When and How to Use Large-Scale Computing: CHTC and HTCondor

When and How to Use Large-Scale Computing: CHTC and HTCondor

Lauren Michael, Research Computing FacilitatorCenter for High Throughput Computing

STAT 692, November 15, 2013

› Why to Access Large-Scale Computing resources› CHTC Services and Campus-Shared Computing› What is High-Throughput Computing (HTC)?› What is HTCondor and How Do You Use It?› Maximizing Computational Throughput› How to Run R on Campus-Shared Resources

Topics We’ll Cover Today

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1. your computing work won’t run at all on your computer(s) (lack sufficient RAM, disk, etc.)

2. your computing work will take too long on your own computer(s)

3. you would like to off-load certain processes in favor of running others on your computer(s)

When should you use outside computing resources?

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Center for High Throughput Computing, est. 2006› Large-scale, campus-shared computing systems

h high-throughput computing (HTC) grid and high-performance computing (HPC) cluster

h all standard services provided free-of-chargeh automatic access to the national Open Science Grid (OSG)h hardware buy-in options for priority access h information about other computing resources

› Support for using our systemsh consultation services, training, and proposal assistanceh solutions for numerous software (including Python, Matlab, R)

CHTC Services

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HTCondor: CHTC’s R&D Arm› R&D for HTCondor and other HTC software› Services provided to the campus community

h HTC Software• HTCondor: manage your compute cluster• DAGMan: manage computing workflows• Bosco: submit locally, run globally

h Software Engineering Expertise & Consulting• CHTC-operated Build-and-Test Lab (BaTLab)

h Software Security Consulting

Your Problems become Our Research!

Jul’10-Jun’11

Jul’11-Jun’12

Jul’12-Jun’13 Quick Facts

45 70 97 Million Hours Served

54 106 120 Research Projects

35 52 52 Departments

10 13 15 Off-Campus Projects

Researchers who use the CHTC are located all over campus (red buildings)

http://chtc.cs.wisc.edu

Director, Miron Livny miron@cs.wisc.edu(also OSG Technical Director and WIDs CTO)

Campus Support: chtc@cs.wisc.edu2+ Research Computing Facilitators› Lauren Michael (lead)

lmichael@wisc.edu3 Systems Administrators+4-8 Part-time Students

HTCondor Development TeamOSG Software Team

CHTC Staff

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› high-throughput computing (HTC)h many independent processes that can run on 1 or few

processors (“cores” or “threads”) on the same computerh mostly standard programming methodsh best accelerated by: access to as many cores as possible

› high-performance computing (HPC)h sharing the workload of interdependent processes over

multiple cores to reduce overall compute timeh OpenMP and MPI programming methods, or multi-threadh requires: access to many servers of cores within the same

tightly-networked cluster; access to shared files

HTC versus HPC

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› essentially means: spread computing work out over multiple processors

› Use of the words “parallel” and “parallelize” can apply to HTC or HPC when referring to programs

› It’s important to be clear!

“parallel” is confusing

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› Why to Access Large-Scale Computing resources› CHTC Services and Campus-Shared Computing› What is High-Throughput Computing (HTC)?› What is HTCondor and How Do You Use It?› Maximizing Computational Throughput› How to Run R on Campus-Shared Resources

Topics We’ll Cover Today

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› match-maker of computing work and computers› “job scheduler”

h matches are made based upon necessary RAM, CPUs, disk space, etc., as requested by the user

h jobs re-run if interrupted

› works beyond “clusters” to coordinate distributed computers for maximum throughput

› coordinates data transfers between users and distributed computers

› can coordinate servers, desktops, and laptops

What is HTCondor?

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Queuejob1.1 user1job1.2 user1job2.1 user2

Submit Node(s)(where jobs are

submitted)

input

How HTCondor Works

Central Manager(of the pool)

Execute Node(s)(where jobs run)

Machine

ClassAdJo

b Clas

sAd

output

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input

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Submit host CS Pool CHTC Pool Campus Grid Open Science Grid

Stat dept servers default

simon.stat.wisc.edu default

CHTC submit nodes default flocking “glidein”

Submit nodes available to YOU

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› Prepare programs and files

› Write submit file(s)› Submit jobs to the queue› Monitor the jobs› (Remove bad jobs)

Basic HTCondor Submission

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› Make programs portableh compile code to a simple binaryh statically-link code dependenciesh consider CHTC’s tools for packaging Matlab, Python, and R

› Consider using a shell script (or other “wrapper”) to run multiple commands for youh create a local install of softwareh set environment variablesh then, run your code

› Stage all files on a submit node

Preparing Programs and Files

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1. Cut up computing work into many independent pieces

(CHTC can consult)

2. Make programs portable, minimize dependencies

(CHTC can consult, or may have prepared solutions)

3. Learn how to submit jobs(CHTC can help you a lot!)

4. Maximize your overall throughput on available computational resources

(CHTC can help you a lot!)

HTC Components

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# This is a commentuniverse = vanillaoutput = process.outerror = process.errlog = process.logexecutable = cosmosarguments = cosmos.in 4should_transfer_files = YEStransfer_input_files = cosmos.inwhen_to_transfer_output = ON_EXITrequest_memory = 100request_disk = 100000request_cpus = 1queue

Basic HTCondor Submit File

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basic jobs are vanilla universe

executable is your single

program or a shell script

log is where HTCondor stores info about how

your job ran

output and error are where

system output and error will go

The program will be run as: ./cosmos cosmos.in 4

queue with no number after it will submit only

one job

memory in MB and disk in KB

# This is a commentuniverse = vanillaoutput = process.outerror = process.errlog = process.logexecutable = cosmosarguments = cosmos.in 4should_transfer_files = YEStransfer_input_files = cosmos.inwhen_to_transfer_output = ON_EXITrequest_memory = 100request_disk = 100000request_cpus = 1queue

Basic HTCondor Submit File

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Initial File Organization

In folder test/ cosmos cosmos.in submit.txt

# This is a commentuniverse = vanillaoutput = $(Process).outerror = $(Process).errlog = $(Cluster).logexecutable = cosmosarguments = cosmos_$(Process).inshould_transfer_files = YEStransfer_input_files = cosmos_$(Process).inwhen_to_transfer_output = ON_EXITrequest_memory = 100request_disk = 100000request_cpus = 1queue 3

HTCondor Multi-Job Submit File

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test/ cosmos cosmos_0.in cosmos_1.in cosmos_2.in submit.txt

# This is a commentuniverse = vanillaInitialDir = $(Process)output = $(Process).outerror = $(Process).errlog = /home/user/test/$(Cluster).logexecutable = /home/user/test/cosmosarguments = cosmos.inshould_transfer_files = YEStransfer_input_files = cosmos.inwhen_to_transfer_output = ON_EXITrequest_memory = 100request_disk = 100000request_cpus = 1queue 3

HTCondor Multi-Folder Submit File

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test/ cosmos cosmos.in submit.txt 0/ cosmos.in 1/ cosmos.in 2/ cosmos.in

Submitting Jobs

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[lmichael@simon test]$ condor_submit submit.txtSubmitting job(s)...3 job(s) submitted to cluster 29747.[lmichael@simon test]$

Checking the Queue

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[lmichael@simon test]$ condor_q lmichael

-- Submitter: simon.stat.wisc.edu : <144.92.142.159:9620?sock=3678_5c57_3> : simon.stat.wisc.edu

ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 29747.0 lmichael 2/15 09:06 0+00:01:34 R 0 9.8 cosmos cosmos.in29747.1 lmichael 2/15 09:06 0+00:00:00 I 0 9.8 cosmos cosmos.in29747.2 lmichael 2/15 09:06 0+00:00:00 I 0 9.8 cosmos cosmos.in

3 jobs; 0 completed, 0 removed, 2 idle, 1 running, 0 held, 0 suspended[lmichael@simon test]$

View all user jobs in the queue: condor_q

Log Files

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000 (29747.001.000) 02/15 09:29:17 Job submitted from host: <144.92.142.159:9620?sock=3678_5c57_3>...001 (29747.001.000) 02/15 09:33:59 Job executing on host: <144.92.142.153:9618?sock=17172_f1f3_3>...005 (29747.001.000) 02/15 09:39:01 Job terminated. (1) Normal termination (return value 0) Usr 0 00:00:00, Sys 0 00:00:00 - Run Remote Usage Usr 0 00:00:00, Sys 0 00:00:00 - Run Local Usage Usr 0 00:00:00, Sys 0 00:00:00 - Total Remote Usage Usr 0 00:00:00, Sys 0 00:00:00 - Total Local Usage 0 - Run Bytes Sent By Job 0 - Run Bytes Received By Job 0 - Total Bytes Sent By Job 0 - Total Bytes Received By Job Partitionable Resources : Usage Request Allocated Cpus : 1 1 Disk (KB) : 225624 100000 645674 Memory (MB) : 85 1000 1024

› Remove a single job: condor_rm 29747.0

› Remove all jobs of a cluster: condor_rm 29747

› Remove all of your jobs: condor_rm lmichael

Removing Jobs

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› Why to Access Large-Scale Computing resources› CHTC Services and Campus-Shared Computing› What is High-Throughput Computing (HTC)?› What is HTCondor and How Do You Use It?› Maximizing Computational Throughput› How to Run R on Campus-Shared Resources

Topics We’ll Cover Today

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› The Philosophy of HTC› The Art of HTC› Other Best-Practices

Maximizing Throughput

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› break up your work into many ‘smaller’ jobsh single CPU, short run times, small input/output data

› run on as many processors as possibleh single CPU and low RAM needsh take everything with you; make programs portableh use the “right” submit node for the right “resources”

› automate as much as you can› (share your processors with others to increase

everyone’s throughput)

The Philosophy of HTC

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› Edgar Spalding: studies effect of gene on plant growth outcomes

› GeoDeepDive Project: extracts and comprises “dark data” from PDFs of publications in Geosciences

We want HTC to revolutionize your research!

Success Stories

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carrying out the philosophy, well

› Tuning job requests for memory and disk› Matching run times to the maximum number of

available processors› Automation

The Art of HTC

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Problem: Don’t know what your job needs?

› If you don’t ask for enough memory and disk:h Your jobs will be kicked off for going over, and will have

to be retried (though, HTCondor will automatically request more for you)

› If you ask for too much:h Your jobs won’t match to as many available “slots” as

they could

Tuning Job Resource Requests

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Solution: Testing is Key!!!

1. Run just a few jobs at first to determine memory and disk needs from log filesh If your first request is not enough, HTCondor will retry the

jobs and request more until they finish.h It’s okay to request a lot (1 GB each) for a few tests.

2. Change the “request” lines to a better value3. Submit a large batch

Tuning Job Resource Requests

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Submit host CS Pool

(4 hrs?)

CHTC Pool<24 hrs

(up to 72)*

Campus Grid<4 hrs

Open Science Grid<2 hrs

Stat dept servers default

simon.stat.wisc.edu default

CHTC submit nodes default +WantFlocking

= true +WantGlidein = true

Time-Matching (submit file additions)

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› Problem: Jobs less than 5 minutes are bad for overall throughputh more time spent on matching and data transfers than

on your job’s processesh Ideal time is between 5 minutes and 2 hours (OSG)

› Solution: Use a shell script (or other method) to run multiple processes within a single jobh avoids transfer of intermediate files between

sequential, related processesh debugging can be a bit trickier

Time-Tuning: Batching

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› The best way to run longer jobs without losing progress to eviction.

Two Ways:1. Compile your code with condor_compile and use

the “standard” universe within HTCondor2. Implement self-checkpointing

*Consult HTCondor’s online manual or contact the CHTC for help

Time-Tuning: Checkpointing

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› Use $(Process)› Shell scripts to run multiple tasks within the same job

h including environment preparation› Hardcode arguments, calculate them (random number

generation), or use parameter files/tables

› Use HTCondor’s DAGMan featureh “directed acyclic graph”h create complex workflows of dependent jobs, and submit

them all at onceh additional helpful features: success checks and more

Automate Tasks

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Remember that you are sharing with others› “Be Kind to Your Submit Node”

h avoid transfers of large files through the submit node (large: >10GB per batch; ~10 MB/job x 1000+ jobs)• transfer files from another server as part of your job

(wget and curl)• compress where appropriate; delete unnecessary files • remember: “new” files are copied back to submit nodes

h avoid running multiple CPU-intensive executables › Test all new batches, and scale up gradually

h 3 jobs, then 100s, then 1000s, then

Non-Throughput Considerations

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› Why to Access Large-Scale Computing resources› CHTC Services and Campus-Shared Computing› What is High-Throughput Computing (HTC)?› What is HTCondor and How Do You Use It?› Maximizing Computational Throughput› How to Run R on Campus-Shared Resources

Topics We’ll Cover Today

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› Problem: R programs don’t easily compile to a binary

› Solution: Take R with your job!

CHTC has tools just for R (and Python, and Matlab)› Installed on CS/Stat submit nodes, simon, and CHTC

submit nodes

Running R on HTC Resources:The Best Way

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› Copy your R code and any R library tar.gz files to the submit node

› Run the following command:chtc_buildRlibs --rversion=sl5-R-2.10.1 \

--<library1>.tar.gz,<library2>.tar.gz› R versions supported: 2.10.1, 2.13.1, 2.15.1

(use the closest version below yours)

› Get back sl5-RLIBS.tar.gz and sl6-RLIBS.tar.gz(you’ll use these in the next step)

1. Build R Code with chtc_buildRlibs

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› download ChtcRun.tar.gz, according to the guide (wget)› un-tar it: tar xzf ChtcRun.tar.gz

› View ChtcRun contents:process.template (submit file template)mkdag (script that will ‘create’ jobs based

upon your staged data)Rin/ (example data staging folder)

2. Download the “ChtcRun” Package

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› Stage data as such:ChtcRun/ data/

1/ input.in <specific_files> 2/ input.in <specific_files> job3/ input.in <specific_files> test4/ input.in <specific_files>shared/ <RLIBS.tar.gz> <program>.R <shared_files>

› Modify process.template with respect to:h request_memory and request_disk, if you knowh +WantFlocking = true OR +WantGlidein = true

3. Prepare data and process.template

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› In ChtcRun, execute the mkdag scripth (Examples at the top of “./mkdag --help”)

./mkdag --data=Rin –outputdir=Rout \--cmdtorun=soartest.R --type=R \--version=R-2.10.1 --pattern=meanx

h “pattern” indicates a portion of a filename that you expect to be created by successful completion of any single job

› A successful mkdag run will instruct you to navigate to the ‘outputdir’, and submit the jobs as a single DAG:condor_submit_dag mydag.dag

4. Run mkdag and submit jobs

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› Check jobs in the queue as they’re gradually added and completed (condor_q)

› Check other files in your ‘outputdir’:

Rout/ mydag.dag.dagman.out (updated table of job stats) 1/ process.log process.out,err ChtcWrapper1.out 2/ process.log process.out,err ChtcWrapper2.out …/

After testing a small number of jobs, submit many!(up to many 10,000s; # submitted is throttled for you)

5. Monitor Job Completion

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1. Use a Stat server to submit shorter jobs to the CS pool.2. Obtain access to simon.stat.wisc.edu from Mike

Camilleri (mikec@stat.wisc.edu), and submit longer jobs to the CHTC Pool.

3. Meet with the CHTC to submit jobs to the entire UW Grid and to the national Open Science Grid.h chtc.cs.wisc.edu, click “Get Started”

User support for HTCondor users at UW:chtc@cs.wisc.edu

What Next?

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1. Use a Stat server to submit shorter jobs to the CS pool.2. Obtain access to simon.stat.wisc.edu from Mike

Camilleri (mikec@stat.wisc.edu), and submit longer jobs to the CHTC Pool.

3. Meet with the CHTC to submit jobs to the entire UW Grid and to the national Open Science Grid.h chtc.cs.wisc.edu, click “Get Started”

User support for HTCondor users at UW:chtc@cs.wisc.edu

What Next?

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