises some of the features of mrb.

Link to the mrb reference guide

Exercise 1

There is no exercise 5. mrb example exercises will be covered in a later session as any useful exercise with mrb takes more than 30 minutes on its own. Everyone gets 100% credit for this exercise!

Key Points

• The multi-repository build (mrb) tool allows code modification in multiple repositories, which is relevant for a large project like LArSoft with different cases (end user and developers) demanding consistency between the builds.

Expert in the Room - LArSoft How to modify a module - in progress

Overview

Teaching: 15 min
Exercises: 0 min

Questions

• How do I check out, modify, and build DUNE code?

Objectives

• How to use mrb.

• Set up your environment.

• Download source code from DUNE’s git repository.

• Build it.

• Run an example program.

• Modify the job configuration for the program.

• Modify the example module to make a custom histogram.

• Test the modified module.

• Stretch goal – run the debugger.

getting set up

You will need three login sessions. These have different environments set up.

• Session #1 For editing code (and searching for code)
• Session #2 For building (compiling) the software
• Session #3 For running the programs

Session 1

Start up session #1, editing code, on one of the dunegpvm*.fnal.gov interactive nodes. These scripts have also been tested on the lxplus.cern.ch interactive nodes.

Note Remember the Apptainer!

see below for special Apptainers for CERN and build machines.

Create two scripts in your home directory:

newDev2024Tutorial.sh should have these contents:

#!/bin/bash
export DUNELAR_VERSION=v09_90_01d00
export PROTODUNEANA_VERSION=$DUNELAR_VERSION
DUNELAR_QUALIFIER=e26:prof
DIRECTORY=2024tutorial
USERNAME=`whoami`
export WORKDIR=/exp/dune/app/users/${USERNAME}
if [ ! -d "$WORKDIR" ]; then
  export WORKDIR=`echo ~`
fi

source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh

cd ${WORKDIR}
touch ${DIRECTORY}
rm -rf ${DIRECTORY}
mkdir ${DIRECTORY}
cd ${DIRECTORY}
mrb newDev -q ${DUNELAR_QUALIFIER}
source ${WORKDIR}/${DIRECTORY}/localProducts*/setup
mkdir work
cd srcs
mrb g -t ${PROTODUNEANA_VERSION} protoduneana

cd ${MRB_BUILDDIR}
mrbsetenv
mrb i -j16

and setup2024Tutorial.sh should have these contents:

DIRECTORY=2024tutorial
USERNAME=`whoami`

source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
export WORKDIR=/exp/dune/app/users/${USERNAME}
if [ ! -d "$WORKDIR" ]; then
  export WORKDIR=`echo ~`
fi

cd $WORKDIR/$DIRECTORY
source localProducts*/setup
cd work
setup dunesw $DUNELAR_VERSION -q $DUNELAR_QUALIFIER
mrbslp

Execute this command to make the first script executable.

  chmod +x newDev2024Tutorial.sh

It is not necessary to chmod the setup script. Problems writing to your home directory? Check to see if your Kerberos ticket has been forwarded.

  klist

Session 2

Start up session #2 by logging in to one of the build nodes, dunebuild01.fnal.gov or dunebuild02.fnal.gov. They have 16 cores apiece and the dunegpvm’s have only four, so builds run much faster on them. If all tutorial users log on to the same one and try building all at once, the build nodes may become very slow or run out of memory. The lxplus nodes are generally big enough to build sufficiently quickly. The Fermilab build nodes should not be used to run programs (people need them to build code!)

Note you need a modified container on the build machines and at CERN as they don’t mount /pnfs

This is done to prevent people from running interactive jobs on the dedicated build machines.

FNAL build machines

# remove /pnfs/ for build machines
/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash \
-B /cvmfs,/exp,/nashome,/opt,/run/user,/etc/hostname,/etc/hosts,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest

CERN

/cvmfs/oasis.opensciencegrid.org/mis/apptainer/current/bin/apptainer shell --shell=/bin/bash\
-B /cvmfs,/afs,/opt,/run/user,/etc/hostname,/etc/krb5.conf --ipc --pid \
/cvmfs/singularity.opensciencegrid.org/fermilab/fnal-dev-sl7:latest

Download source code and build it

On the build node, execute the newDev script:

  ./newDev2024Tutorial.sh

Note that this script will delete the directory planned to store the source code and built code, and make a new directory, in order to start clean. Be careful not to execute this script then if you’ve worked on the code some, as this script will wipe it out and start fresh.

This build script will take a few minutes to check code out and compile it.

The mrb g command does a git clone of the specified repository with an optional tag and destination name. More information is available here and here.

Some comments on the build command

  mrb i -j16

The -j16 says how many concurrent processes to run. Set the number to no more than the number of cores on the computer you’re running it on. A dunegpvm machine has four cores, and the two build nodes each have 16. Running more concurrent processes on a computer with a limited number of cores won’t make the build finish any faster, but you may run out of memory. The dunegpvms do not have enough memory to run 16 instances of the C++ compiler at a time, and you may see the word killed in your error messages if you ask to run many more concurrent compile processes than the interactive computer can handle.

You can find the number of cores a machine has with

  cat /proc/cpuinfo

The mrb system builds code in a directory distinct from the source code. Source code is in $MRB_SOURCE and built code is in $MRB_BUILDDIR. If the build succeeds (no error messages, and compiler warnings are treated as errors, and these will stop the build, forcing you to fix the problem), then the built artifacts are put in $MRB_TOP/localProducts*. mrbslp directs ups to search in $MRB_TOP/localProducts* first for software and necessary components like fcl files. It is good to separate the build directory from the install directory as a failed build will not prevent you from running the program from the last successful build. But you have to look at the error messages from the build step before running a program. If you edited source code, made a mistake, built it unsuccessfully, then running the program may run successfully with the last version which compiled. You may be wondering why your code changes are having no effect. You can look in $MRB_TOP/localProducts* to see if new code has been added (look for the “lib” directory under the architecture-specific directory of your product).

Because you ran the newDev2024Tutorial.sh script instead of sourcing it, the environment it set up within it is not retained in the login session you ran it from. You will need to set up your environment again. You will need to do this when you log in anyway, so it is good to have that setup script. In session #2, type this:

  source setup2024Tutorial.sh
  cd $MRB_BUILDDIR
  mrbsetenv

The shell command “source” instructs the command interpreter (bash) to read commands from the file setup2024Tutorial.sh as if they were typed at the terminal. This way, environment variables set up by the script stay set up. Do the following in session #1, the source editing session:

source setup2024Tutorial.sh
  cd $MRB_SOURCE
  mrbslp

Run your program

YouTube Lecture Part 2: Start up the session for running programs – log in to a dunegpvm interactive computer for session #3

  source setup2024Tutorial.sh
  mrbslp
  setup_fnal_security

We need to locate an input file. Here are some tips for finding input data:

https://wiki.dunescience.org/wiki/Look_at_ProtoDUNE_SP_data

Data and MC files are typically on tape, but can be cached on disk so you don’t have to wait possibly a long time for the file to be staged in. Check to see if a sample file is in dCache or only on tape:

cache_state.py PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root

Get the xrootd URL:

samweb get-file-access-url --schema=root PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root

which should print the following URL:

root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root

Now run the program with the input file accessed by that URL:

lar -c analyzer_job.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root

CERN Users without access to Fermilab’s dCache: – example input files for this tutorial have been copied to /afs/cern.ch/work/t/tjunk/public/2024tutorialfiles/.

After running the program, you should have an output file tutorial_hist.root. Note – please do not store large rootfiles in /exp/dune/app! The disk is rather small, and we’d like to save it for applications, not data. But this file ought to be quite small. Open it in root

  root tutorial_hist.root

and look at the histograms and trees with a TBrowser. It is empty!

Adjust the program’s job configuration

In Session #1, the code editing session,

  cd ${MRB_SOURCE}/protoduneana/protoduneana/TutorialExamples/

See that analyzer_job.fcl includes clustercounter.fcl. The module_type line in that fcl file defines the name of the module to run, and ClusterCounter_module.cc just prints out a message in its analyze() method just prints out a line to stdout for each event, without making any histograms or trees.

Aside on module labels and types: A module label is used to identify which modules to run in which order in a trigger path in an art job, and also to label the output data products. The “module type” is the name of the source file: moduletype_module.cc is the filename of the source code for a module with class name moduletype. The build system preserves this and makes a shared object (.so) library that art loads when it sees a particular module_type in the configuration document. The reason there are two names here is so you can run a module multiple times in a job, usually with different inputs. Underscores are not allowed in module types or module labels because they are used in contexts that separate fields with underscores.

Let’s do something more interesting than ClusterCounter_module’s print statement.

Let’s first experiment with the configuration to see if we can get some output. In Session #3 (the running session),

  fhicl-dump analyzer_job.fcl > tmp.txt

and open tmp.txt in a text editor. You will see what blocks in there contain the fcl parameters you need to adjust. Make a new fcl file in the work directory called myana.fcl with these contents:

#include "analyzer_job.fcl"

physics.analyzers.clusterana.module_type: "ClusterCounter3"

Try running it:

  lar -c myana.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root

but you will get error messages about “product not found”. Inspection of ClusterCounter3_module.cc in Session #1 shows that it is looking for input clusters. Let’s see if we have any in the input file, but with a different module label for the input data.

Look at the contents of the input file:

  product_sizes_dumper root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root | grep -i cluster

There are clusters with module label “pandora” but not lineclusterdc which you can find in the tmp.txt file above. Now edit myana.fcl to say

#include "analyzer_job.fcl"

physics.analyzers.clusterana.module_type: "ClusterCounter3"
physics.analyzers.clusterana.ClusterModuleLabel: "pandora"

and run it again:

  lar -c myana.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root

Lots of information on job configuration via FHiCL is available at this link

Editing the example module and building it

YouTube Lecture Part 3: Now in session #1, edit ${MRB_SOURCE}/protoduneana/protoduneana/TutorialExamples/ClusterCounter3_module.cc

Add

#include "TH1F.h"

to the section with includes.

Add a private data member

TH1F *fTutorialHisto;

to the class. Create the histogram in the beginJob() method:

fTutorialHisto = tfs->make<TH1F>("TutorialHisto","NClus",100,0,500);

Fill the histo in the analyze() method, after the loop over clusters:

fTutorialHisto->Fill(fNClusters);

Go to session #2 and build it. The current working directory should be the build directory:

make install -j16

Note – this is the quicker way to re-build a product. The -j16 says to use 16 parallel processes, which matches the number of cores on a build node. The command

mrb i -j16

first does a cmake step – it looks through all the CMakeLists.txt files and processes them, making makefiles. If you didn’t edit a CMakeLists.txt file or add new modules or fcl files or other code, a simple make can save you some time in running the single-threaded cmake step.

Rerun your program in session #3 (the run session)

  lar -c myana.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root

Open the output file in a TBrowser:

  root tutorial_hist.root

and browse it to see your new histogram. You can also run on some data.

  lar -c myana.fcl -T dataoutputfile.root root://fndca1.fnal.gov/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2020/detector/physics/PDSPProd4/00/00/53/87/np04_raw_run005387_0041_dl7_reco1_13832298_0_20201109T215042Z.root

The -T dataoutputfile.root changes the output filename for the TTrees and histograms to dataoutputfile.root so it doesn’t clobber the one you made for the MC output.

This iteration of course is rather slow – rebuilding and running on files in dCache. Far better, if you are just changing histogram binning, for example, is to use the output TTree. TTree::MakeClass is a very useful way to make a script that reads in the TBranches of a TTree on a file. The workflow in this tutorial is also useful in case you decide to add more content to the example TTree.

Run your program in the debugger

YouTube Lecture Part 4: In session #3 (the running session)

  setup forge_tools

  ddt `which lar` -c myana.fcl root://fndca1.fnal.gov:1094/pnfs/fnal.gov/usr/dune/tape_backed/dunepro/protodune-sp/full-reconstructed/2021/mc/out1/PDSPProd4a/18/80/06/50/PDSPProd4a_protoDUNE_sp_reco_stage1_p1GeV_35ms_sce_datadriven_18800650_2_20210414T012053Z.root

Click the “Run” button in the window that pops up. The which lar is needed because ddt cannot find executables in your path – you have to specify their locations explicitly.

In session #1, look at ClusterCounter3_module.cc in a text editor that lets you know what the line numbers are. Find the line number that fills your new histogram. In the debugger window, select the “Breakpoints” tab in the bottom window, and usethe right-mouse button (sorry mac users – you may need to get an external mouse if you are using VNC. XQuartz emulates a three-button mouse I believe). Make sure the “line” radio button is selected, and type ClusterCounter3_module.cc for the filename. Set the breakpoint line at the line you want, for the histogram filling or some other place you find interesting. Click Okay, and “Yes” to the dialog box that says ddt doesn’t know about the source code yet but will try to find it when it is loaded.

Click the right green arrow to start the program. Watch the program in the Input/Output section. When the breakpoint is hit, you can browse the stack, inspect values (sometimes – it is better when compiled with debug), set more breakpoints, etc.

You will need Session #1 to search for code that ddt cannot find. Shared object libraries contain information about the location of the source code when it was compiled. So debugging something you just compiled usually results in a shared object that knows the location of the source, but installed code in CVMFS points to locations on the Jenkins build nodes.

Looking for source code:

Your environment has lots of variables pointing at installed code. Look for variables like

  PROTODUNEANA_DIR

which points to a directory in CVMFS.

  ls $PROTODUNEANA_DIR/source

or $LARDATAOBJ_DIR/include

are good examples of places to look for code, for example.

Checking out and committing code to the git repository

For protoduneana and dunesw, this wiki page is quite good. LArSoft uses GitHub with a pull-request model. See

https://cdcvs.fnal.gov/redmine/projects/larsoft/wiki/Developing_With_LArSoft

https://cdcvs.fnal.gov/redmine/projects/larsoft/wiki/Working_with_GitHub

Common errors and recovery

Version mismatch between source code and installed products

When you perform an mrbsetenv or a mrbslp, sometimes you get a version mismatch. The most common reason for this is that you have set up an older version of the dependent products. Dunesw depends on protoduneana, which depends on dunecore, which depends on larsoft, which depends on art, ROOT, GEANT4, and many other products. This picture shows the software dependency tree for dunesw v09_72_01_d00. If the source code is newer than the installed products, the versions may mismatch. You can check out an older version of the source code (see the example above) with

  mrb g -t <tag> repository

Alternatively, if you have already checked out some code, you can switch to a different tag using your local clone of the git repository.

  cd $MRB_SOURCE/<product>
  git checkout <tag>

Try mrbsetenv again after checking out a consistent version.

Telling what version is the right one

The versions of dependent products for a product you’re building from source are listed in the file $MRB_SOURCE/<product>/ups/product_deps`.

Sometimes you may want to know what the version number is of a product way down on the dependency tree so you can check out its source and edit it. Set up the product in a separate login session:

  source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
  setup <product> $DUNELAR_VERSION -q $DUNELAR_QUALIFIER
  ups active

It usually is a good idea to pipe the output through grep to find a particular product version. You can get dependency information with

  ups depend <product> $DUNELAR_VERSION -q $DUNELAR_QUALIFIER

Note: not all dependencies of dependent products are listed by this command. If a product is already listed, it sometimes is not listed a second time, even if two products in the tree depend on it. Some products are listed multiple times.

There is a script in duneutil called dependency_tree.sh which makes graphical displays of dependency trees.

Inconsistent build directory

The directory $MRB_BUILD contains copies of built code before it gets installed to localProducts. If you change versions of the source or delete things, sometimes the build directory will have clutter in it that has to be removed.

  mrb z

will delete the contents of $MRB_BUILDDIR and you will have to type mrbsetenv again.

  mrb zd

will also delete the contents of localProducts. This can be useful if you are removing code and want to make sure the installed version also has it gone.

Inconsistent environment

When you use UPS’s setup command, a lot of variables get defined. For each product, a variable called <product>_DIR is defined, which points to the location of the version and flavor of the product. UPS has a command “unsetup” which often succeeds in undoing what setup does, but it is not perfect. It is possible to get a polluted environment in which inconsistent versions of packages are set up and it is too hard to repair it one product at a time. Logging out and logging back in again, and setting up the session is often the best way to start fresh.

The setup command is the wrong one

If you have not sourced the DUNE software setup script

  source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh

you will find that the setup command that is used instead is one provided by the operating system and it requires root privilege to execute and setup will ask you for the root password. Rather than typing that if you get in this situation, ctrl-c, source the setup_dune.sh script and try again.

Compiler and linker warnings and errors

Common messages from the g++ compiler are undeclared variables, uninitialized variables, mismatched parentheses or brackets, missing semicolons, checking unsigned variables to see if they are positive (yes, that’s a warning!) and other things. mrb is set up to tell g++ and clang to treat warnings as errors, so they will stop the build and you will have to fix them. Often undeclared variables or methods that aren’t members of a class messages result from having forgotten to include the appropriate include file.

The linker has fewer ways to fail than the compiler. Usually the error message is “Undefined symbol”. The compiler does not emit this message, so you always know this is in the link step. If you have an undefined symbol, one of three things may have gone wrong. 1) You may have mistyped it (usually this gets caught by the compiler because names are defined in header files). More likely, 2) You introduced a new dependency without updating the CMakeLists.txt file. Look in the CMakeLists.txt file that steers the building of the source code that has the problem. Look at other CMakeLists.txt files in other directories for examples of how to refer to libraries. ` MODULE_LIBRARIES are linked with modules in the ART_MAKE blocks, and LIB_LIBRARIES` are linked when building non-module libraries (free-floating source code, for algorithms). 3) You are writing new code and just haven’t gotten around to finishing writing something you called.

Out of disk quota

Do not store data files on the app disk! Sometimes the app disk fills up nonetheless, and there is a quota of 100 GB per user on it. If you need more than that for several builds, you have some options. 1) Use /exp/dune/data/users/<username>. You have a 400 GB quota on this volume. They are slower than the app disk and can get even slower if many users are accessing them simultaneously or transferring large amounts of data to or ofrm them. 3) Clean up some space on app. You may want to tar up an old release and store the tarball on the data volume or in dCache for later use.

Runtime errors

Segmentation faults: These do not throw errors that art can catch. They terminate the program immediately. Use the debugger to find out where they happened and why.

Exceptions that are caught. The ddt debugger has in its menu a set of standard breakpoints. You can instruct the debugger to stop any time an exception is thrown. A common exception is a vector accessed past its size using at(), but often these are hard to track down because they could be anywhere. Start your program with the debugger, but it is often a good idea to turn off the break-on-exception feature until after the geometry has been read in. Some of the XML parsing code throws a lot of exceptions that are later caught as part of its normal mode of operation, and if you hit a breakpoint on each of these and push the “go” button with your mouse each time, you could be there all day. Wait until the initialization is over, press “pause” and then turn on the breakpoints by exception.

If you miss, start the debugging session over again. Starting the session over is also a useful technique when you want to know what happened before a known error condition occurs. You may find yourself asking “how did it get in that condition? Set a breakpoint that’s earlier in the execution and restart the session. Keep backing up – it’s kind of like running the program in reverse, but it’s very slow. Sometimes it’s the only way.

Print statements are also quite useful for rare error conditions. If a piece of code fails infrequently, based on the input data, sometimes a breakpoint is not very useful because most of the time it’s fine and you need to catch the program in the act of misbehaving. Putting in a low-tech print statement, sometimes with a uniquely-identifying string so you can grep the output, can let you put some logic in there to print only when things have gone bad, or even if you print on each iteration, you can just look at the last bit of printout before a crash.

No authentication/permission

You will almost always need to have a valid Kerberos ticket in your session. Accessing your home directory on the Fermilab machines requires it. Find your tickets with the command

  klist

By default, they last for 25 hours or so (a bit more than a day). You can refresh them for another 25 hours (up to one week’s worth of refreshes are allowed) with

  kinit -R

If you have a valid ticket on one machine and want to refresh tickets on another, you can

k5push <nodename>

The safest way to get a new ticket to a machine is to kinit on your local computer (like your laptop) and log in again, making sure to forward all tickets. In a pinch, you can run kinit on a dunegpvm and enter your Kerberos password, but this is discouraged as bad actors can (and have!) installed keyloggers on shared systems, and have stolen passwords. DO NOT KEEP PRIVATE, PERSONAL INFORMATION ON FERMILAB COMPUTERS! Things like bank account numbers, passwords, and social security numbers are definitely not to be stored on public, shared computers. Running kinit -R on a shared machine is fine.

You will need a grid proxy to submit jobs and access data in dCache via xrootd or ifdh.

  setup_fnal_security

will use your valid Kerberos ticket to generate the necessary certificates and proxies.

Link to art/LArSoft tutorial May 2021

https://wiki.dunescience.org/wiki/Presentation_of_LArSoft_May_2021

Key Points

• DUNE’s software stack is built out of a tree of UPS products.

• You don’t have to build all of the software to make modifications – you can check out and build one or more products to achieve your goals.

• You can set up pre-built CVMFS versions of products you aren’t developing, and UPS will check version consistency, though it is up to you to request the right versions.

• mrb is the tool DUNE uses to check out software from multiple repositories and build it in a single test release.

• mrb uses git and cmake, though aspects of both are exposed to the user.

Grid Job Submission and Common Errors

Overview

Teaching: 65 min
Exercises: 0 min

Questions

• How to submit grid jobs?

Objectives

• Submit a basic batchjob and understand what’s happening behind the scenes

• Monitor the job and look at its outputs

• Review best practices for submitting jobs (including what NOT to do)

• Extension; submit a small job with POMS

Note:

This section describes basic job submission. Large scale submission of jobs to read DUNE data files are described in the next section.

Session Video

This session will be captured on video a placed here after the workshop for asynchronous study. The video from the two day version of this training in May 2022 is provided here as a reference.

Live Notes

Participants are encouraged to monitor and utilize the Livedoc for May. 2023 to ask questions and learn. For reference, the Livedoc from Jan. 2023 is provided.

Temporary Instructor Note:

The May 2023 training event was cloned from the May 2022, both two day events.

This lesson (07-grid-job-submission.md) was imported from the Jan. 2023 lesson which was a one half day version of the training.

Quiz blocks are added at the bottom of this page, and invite your review, modify, review, and additional comments.

The official timetable for this training event is on the Indico site.

Notes on changes in the 2023/2024 versions

The past few months have seen significant changes in how DUNE (as well as other FNAL experiments) submits jobs and interacts with storage elements. While every effort was made to preserve backward compatibility a few things will be slightly different (and some are easier!) than what’s been shown at previous versions. Therefore even if you’ve attended this tutorial multiple times in past and know the difference between copying and streaming, tokens vs. proxies, and know your schedds from your shadows, you are encouraged to attend this session. Here is a partial list of significant changes:

• The jobsub_client product generally used for job submission has been replaced by the jobsub_lite product, which is very similar to jobsub_client except there is no server on the other side (i.e. there is more direct HTCondor interaction). You no longer need to set up the jobsub_client product as part of your software setup; it is installed via RPM now on all DUNE interactive machines. See this Wiki page for some differences between jobsub_lite and legacy jobsub.
• As of May 2024 you cannot submit batch jobs from SL7 containers but many submission scripts only run on SL7. You need to record the submission command from SL7 and the open a separate window running Alma9 and execute that command.
• Authentication via tokens instead of proxies is now rolling out and is now the primary authentication method. Please note that not only are tokens used for job submission now, they are also used for storage element access.
• It is no longer possible to write to certain directories from grid jobs as analysis users, namely the persistent area. Read access to the full /pnfs tree is still available. Bulk copies of job outputs from scratch to persistent have to be done outside of grid jobs.
• Multiple --tar_file_name options are now supported (and will be unpacked) if you need things in multiple tarballs.
• The -f behavior with and without dropbox:// in front is slightly different from legacy jobsub; see the documentation for details.
• jobsub_lite will probably not work directly from lxplus at the moment, though work is underway to make it possible to submit batch jobs to non-FNAL schedulers.

Submit a job

**Note that job submission requires FNAL account or access to another HTCondor submission point conneected to the Fermilab pool (currently BNL or RAL).

First, log in to a dunegpvm machine . Then you will need to set up the job submission tools (jobsub). If you set up dunesw it will be included, but if not, you need to do

mkdir -p /pnfs/dune/scratch/users/${USER}/DUNE_tutorial_may2023 # if you have not done this before
mkdir -p /pnfs/dune/scratch/users/${USER}/may2023tutorial

Having done that, let us submit a prepared script:

jobsub_submit -G dune --mail_always -N 1 --memory=1000MB --disk=1GB --cpu=1 --expected-lifetime=1h  --singularity-image /cvmfs/singularity.opensciencegrid.org/fermilab/fnal-wn-sl7:latest --append_condor_requirements='(TARGET.HAS_Singularity==true&&TARGET.HAS_CVMFS_dune_opensciencegrid_org==true&&TARGET.HAS_CVMFS_larsoft_opensciencegrid_org==true&&TARGET.CVMFS_dune_opensciencegrid_org_REVISION>=1105)' -e GFAL_PLUGIN_DIR=/usr/lib64/gfal2-plugins -e GFAL_CONFIG_DIR=/etc/gfal2.d file:///dune/app/users/kherner/submission_test_singularity.sh

If all goes well you should see something like this:

Attempting to get token from https://htvaultprod.fnal.gov:8200 ... succeeded
Storing bearer token in /tmp/bt_token_dune_Analysis_11469
Transferring files to web sandbox...
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_21_205736_877318d7-d14a-4c5a-b2fc-7486f1e54fa2/submission_test_singularity.sh   [DONE]  after 2s                                                                                                               
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_21_205736_877318d7-d14a-4c5a-b2fc-7486f1e54fa2/simple.cmd   [DONE]  after 5s                                                                                                                                   
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_21_205736_877318d7-d14a-4c5a-b2fc-7486f1e54fa2/simple.sh   [DONE]  after 5s                                                                                                                                    
Submitting job(s).
1 job(s) submitted to cluster 710165.

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Use job id 710165.0@jobsub05.fnal.gov to retrieve output
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Quiz

1. What is your job ID?

Note if you have not submitted a DUNE batch job within the past 30 days: you may be asked to (re-)authenticate. If so you will see the following:

Attempting OIDC authentication with https://htvaultprod.fnal.gov:8200

Complete the authentication at:
    https://cilogon.org/device/?user_code=ABC-D1E-FGH
No web open command defined, please copy/paste the above to any web browser
Waiting for response in web browser

The user code will be different of course. In this particular case, you do want to follow the instructions and copy and paste the link into your browser (can be any browser). There is a time limit on it so its best to do it right away. Always choose Fermilab as the identity provider in the menu, even if your home institution is listed. After you hit log on, you’ll get a message saying you approved the access request, and then after a short delay (may be several seconds) in the terminal you will see

Saving credkey to /nashome/u/username/.config/htgettoken/credkey-dune-default
Saving refresh token ... done
Attempting to get token from https://htvaultprod.fnal.gov:8200 ... succeeded
Storing bearer token in /tmp/bt_token_dune_Analysis_somenumber.othernumber
Storing condor credentials for dune
Submitting job(s)
.
1 job(s) submitted to cluster 57110235.

Now, let’s look at some of these options in more detail.

• --mail_always sends mail after the job completes whether it was successful for not. To disable all emails, use --mail_never.
• -N controls the number of identical jobs submitted with each cluster. Also called the process ID, the number ranges from 0 to N-1 and forms the part of the job ID number after the period, e.g. 12345678.N.
• --memory, --disk, --cpu, --expected-lifetime request this much memory, disk, number of cpus, and max run time. Jobs that exceed the requested amounts will go into a held state. Defaults are 2000 MB, 10 GB, 1, and 8h, respectively. Note that jobs are charged against the DUNE FermiGrid quota according to the greater of memory/2000 MB and number of CPUs, with fractional values possible. For example, a 3000 MB request is charged 1.5 “slots”, and 4000 MB would be charged 2. You are charged for the amount requested, not what is actually used, so you should not request any more than you actually need (your jobs will also take longer to start the more resources you request). Note also that jobs that run offsite do NOT count against the FermiGrid quota. In general, aim for memory and run time requests that will cover 90-95% of your jobs and use the autorelease feature to deal with the remainder.
• -l (or --lines=) allows you to pass additional arbitrary HTCondor-style classad variables into the job. In this case, we’re specifying exactly what Singularity image we want to use in the job. It will be automatically set up for us when the job starts. Any other valid HTCondor classad is possible. In practice you don’t have to do much beyond the Singularity image. Here, pay particular attention to the quotes and backslashes.
• --append_condor_requirements allows you to pass additional HTCondor-style requirements to your job. This helps ensure that your jobs don’t start on a worker node that might be missing something you need (a corrupt or out of date CVMFS repository, for example). Some checks run at startup for a variety of CVMFS repositories. Here, we check that Singularity invocation is working and that the CVMFS repos we need ( [dune.opensciencegrid.org][dune-openscience-grid-org] and [larsoft.opensciencegrid.org][larsoft-openscience-grid-org] ) are in working order. Optionally you can also place version requirements on CVMFS repos (as we did here as an example), useful in case you want to use software that was published very recently and may not have rolled out everywhere yet.
• -e VAR=VAL will set the environment variable VAR to the value VAL inside the job. You can pass this option multiple times for each variable you want to set. You can also just do -e VAR and that will set VAR inside the job to be whatever value it’s set to in your current environment (make sure it’s actually set though!) One thing to note here as of May 2023 is that these two gfal variables may need to be set as shown to prevent problems with output copyback at a few sites. It is safe to set these variable to the values shown here in all jobs at all sites, since the locations exist in the default container (assuming you’re using that).

Job Output

This particular test writes a file to /pnfs/dune/scratch/users/<username>/job_output_<id number>.log. Verify that the file exists and is non-zero size after the job completes. You can delete it after that; it just prints out some information about the environment.

Manipulating submitted jobs

If you want to remove existing jobs, you can do

jobsub_rm -G dune --jobid=12345678.9@jobsub0N.fnal.gov

to remove all jobs in a given submission (i.e. if you used -N <some number greater than 1>) you can do

jobsub_rm -G dune --jobid=12345678@jobsub0N.fnal.gov

To remove all of your jobs, you can do

jobsub_rm -G dune --user=username

If you want to manipulate only a certain subset of jobs, you can use a HTCondor-style constraint. For example, if I want to remove only held jobs asking for more than say 8 GB of memory that went held because they went over their request, I could do something like

jobsub_rm -G dune --constraint='Owner=="username"&&JobStatus==5&&RequestMemory>=8000&&(HoldReasonCode==34||(HoldReasonCode==26&&HoldReasonSubCode==1))'

To hold jobs, it’s the same procedure as jobsub_rm; just replace that with jobsub_hold. To release a held job (which will restart from the beginning), it’s the same commands as above, only use jobsub_release in place of rm or hold.

if you get tired of typing -G dune all the time, you can set the JOBSUB_GROUP environment variable to dune, and then omit the -G option.

Submit a job using the tarball containing custom code

First off, a very important point: for running analysis jobs, you may not actually need to pass an input tarball, especially if you are just using code from the base release and you don’t actually modify any of it. In that case, it is much more efficient to use everything from the release and refrain from using a tarball. All you need to do is set up any required software from CVMFS (e.g. dunetpc and/or protoduneana), and you are ready to go. If you’re just modifying a fcl file, for example, but no code, it’s actually more efficient to copy just the fcl(s) your changing to the scratch directory within the job, and edit them as part of your job script (copies of a fcl file in the current working directory have priority over others by default).

Sometimes, though, we need to run some custom code that isn’t in a release. We need a way to efficiently get code into jobs without overwhelming our data transfer systems. We have to make a few minor changes to the scripts you made in the previous tutorial section, generate a tarball, and invoke the proper jobsub options to get that into your job. There are many ways of doing this but by far the best is to use the Rapid Code Distribution Service (RCDS), as shown in our example.

If you have finished up the LArSoft follow-up and want to use your own code for this next attempt, feel free to tar it up (you don’t need anything besides the localProducts* and work directories) and use your own tar ball in lieu of the one in this example. You will have to change the last line with your own submit file instead of the pre-made one.

First, we should make a tarball. Here is what we can do (assuming you are starting from /dune/app/users/username/):

cp /dune/app/users/kherner/setupmay2023tutorial-grid.sh /dune/app/users/${USER}/
cp /dune/app/users/kherner/may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof/setup-grid /dune/app/users/${USER}/may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof/setup-grid

Before we continue, let’s examine these files a bit. We will source the first one in our job script, and it will set up the environment for us.

#!/bin/bash                                                                                                                                                                                                      

DIRECTORY=may2023tutorial
# we cannot rely on "whoami" in a grid job. We have no idea what the local username will be.
# Use the GRID_USER environment variable instead (set automatically by jobsub). 
USERNAME=${GRID_USER}

source /cvmfs/dune.opensciencegrid.org/products/dune/setup_dune.sh
export WORKDIR=${_CONDOR_JOB_IWD} # if we use the RCDS the our tarball will be placed in $INPUT_TAR_DIR_LOCAL.
if [ ! -d "$WORKDIR" ]; then
  export WORKDIR=`echo .`
fi

source ${INPUT_TAR_DIR_LOCAL}/${DIRECTORY}/localProducts*/setup-grid 
mrbslp

Now let’s look at the difference between the setup-grid script and the plain setup script. Assuming you are currently in the /dune/app/users/username directory:

diff may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof/setup may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof/setup-grid

< setenv MRB_TOP "/dune/app/users/<username>/may2023tutorial"
< setenv MRB_TOP_BUILD "/dune/app/users/<username>/may2023tutorial"
< setenv MRB_SOURCE "/dune/app/users/<username>/may2023tutorial/srcs"
< setenv MRB_INSTALL "/dune/app/users/<username>/may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof"
---
> setenv MRB_TOP "${INPUT_TAR_DIR_LOCAL}/may2023tutorial"
> setenv MRB_TOP_BUILD "${INPUT_TAR_DIR_LOCAL}/may2023tutorial"
> setenv MRB_SOURCE "${INPUT_TAR_DIR_LOCAL}/may2023tutorial/srcs"
> setenv MRB_INSTALL "${INPUT_TAR_DIR_LOCAL}/may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof"

As you can see, we have switched from the hard-coded directories to directories defined by environment variables; the INPUT_TAR_DIR_LOCAL variable will be set for us (see below). Now, let’s actually create our tar file. Again assuming you are in /dune/app/users/kherner/may2023tutorial/:

tar --exclude '.git' -czf may2023tutorial.tar.gz may2023tutorial/localProducts_larsoft_v09_72_01_e20_prof may2023tutorial/work setupmay2023tutorial-grid.sh

Note how we have excluded the contents of “.git” directories in the various packages, since we don’t need any of that in our jobs. It turns out that the .git directory can sometimes account for a substantial fraction of a package’s size on disk!

Then submit another job (in the following we keep the same submit file as above):

jobsub_submit -G dune --mail_always -N 1 --memory=2500MB --disk=2GB --expected-lifetime=3h --cpu=1 --tar_file_name=dropbox:///dune/app/users/<username>/may2023tutorial.tar.gz --singularity-image /cvmfs/singularity.opensciencegrid.org/fermilab/fnal-wn-sl7:latest --append_condor_requirements='(TARGET.HAS_Singularity==true&&TARGET.HAS_CVMFS_dune_opensciencegrid_org==true&&TARGET.HAS_CVMFS_larsoft_opensciencegrid_org==true&&TARGET.CVMFS_dune_opensciencegrid_org_REVISION>=1105&&TARGET.HAS_CVMFS_fifeuser1_opensciencegrid_org==true&&TARGET.HAS_CVMFS_fifeuser2_opensciencegrid_org==true&&TARGET.HAS_CVMFS_fifeuser3_opensciencegrid_org==true&&TARGET.HAS_CVMFS_fifeuser4_opensciencegrid_org==true)' -e GFAL_PLUGIN_DIR=/usr/lib64/gfal2-plugins -e GFAL_CONFIG_DIR=/etc/gfal2.d file:///dune/app/users/kherner/run_may2023tutorial.sh

You’ll see this is very similar to the previous case, but there are some new options:

• --tar_file_name=dropbox:// automatically copies and untars the given tarball into a directory on the worker node, accessed via the INPUT_TAR_DIR_LOCAL environment variable in the job. The value of INPUT_TAR_DIR_LOCAL is by default $CONDOR_DIR_INPUT/name_of_tar_file_without_extension, so if you have a tar file named e.g. may2023tutorial.tar.gz, it would be $CONDOR_DIR_INPUT/may2023tutorial.
• Notice that the --append_condor_requirements line is longer now, because we also check for the fifeuser[1-4]. opensciencegrid.org CVMFS repositories.

The submission output will look something like this:

Attempting to get token from https://htvaultprod.fnal.gov:8200 ... succeeded
Storing bearer token in /tmp/bt_token_dune_Analysis_11469
Using bearer token located at /tmp/bt_token_dune_Analysis_11469 to authenticate to RCDS
Checking to see if uploaded file is published on RCDS
Could not locate uploaded file on RCDS.  Will retry in 30 seconds.
Could not locate uploaded file on RCDS.  Will retry in 30 seconds.
Could not locate uploaded file on RCDS.  Will retry in 30 seconds.
Found uploaded file on RCDS.
Transferring files to web sandbox...
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_24_224713_9669e535-daf9-496f-8332-c6ec8a4238d9/run_may2023tutorial.sh   [DONE]  after 0s                                                                                                                       
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_24_224713_9669e535-daf9-496f-8332-c6ec8a4238d9/simple.cmd   [DONE]  after 0s                                                                                                                                   
Copying file:///nashome/k/kherner/.cache/jobsub_lite/js_2023_05_24_224713_9669e535-daf9-496f-8332-c6ec8a4238d9/simple.sh   [DONE]  after 0s                                                                                                                                    
Submitting job(s).
1 job(s) submitted to cluster 62007523.

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Use job id 62007523.0@jobsub01.fnal.gov to retrieve output
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

Note that the job submission will pause while it uploads the tarball to RCDS, and then it continues normally.

Now, there’s a very small gotcha when using the RCDS, and that is when your job runs, the files in the unzipped tarball are actually placed in your work area as symlinks from the CVMFS version of the file (which is what you want since the whole point is not to have N different copies of everything). The catch is that if your job script expected to be able to edit one or more of those files within the job, it won’t work because the link is to a read-only area. Fortunately there’s a very simple trick you can do in your script before trying to edit any such files:

cp ${INPUT_TAR_DIR_LOCAL}/file_I_want_to_edit mytmpfile  # do a cp, not mv
rm ${INPUT_TAR_DIR_LOCAL}file_I_want_to_edit # This really just removes the link
mv mytmpfile file_I_want_to_edit # now it's available as an editable regular file.

You certainly don’t want to do this for every file, but for a handful of small text files this is perfectly acceptable and the overall benefits of copying in code via the RCDS far outweigh this small cost. This can get a little complicated when trying to do it for things several directories down, so it’s easiest to have such files in the top level of your tar file.

Monitor your jobs

For all links below, log in with your FNAL Services credentials (FNAL email, not Kerberos password).

• What DUNE is doing overall:
  https://fifemon.fnal.gov/monitor/d/000000053/experiment-batch-details?orgId=1&var-experiment=dune

• What’s going on with only your jobs:
  Remember to change the url with your own username and adjust the time range to cover the region of interest. https://fifemon.fnal.gov/monitor/d/000000116/user-batch-details?orgId=1&var-cluster=fifebatch&var-user=kherner

• Why your jobs are held:
  Remember to choose your username in the upper left.
  https://fifemon.fnal.gov/monitor/d/000000146/why-are-my-jobs-held?orgId=1

View the stdout/stderr of our jobs

Here’s the link for the history page of the example job: link.

Feel free to sub in the link for your own jobs.

Once there, click “View Sandbox files (job logs)”. In general you want the .out and .err files for stdout and stderr. The .cmd file can sometimes be useful to see exactly what got passed in to your job.

Kibana can also provide a lot of information.

You can also download the job logs from the command line with jobsub_fetchlog:

jobsub_fetchlog --jobid=12345678.0@jobsub0N.fnal.gov --unzipdir=some_appropriately_named_directory

That will download them as a tarball and unzip it into the directory specified by the –unzipdir option. Of course replace 12345678.0@jobsub0N.fnal.gov with your own job ID.

Quiz

Download the log of your last submission via jobsub_fetchlog or look it up on the monitoring pages. Then answer the following questions (all should be available in the .out or .err files):

1. On what site did your job run?
2. How much memory did it use?
3. Did it exit abnormally? If so, what was the exit code?

Review of best practices in grid jobs (and a bit on the interactive machines)

• When creating a new workflow or making changes to an existing one, ALWAYS test with a single job first. Then go up to 10, etc. Don’t submit thousands of jobs immediately and expect things to work.
• ALWAYS be sure to prestage your input datasets before launching large sets of jobs. This may become less necesaary in the future as we move to distributed storage locations.
• Use RCDS; do not copy tarballs from places like scratch dCache. There’s a finite amount of transfer bandwidth available from each dCache pool. If you absolutely cannot use RCDS for a given file, it’s better to put it in resilient (but be sure to remove it when you’re done!). The same goes for copying files from within your own job script: if you have a large number of jobs looking for a same file, get it from resilient. Remove the copy when no longer needed. Files in resilient dCache that go unaccessed for 45 days are automatically removed.
• Be careful about placing your output files. **NEVER place more than a few thousand files into any one directory inside dCache. That goes for all type of dCache (scratch, persistent, resilient, etc). Subdirectories also count against the total for these purposes, so don’t put too many subdirectories at any one level.
• AVOID commands like ifdh ls /some/path inside grid jobs unless it is absolutely necessary. That is an expensive operation and can cause a lot of pain for many users, especially when a directory has large number of files in it. Remote listings take much, much longer than the corresponding op on a machine where the directory is mounted via NFS. If you just need to verify a directory exists, there are much better ways than ifdh ls, for example the gfal-stat command. Note also that ifdh cp will now, by default, create an output directory if it does not exist (so be careful that you’ve specified your output string properly).
• Use xrootd when opening files interactively; this is much more stable than simply doing root /pnfs/dune/... (and in general, do NOT do that...)
• NEVER copy job outputs to a directory in resilient dCache. Remember that they are replicated by a factor of 20! Any such files are subject to deletion without warning.
• NEVER do hadd on files in /pnfs areas unless you’re using xrootd. I.e. do NOT do hadd out.root /pnfs/dune/file1 /pnfs/dune/file2 ... This can cause severe performance degradations.
• Generally aim for output file sizes of 1 GB or greater. dCache is really not a fan of small files. You may need to process multiple input files to get to that size (and we generally encourage that anyway!)
• Very short jobs (measured in minutes) are quite inefficient, especially if you have an input tarball. In general you want to run for at least a few hours, and 8-12 is probably ideal (and of course longer jobs are allowed). Again you may need to process multiple input files, depending on what you’re doing, or run multiple workflow stages in the same job. See the POMS section of the tutorial for more details.

Side note: Some people will pass file lists to their jobs instead of using a SAM dataset. We do not recommend that for two reasons: 1) Lists do not protect you from cases where files fall out of cache at the location(s) in your list. When that happens your jobs sit idle waiting for the files to be fetched from tape, which kills your efficiency and blocks resources for others. 2) You miss out on cases where there might be a local copy of the file at the site you’re running on, or at least at closer one to your list. So you may end up unecessarily streaming across oceans, whereas using SAM (or later Rucio) will find you closer, local copies when they exist.

Another important side note: If you are used to using other programs for your work such as project.py (which is NOT officially supported by DUNE or the Fermilab Scientific Computing Division), there is a helpful tool called Project-py that you can use to convert existing xml into POMS configs, so you don’t need to start from scratch! Then you can just switch to using POMS from that point forward. As a reminder, if you use unsupported tools, you are own your own and will receive NO SUPPORT WHATSOEVER. You are still responsible for making sure that your jobs satisfy Fermilab’s policy for job efficiency: https://cd-docdb.fnal.gov/cgi-bin/sso/RetrieveFile?docid=7045&filename=FIFE_User_activity_mitigation_policy_20200625.pdf&version=1

The cost of getting it wrong: a cautionary tale

Earlier in May 2023 there was a fairly significant disruption to FNAL dCache, which resulted in at least five different tickets across four different experiments complaining of poor performance (resulting in jobs going held of exceeding time), timeouts, or other storage-related failures. It’s unclear exactly how many jobs were affcted but it was likely in the many thousands. The root cause was a DUNE user running ifdh ls $OUTDIR 0 to check the existence of a given directory. That command, though it only spits out the directory name, was indeed doing a full internal listing of the contents of $OUTDIR. Normally that’s not the end of the world (see the comment in the best practices section), but this directory had over 100,000 files in it! The user was writing all job outputs into the same directory from what we could tell.

Since the workflow was causing a systemwide disruption we immediately held all of the user’s jobs and blocked new submissions until the workflow was re-engineered. Fortunately dCache performance recovered very quickly after that. The user’s jobs are running again and they are also much more CPU-efficient than they were before the changes.

The bottom line: one single user not following best practices can disrupt the entire system if they get enough jobs running. EVERYONE is responsible for following best practices. Getting it wrong affects not only you, but your collaborators!

A word on the DUNE Global Pool

DUNE has also created a a global glideinWMS pool similar to the CMS Global Pool that is intended to serve as a single point through which multiple job submission systems (e.g. HTCondor schedulers at sites outside of Fermilab) can have access to the same resources. Jobs using the global pool still run in the exactly the same way as those that don’t. We plan to move more and more work over to the global pool in 2023 and priority access to the FermiGrid quota will eventually be given to jobs submitted to the global pool. To switch to the global pool with jobsub, it’s simply a matter of adding --global-pool dune as an option to your submission command. The only practical difference is that your jobs will come back with IDs of the form NNNNNNN.N@dunegpschedd0X.fnal.gov instead of NNNNNNN.N@jobsub0X.fnal.gov. Again, everything else is identical, so feel free to test it out.

Making subsets of sam definitions

Running across very large number of files puts you at risk of system issues. It is often much nicer to run over several smaller subsets. Many official samweb definitions are large data collections defined only by their properties and not really suitable for a single job.

There are two ways of reducing their size.

You can create new dataset definitions; say mydataset has 10000 entries and you want to split it on to groups of 2000:

samweb create-definition $USER-mydataset-part0 “defname:mydataset limit 2000 offset 0”
samweb create-definition $USER-mydataset-part1 “defname:mydataset limit 2000 offset 2000"
samweb create-definition $USER-mydataset-part2 “defname:mydataset limit 2000 offset 4000”
samweb create-definition $USER-mydataset-part3 “defname:mydataset limit 2000 offset 6000"
samweb create-definition $USER-mydataset-part4 “defname:mydataset limit 2000 offset 8000”

Your name needs to be in there, unless it is already in the dataset name, but make certain you don’t miss a few at the end…

Alternatively you can use the syntax with stride 5 offset 0 to take every 5th file:

samweb create-definition $USER-mydataset-part0 “defname:mydataset limit 2000 with stride 5 offset 0”
samweb create-definition $USER-mydataset-part1 “defname:mydataset limit 2000 with stride 5 offset 1"
samweb create-definition $USER-mydataset-part2 “defname:mydataset limit 2000 with stride 5 offset 2”
samweb create-definition $USER-mydataset-part3 “defname:mydataset limit 2000 with stride 5 offset 3"
samweb create-definition $USER-mydataset-part4 “defname:mydataset limit 2000 with stride 5 offset 4”

More on samweb can be found here.

Verify Your Learning:

Question 01

What are the differences in environment between Fermilab worker nodes and those at other sites (assuming the site supports Singularity)?

1. Fermilab workers have additional libraries available.
2. Worker nodes at other sites have additional libraries installed.
3. No difference.

Answer

The correct answer is C - No difference.

Comment:

Question 02

After setting up a new workflow or preparing to run one that has not been exercised in a while, what is an that has not been exercised in a while, what is an appropriate number of test jobs to initially submit?

1. 1.
2. 10.
3. 100.
4. As many as needed.

Answer

The correct answer is A - 1.

Comment:

Question 03

project.py is supported by the Fermilab Scientific Computing Division

1. True.
2. False.

Answer

The correct answer is B - False.

Comment:

Question 04

What is generally the best way to read in a .root file for analysis within a grid job?

1. Open with an xrootd URI (root://).
2. Copy the entire file at the beginning of the job.
3. Both A and B.

Answer

The correct answer is A - Open with an xrootd URI (root://).

Comment:

Question 05

What is the best way to specify your desired operating system and environment in a grid job?

1. Use the --OS option in jobsub.
2. Do not specify any OS, but control it with the SingularityImage classad.
3. Don’t specify anything. The grid does it
4. None of the Above

Answer

The correct answer is B - Do not specify any OS, but control it with the SingularityImage classad.

Comment:

Question 06

What is the best way to copy custom code into a grid job?

1. Use the RCDS (i.e. --tar_file_name=dropbox://foo/bar/) and stage the file in via CVMFS.
2. Copy a tarball to /pnfs/dune/scratch/users/username.
3. Copy a tarball to /pnfs/dune/persistent/users/username.
4. Copy a tarball to /pnfs/dune/resilient/users/username.
5. None of the Above

Answer

The correct answer is A - Use the RCDS (i.e. –tar_file_name=dropbox://foo/bar/) and stage the file in via CVMFS.

Comment:

Further Reading

Some more background material on these topics (including some examples of why certain things are bad) is in these links:

December 2022 jobsub_lite demo and information session

January 2023 additional experiment feedback session on jobsub_lite

Wiki page listing differences between jobsub_lite and legacy jobsub

DUNE Computing Tutorial:Advanced topics and best practices

2021 Intensity Frontier Summer School

The Glidein-based Workflow Management System

Introduction to Docker

Key Points

• When in doubt, ask! Understand that policies and procedures that seem annoying, overly complicated, or unnecessary (especially when compared to running an interactive test) are there to ensure efficient operation and scalability. They are also often the result of someone breaking something in the past, or of simpler approaches not scaling well.

• Send test jobs after creating new workflows or making changes to existing ones. If things don’t work, don’t blindly resubmit and expect things to magically work the next time.

• Only copy what you need in input tar files. In particular, avoid copying log files, .git directories, temporary files, etc. from interactive areas.

• Take care to follow best practices when setting up input and output file locations.

• Always, always, always prestage input datasets. No exceptions.

Submit grid jobs with JustIn

Overview

Teaching: 20 min
Exercises: 0 min

Questions

• How to submit realistic grid jobs with JustIn

Objectives

• Demonstrate use of JustIn for job submission with more complicated setups.

PLEASE USE THE NEW JUSTIN SYSTEM INSTEAD OF POMS

The JustIn Tutorial is currently in docdb at: JustIn Tutorial

The JustIn system is describe in detail at:

JustIn Home

JustIn Docs

Note More documentation coming soon

Key Points

• Always, always, always prestage input datasets. No exceptions.

Expert in the Room Grid and Batch System

Overview

Teaching: 20 min
Exercises: 0 min

Questions

• How to become a grid and batch yoda master?

Objectives

• Learn common job failures and how to avoid them in the future

• Get tips and tricks to debug on your own

Session Video

This session will be captured on video a placed here after the workshop for asynchronous study.

Live Notes

Participants are encouraged to monitor and utilize the Livedoc for May. 2023 to ask questions and learn. For reference, the Livedoc from Jan. 2023 is provided.

Debug Session

This session of Expert in the Room is a Q&A. You bring content! Write on the live doc your current error(s) and experts will provide guidance to participants in a live debug session.
On top of learning why a job failed, we hope it will give you the tools to solve future issues by yourself.

Debugging is an art.

Confusion is the sweat of learning.

Key Points

• Debugging requires a methodical and inquisitive mindset, gained through experience and good bookkeeping (write down how to you solved past issues!)

Closing Remarks

Overview

Teaching: 10 min
Exercises: 0 min

Questions

• Are you fortified with enough information to start your event analysis?

Objectives

• Reflect on the days of learning.

Closing Remarks

Two Days of Training

The instruction in this one day version of the DUNE computing workshop was provided by several experienced physicists and is based on years of experience.

The secure access to Fermilab computing systems and a familiarity with data storage are key components.

Data management and event processing tools were described and modeled.

Art and LArSoft were introduced

We are thankful for the instructor’s hard work, and for the numerous participants who joined.

Survey time!

Please give us feedback on this training by filling out our S U R V E Y. Entries to the survey are anonymous, any impressions, praise, critiques and suggestions are appreciated.

Next Steps

Session recordings have been posted within each lesson after processed.

We invite you to bookmark this training site and to revisit the content regularly.

Point a colleague to the material.

Long Term Support

You made some excellent connections with computing experts and invite your continued dialog.

A DUNE Slack channel (#computing-training-basics) will remain available and we encourage your activity in the dialog.

See also the GitHub FAQ site for DUNE Computing.

Key Points

• The DUNE Computing Consortium has presented this workshop so as to broaden the use of software tools used for analysis.