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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 Facilitator Center for High Throughput Computing STAT 692, November 15, 2013. Topics We’ll Cover Today. Why to Access L arge-Scale C omputing resources

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

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  1. When and How to Use Large-Scale Computing: CHTC and HTCondor Lauren Michael, Research Computing Facilitator Center for High Throughput Computing STAT 692, November 15, 2013

  2. Topics We’ll Cover Today • 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

  3. When should you use outside computing resources? • your computing work won’t run at all on your computer(s) (lack sufficient RAM, disk, etc.) • your computing work will take too long on your own computer(s) • you would like to off-load certain processes in favor of running others on your computer(s)

  4. CHTC Services Center for High Throughput Computing, est. 2006 • Large-scale, campus-shared computing systems • high-throughput computing (HTC) grid and high-performance computing (HPC) cluster • all standard services provided free-of-charge • automatic access to the national Open Science Grid (OSG) • hardware buy-in options for priority access • information about other computing resources • Support for using our systems • consultation services, training, and proposal assistance • solutions for numerous software (including Python, Matlab, R)

  5. HTCondor: CHTC’s R&D Arm • R&D for HTCondor and other HTC software • Services provided to the campus community • HTC Software • HTCondor: manage your compute cluster • DAGMan: manage computing workflows • Bosco: submit locally, run globally • Software Engineering Expertise & Consulting • CHTC-operated Build-and-Test Lab (BaTLab) • Software Security Consulting Your Problems become Our Research!

  6. http://chtc.cs.wisc.edu Researchers who use the CHTC are located all over campus (red buildings)

  7. CHTC Staff Director, MironLivnymiron@cs.wisc.edu (also OSG Technical Director and WIDs CTO) Campus Support:chtc@cs.wisc.edu 2+ Research Computing Facilitators • Lauren Michael (lead) lmichael@wisc.edu 3 Systems Administrators +4-8 Part-time Students HTCondorDevelopment Team OSG Software Team

  8. HTC versus HPC • high-throughput computing (HTC) • many independent processes that can run on 1 or few processors (“cores” or “threads”) on the same computer • mostly standard programming methods • best accelerated by: access to as many cores as possible • high-performance computing (HPC) • sharing the workload of interdependent processes over multiple cores to reduce overall compute time • OpenMP and MPI programming methods, or multi-thread • requires: access to many servers of cores within the same tightly-networked cluster; access to shared files

  9. “parallel” is confusing • essentially means: spread computing work out over multiple processors • Use of the words “parallel” and “parallelize” can apply to HTCorHPC when referring to programs • It’s important to be clear!

  10. Topics We’ll Cover Today • 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

  11. What is HTCondor? • match-maker of computing work and computers • “job scheduler” • matches are made based upon necessary RAM, CPUs, disk space, etc., as requested by the user • 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

  12. How HTCondor Works Central Manager (of the pool) Job ClassAd Machine ClassAd Submit Node(s) (where jobs are submitted) input input Execute Node(s) (where jobs run) output Queue job1.1 user1 job1.2 user1 job2.1 user2

  13. Submit nodes available to YOU

  14. Basic HTCondor Submission • Prepare programs and files • Write submit file(s) • Submit jobs to the queue • Monitor the jobs • (Remove bad jobs)

  15. Preparing Programs and Files • Make programs portable • compile code to a simple binary • statically-link code dependencies • consider CHTC’s tools for packaging Matlab, Python, and R • Consider using a shell script (or other “wrapper”) to run multiple commands for you • create a local install of software • set environment variables • then, run your code • Stage all files on a submit node

  16. HTC Components 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!)

  17. Basic HTCondor Submit File basicjobs arevanilla universe # This is a comment universe = vanilla output = process.out error = process.err log = process.log executable = cosmos arguments = cosmos.in 4 should_transfer_files = YES transfer_input_files = cosmos.in when_to_transfer_output = ON_EXIT request_memory = 100 request_disk = 100000 request_cpus = 1 queue outputand errorare where system output and error will go logis where HTCondor stores info about how your job ran executable is your single program or a shell script The program will be run as: ./cosmos cosmos.in 4 memory in MB and disk in KB queuewith no number after it will submit only one job

  18. Basic HTCondor Submit File # This is a comment universe = vanilla output = process.out error = process.err log = process.log executable = cosmos arguments = cosmos.in 4 should_transfer_files = YES transfer_input_files = cosmos.in when_to_transfer_output = ON_EXIT request_memory = 100 request_disk = 100000 request_cpus = 1 queue Initial File Organization In foldertest/ cosmos cosmos.in submit.txt

  19. HTCondor Multi-Job Submit File # This is a comment universe = vanilla output = $(Process).out error = $(Process).err log = $(Cluster).log executable = cosmos arguments = cosmos_$(Process).in should_transfer_files = YES transfer_input_files = cosmos_$(Process).in when_to_transfer_output = ON_EXIT request_memory = 100 request_disk = 100000 request_cpus = 1 queue 3 test/ cosmos cosmos_0.in cosmos_1.in cosmos_2.in submit.txt

  20. HTCondor Multi-Folder Submit File # This is a comment universe = vanilla InitialDir =$(Process) output = $(Process).out error = $(Process).err log = /home/user/test/$(Cluster).log executable = /home/user/test/cosmos arguments = cosmos.in should_transfer_files = YES transfer_input_files = cosmos.in when_to_transfer_output = ON_EXIT request_memory = 100 request_disk = 100000 request_cpus = 1 queue 3 test/ cosmos cosmos.in submit.txt 0/ cosmos.in 1/ cosmos.in 2/ cosmos.in

  21. Submitting Jobs [lmichael@simon test]$ condor_submitsubmit.txt Submitting job(s)... 3job(s) submitted to cluster 29747. [lmichael@simon test]$

  22. Checking the Queue [lmichael@simon test]$ condor_qlmichael • -- 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.in • 29747.1 lmichael 2/15 09:06 0+00:00:00 I 0 9.8 cosmos cosmos.in • 29747.2 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@simontest]$ • View all user jobs in the queue: condor_q

  23. Log Files 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

  24. Removing Jobs • Remove a single job: condor_rm 29747.0 • Remove all jobs of a cluster: condor_rm 29747 • Remove all of your jobs: condor_rmlmichael

  25. Topics We’ll Cover Today • 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

  26. Maximizing Throughput • The Philosophy of HTC • The Art of HTC • Other Best-Practices

  27. The Philosophy of HTC • break up your work into many ‘smaller’ jobs • single CPU, short run times, small input/output data • run on as many processors as possible • single CPU and low RAM needs • take everything with you; make programs portable • 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)

  28. Success Stories • 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!

  29. The Art of HTC carrying out the philosophy, well • Tuning job requests for memory and disk • Matching run times to the maximum number of available processors • Automation

  30. Tuning Job Resource Requests Problem: Don’t know what your job needs? • If you don’t ask for enough memory and disk: • 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: • Your jobs won’t match to as many available “slots” as they could

  31. Tuning Job Resource Requests Solution: Testing is Key!!! • Run just a few jobs at first to determine memory and disk needs from log files • If your first request is not enough, HTCondor will retry the jobs and request more until they finish. • It’s okay to request a lot (1 GB each) for a few tests. • Change the “request” lines to a better value • Submit a large batch

  32. Time-Matching (submit file additions)

  33. Time-Tuning: Batching • Problem: Jobs less than 5 minutes are bad for overall throughput • more time spent on matching and data transfers than on your job’s processes • 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 job • avoids transfer of intermediate files between sequential, related processes • debugging can be a bit trickier

  34. Time-Tuning: Checkpointing • The best way to run longer jobs without losing progress to eviction. Two Ways: • Compile your code with condor_compile and use the “standard” universe within HTCondor • Implement self-checkpointing *Consult HTCondor’s online manual or contact the CHTC for help

  35. Automate Tasks • Use $(Process) • Shell scripts to run multiple tasks within the same job • including environment preparation • Hardcode arguments, calculate them (random number generation), or use parameter files/tables • Use HTCondor’sDAGMan feature • “directed acyclic graph” • create complex workflows of dependent jobs, and submit them all at once • additional helpful features: success checks and more

  36. Non-Throughput Considerations Remember that you are sharing with others • “Be Kind to Your Submit Node” • 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 • avoid running multiple CPU-intensive executables • Test all new batches, and scale up gradually • 3 jobs, then 100s, then 1000s, then

  37. Topics We’ll Cover Today • 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

  38. Running R on HTC Resources:The BestWay • 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

  39. 1. Build R Code with chtc_buildRlibs • 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)

  40. 2. Download the “ChtcRun” Package • download ChtcRun.tar.gz, according to the guide (wget) • un-tar it: tar xzfChtcRun.tar.gz • View ChtcRuncontents: process.template (submit file template) mkdag (script that will ‘create’ jobs based upon your staged data) Rin/ (example data staging folder)

  41. 3. Prepare data and process.template • 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: • request_memory and request_disk, if you know • +WantFlocking = true OR +WantGlidein = true

  42. 4. Run mkdag and submit jobs • In ChtcRun, execute the mkdag script • (Examples at the top of “./mkdag --help”) ./mkdag --data=Rin–outputdir=Rout \ --cmdtorun=soartest.R--type=R \ --version=R-2.10.1 --pattern=meanx • “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_dagmydag.dag

  43. 5. Monitor Job Completion • 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.logprocess.out,err ChtcWrapper1.out 2/ process.logprocess.out,err ChtcWrapper2.out …/ After testing a small number of jobs, submit many! (up to many 10,000s; # submitted is throttled for you)

  44. What Next? • Use a Stat server to submit shorter jobs to the CS pool. • Obtain access to simon.stat.wisc.edu from Mike Camilleri (mikec@stat.wisc.edu), and submit longer jobs to the CHTC Pool. • Meet with the CHTC to submit jobs to the entire UW Grid and to the national Open Science Grid. • chtc.cs.wisc.edu, click “Get Started” User support for HTCondorusers at UW: chtc@cs.wisc.edu

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