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High Performance Computing Workshop HPC 101 Dr. Charles J Antonelli LSAIT ARS February, 2014.

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Presentation on theme: "High Performance Computing Workshop HPC 101 Dr. Charles J Antonelli LSAIT ARS February, 2014."— Presentation transcript:

1 High Performance Computing Workshop HPC 101 Dr. Charles J Antonelli LSAIT ARS February, 2014

2 Credits Contributors: Brock Palen (CAEN HPC) Jeremy Hallum (MSIS) Tony Markel (MSIS) Bennet Fauber (CAEN HPC) Mark Montague (LSAIT ARS) Nancy Herlocher (LSAIT ARS) LSAIT ARS CAEN HPC 2/14cja 20142

3 Roadmap High Performance Computing Flux Architecture Flux Mechanics Flux Batch Operations Introduction to Scheduling 2/14cja 20143

4 High Performance Computing 2/14 cja

5 Cluster HPC A computing cluster a number of computing nodes connected together via special hardware and software that together can solve large problems. A cluster is much less expensive than a single supercomputer (e.g., a mainframe) Using clusters effectively requires support in scientific software applications (e.g., Matlab's Parallel Toolbox, or R's Snow library), or custom code 2/145cja 2014

6 Programming Models Two basic parallel programming models Message-passing The application consists of several processes running on different nodes and communicating with each other over the network Used when the data are too large to fit on a single node, and simple synchronization is adequate “Coarse parallelism” Implemented using MPI (Message Passing Interface) libraries Multi-threaded The application consists of a single process containing several parallel threads that communicate with each other using synchronization primitives Used when the data can fit into a single process, and the communications overhead of the message-passing model is intolerable “Fine-grained parallelism” or “shared-memory parallelism” Implemented using OpenMP (Open Multi-Processing) compilers and libraries Both 2/14cja 20146

7 Amdahl’s Law 2/14cja 20147

8 Flux Architecture 2/14cja 20148

9 Flux Flux is a university-wide shared computational discovery / high-performance computing service. Provided by Advanced Research Computing at U-M Operated by CAEN HPC Procurement, licensing, billing by U-M ITS Interdisciplinary since /149 cja 2014

10 The Flux cluster … 2/14cja

11 A Flux node Intel cores GB RAM Local disk EthernetInfiniBand 2/14cja

12 A Large Memory Flux node 1 TB RAM Local disk EthernetInfiniBand 2/14cja Intel cores

13 Coming soon: A Flux GPU node 16 Intel cores 64 GB RAM Local disk 2/ GPUs Each GPU contains 2,688 GPU cores cja 2014

14 Flux software Licensed and open software: Abacus, BLAST, BWA, bowtie, ANSYS, Java, Mason, Mathematica, Matlab, R, RSEM, STATA SE, … See C, C++, Fortran compilers: Intel (default), PGI, GNU toolchains You can choose software using the module command 2/1414cja 2014

15 Flux network All Flux nodes are interconnected via Infiniband and a campus-wide private Ethernet network The Flux login nodes are also connected to the campus backbone network The Flux data transfer node is connected over a 10 Gbps connection to the campus backbone network This means The Flux login nodes can access the Internet The Flux compute nodes cannot If Infiniband is not available for a compute node, code on that node will fall back to Ethernet communications 2/14cja

16 Flux data Lustre filesystem mounted on /scratch on all login, compute, and transfer nodes 640 TB of short-term storage for batch jobs Large, fast, short-term NFS filesystems mounted on /home and /home2 on all nodes 80 GB of storage per user for development & testing Small, slow, long-term 2/14cja

17 Flux data Flux does not provide large, long-term storage Alternatives: Value Storage (NFS) $20.84 / TB / month (replicated, no backups) $10.42 / TB / month (non-replicated, no backups) LSA Large Scale Research Storage 2 TB free to researchers (replicated, no backups) Faculty members, lecturers, postdocs, GSI/GSRA Additional storage $30 / TB / year (replicated, no backups) Departmental server CAEN can mount your storage on the login nodes 2/14cja

18 Copying data Three ways to copy data to/from Flux From Linux or Mac OS X, use scp: scp localfile scp localfile scp -r localdir From Windows, use WinSCP U-M Blue Disc Use Globus Connect 2/1418cja 2014

19 Globus Connect Features High-speed data transfer, much faster than SCP or SFTP Reliable & persistent Minimal client software: Mac OS X, Linux, Windows GridFTP Endpoints Gateways through which data flow Exist for XSEDE, OSG, … UMich: umich#flux, umich#nyx Add your own client endpoint! Add your own server endpoint: contact More information 2/14cja

20 Flux Mechanics 2/14cja

21 Using Flux Three basic requirements to use Flux: 1.A Flux account 2.A Flux allocation 3.An MToken (or a Software Token) 2/14cja

22 Using Flux 1.A Flux account Allows login to the Flux login nodes Develop, compile, and test code Available to members of U-M community, free Get an account by visiting on on on 2/14cja

23 Using Flux 2.A Flux allocation Allows you to run jobs on the compute nodes Some units cost-share Flux rates Regular Flux: $11.72/core/month LSA, Engineering, Medical School $6.60/month Large Memory Flux: $23.82/core/month LSA, Engineering, Medical School $13.30/month GPU Flux: $107.10/2 CPU cores and 1 GPU/month LSA, Engineering, Medical School $60/month Flux Operating Environment: $113.25/node/month LSA, Engineering, Medical School $63.50/month Flux pricing at Rackham grants are available for graduate students Details at To inquire about Flux allocations please 2/14cja

24 Using Flux 3.An MToken (or a Software Token) Required for access to the login nodes Improves cluster security by requiring a second means of proving your identity You can use either an MToken or an application for your mobile device (called a Software Token) for this Information on obtaining and using these tokens at 2/14cja

25 Logging in to Flux ssh MToken (or Software Token) required You will be randomly connected a Flux login node Currently flux-login1 or flux-login2 Firewalls restrict access to flux-login. To connect successfully, either Physically connect your ssh client platform to the U-M campus wired or MWireless network, or Use VPN software on your client platform, or Use ssh to login to an ITS login node (, and ssh to flux-login from there 2/14cja

26 Modules The module command allows you to specify what versions of software you want to use module list -- Show loaded modules module load name -- Load module name for use module avail -- Show all available modules module avail name -- Show versions of module name* module unload name -- Unload module name module-- List all options Enter these commands at any time during your session A configuration file allows default module commands to be executed at login Put module commands in file ~/privatemodules/default Don’t put module commands in your.bashrc /.bash_profile 2/14cja

27 Flux environment The Flux login nodes have the standard GNU/Linux toolkit: make, autoconf, awk, sed, perl, python, java, emacs, vi, nano, … Watch out for source code or data files written on non-Linux systems Use these tools to analyze and convert source files to Linux format file dos2unix 2/14cja

28 Lab 1 Task: Invoke R interactively on the login node module load R module list Rq() Please run only very small computations on the Flux login nodes, e.g., for testing 2/14cja

29 Lab 2 Task: Run R in batch mode module load R Copy sample code to your login directory cd cp ~cja/hpc-sample-code.tar.gz. tar -zxvf hpc-sample-code.tar.gz cd./hpc-sample-code Examine Rbatch.pbs and Rbatch.R Edit Rbatch.pbs with your favorite Linux editor Change #PBS -M address to your own 2/14cja

30 Lab 2 Task: Run R in batch mode Submit your job to Flux qsub Rbatch.pbs Watch the progress of your job qstat -u uniqname where uniqname is your own uniqname When complete, look at the job’s output less Rbatch.out Copy your results to your local workstation (change uniqname to your own uniqname) scp sample-code/Rbatch.out Rbatch.out 2/14cja

31 Lab 3 Task: Use the multicore package The multicore package allows you to use multiple cores on the same node module load R cd ~/sample-code Examine Rmulti.pbs and Rmulti.R Edit Rmulti.pbs with your favorite Linux editor Change #PBS -M address to your own 2/14cja

32 Lab 3 Task: Use the multicore package Submit your job to Flux qsub Rmulti.pbs Watch the progress of your job qstat -u uniqname where uniqname is your own uniqname When complete, look at the job’s output less Rmulti.out Copy your results to your local workstation (change uniqname to your own uniqname) scp code/Rmulti.out Rmulti.out 2/14cja

33 Compiling Code Assuming default module settings Use mpicc/mpiCC/mpif90 for MPI code Use icc/icpc/ifort with -mp for OpenMP code Serial code, Fortran 90: ifort -O3 -ipo -no-prec-div –xHost -o prog prog.f90 Serial code, C: icc -O3 -ipo -no-prec-div –xHost –o prog prog.c MPI parallel code: mpicc -O3 -ipo -no-prec-div –xHost -o prog prog.c mpirun -np 2./prog 2/14cja

34 Lab 4 Task: compile and execute simple programs on the Flux login node Copy sample code to your login directory: cd cp ~brockp/cac-intro-code.tar.gz. tar -xvzf cac-intro-code.tar.gz cd./cac-intro-code Examine, compile & execute helloworld.f90: ifort -O3 -ipo -no-prec-div -xHost -o f90hello helloworld.f90./f90hello Examine, compile & execute helloworld.c: icc -O3 -ipo -no-prec-div -xHost -o chello helloworld.c./chello Examine, compile & execute MPI parallel code: mpicc -O3 -ipo -no-prec-div -xHost -o c_ex01 c_ex01.c mpirun -np 2./c_ex01 2/14cja

35 Makefiles The make command automates your code compilation process Uses a makefile to specify dependencies between source and object files The sample directory contains a sample makefile To compile c_ex01 : make c_ex01 To compile all programs in the directory make To remove all compiled programs make clean To make all the programs using 8 compiles in parallel make -j8 2/14cja

36 Flux Batch Operations 2/14cja

37 Portable Batch System All production runs are run on the compute nodes using the Portable Batch System (PBS) PBS manages all aspects of cluster job execution except job scheduling Flux uses the Torque implementation of PBS Flux uses the Moab scheduler for job scheduling Torque and Moab work together to control access to the compute nodes PBS puts jobs into queues Flux has a single queue, named flux 2/14cja

38 Cluster workflow You create a batch script and submit it to PBS PBS schedules your job, and it enters the flux queue When its turn arrives, your job will execute the batch script Your script has access to any applications or data stored on the Flux cluster When your job completes, anything it sent to standard output and error are saved and returned to you You can check on the status of your job at any time, or delete it if it’s not doing what you want A short time after your job completes, it disappears 2/14cja

39 Basic batch commands Once you have a script, submit it: qsub scriptfile $ qsub singlenode.pbs You can check on the job status: qstat jobid qstat -u user $ qstat -u cja Req'd Req'd Elap Req'd Req'd Elap Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time nyx.engi cja flux hpc101i :05 Q -- To delete your job qdel jobid $ qdel $ 2/14cja

40 Loosely-coupled batch script #PBS -N yourjobname #PBS -V #PBS -A youralloc_flux #PBS -l qos=flux #PBS -q flux #PBS –l procs=12,pmem=1gb,walltime=01:00:00 #PBS -M your address #PBS -m abe #PBS -j oe #Your Code Goes Below: cd $PBS_O_WORKDIR mpirun./c_ex01 2/1440cja 2014

41 Tightly-coupled batch script #PBS -N yourjobname #PBS -V #PBS -A youralloc_flux #PBS -l qos=flux #PBS -q flux #PBS –l nodes=1:ppn=12,mem=47gb,walltime=02:00:00 #PBS -M your address #PBS -m abe #PBS -j oe #Your Code Goes Below: cd $PBS_O_WORKDIR matlab -nodisplay -r script 2/1441cja 2014

42 Lab 5 Task: Run an MPI job on 8 cores Compile c_ex05 cd ~/cac-intro-code make c_ex05 Edit file run with your favorite Linux editor Change #PBS -M address to your own I don’t want Brock to get your ! Change #PBS -A allocation to FluxTraining_flux, or to your own allocation, if desired Change #PBS -l allocation to flux Submit your job qsub run 2/14cja

43 PBS attributes As always, man qsub is your friend -N : sets the job name, can’t start with a number -V : copy shell environment to compute node -A youralloc_flux: sets the allocation you are using -l qos=flux: sets the quality of service parameter -q flux : sets the queue you are submitting to -l : requests resources, like number of cores or nodes -M : whom to , can be multiple addresses -m : when to a=job abort, b=job begin, e=job end -j oe: join STDOUT and STDERR to a common file -I : allow interactive use -X : allow X GUI use 2/14cja

44 PBS resources (1) A resource ( -l ) can specify: Request wallclock (that is, running) time -l walltime=HH:MM:SS Request C MB of memory per core -l pmem=Cmb Request T MB of memory for entire job -l mem=Tmb Request M cores on arbitrary node(s) -l procs=M Request a token to use licensed software -l gres=stata:1 -l gres=matlab -l gres=matlab%Communication_toolbox 2/14cja

45 PBS resources (2) A resource ( -l ) can specify: For multithreaded code: Request M nodes with at least N cores per node -l nodes=M:ppn=N Request M cores with exactly N cores per node (note the difference vis a vis ppn syntax and semantics!) -l nodes=M,tpn=N (you’ll only use this for specific algorithms) 2/14cja

46 Interactive jobs You can submit jobs interactively: qsub -I -X -V -l procs=2 -l walltime=15:00 -A youralloc_flux -l qos=flux –q flux This queues a job as usual Your terminal session will be blocked until the job runs When your job runs, you'll get an interactive shell on one of your nodes Invoked commands will have access to all of your nodes When you exit the shell your job is deleted Interactive jobs allow you to Develop and test on cluster node(s) Execute GUI tools on a cluster node Utilize a parallel debugger interactively 2/1446cja 2014

47 Lab 6 Task: Run an interactive job Enter this command (all on one line): qsub -I -V -l procs=1 -l walltime=30:00 -A FluxTraining_flux -l qos=flux -q flux When your job starts, you’ll get an interactive shell Copy and paste the batch commands from the “run” file, one at a time, into this shell Experiment with other commands After thirty minutes, your interactive shell will be killed 2/14cja

48 Lab 7 Task: Run Matlab interactively module load matlab Start an interactive PBS session qsub -I -V -l procs=2 -l walltime=30:00 -A FluxTraining_flux -l qos=flux -q flux Run Matlab in the interactive PBS session matlab -nodisplay 2/14cja

49 Introduction to Scheduling 2/14cja

50 The Scheduler (1/3) Flux scheduling policies: The job’s queue determines the set of nodes you run on The job’s account and qos determine the allocation to be charged If you specify an inactive allocation, your job will never run The job’s resource requirements help determine when the job becomes eligible to run If you ask for unavailable resources, your job will wait until they become free There is no pre-emption 2/14cja

51 The Scheduler (2/3) Flux scheduling policies : If there is competition for resources among eligible jobs in the allocation or in the cluster, two things help determine when you run: How long you have waited for the resource How much of the resource you have used so far This is called “fairshare” The scheduler will reserve nodes for a job with sufficient priority This is intended to prevent starving jobs with large resource requirements 2/14cja

52 The Scheduler (3/3) Flux scheduling policies : If there is room for shorter jobs in the gaps of the schedule, the scheduler will fit smaller jobs in those gaps This is called “backfill” Cores Time 2/14cja

53 Gaining insight There are several commands you can run to get some insight over the scheduler’s actions: freenodes : shows the number of free nodes and cores currently available mdiag -a youralloc_name : shows resources defined for your allocation and who can run against it showq -w acct=yourallocname : shows jobs using your allocation (running/idle/blocked) checkjob jobid : Can show why your job might not be starting showstart -e all jobid : Gives you a coarse estimate of job start time; use the smallest value returned 2/14cja

54 More advanced scheduling Job Arrays Dependent Scheduling cja 20142/1454

55 Job Arrays Submit copies of identical jobs Submit copies of identical jobs Invoked via qsub –t: Invoked via qsub –t: qsub –t array-spec pbsbatch.txt Where array-spec can be m-na,b,cm-n%slotlimite.g. qsub –t 1-50%10Fifty jobs, numbered 1 through 50, only ten can run simultaneously $PBS_ARRAYID records array identifier $PBS_ARRAYID records array identifier cja 20142/1455

56 Dependent scheduling Submit jobs whose execution scheduling depends on other jobs Submit jobs whose execution scheduling depends on other jobs Invoked via qsub –W: Invoked via qsub –W: qsub -W depend=type:jobid[:jobid]… Where depend can be afterSchedule after jobids have started afterokSchedule after jobids have finished, only if no errors afternotokSchedule after jobids have finished, only if errors afteranySchedule after jobids have finished, regardless of status before,beforeok,beforenotok,beforeany cja 20142/1456

57 Dependent scheduling Where depend can be (cont’t) beforeWhen this job has started, jobids will be scheduled beforeokAfter this job completes without errors, jobids will be scheduled beforenotokAfter this job completes without errors, jobids will be scheduled afteranyAfter this job completes, regardless of status, jobids will be scheduled cja 20142/1457

58 Some Flux Resources U-M Advanced Research Computing Flux pages CAEN HPC Flux pages CAEN HPC YouTube channel For assistance: Read by a team of people including unit support staff Cannot help with programming questions, but can help with operational Flux and basic usage questions 2/14cja

59 Summary The Flux cluster is just a collection of similar Linux machines connected together to run your code, much faster than your desktop can Command-line scripts are queued by a batch system and executed when resources become available Some important commands are qsub qstat -u username qdel jobid checkjob Develop and test, then submit your jobs in bulk and let the scheduler optimize their execution 2/14cja

60 Any Questions? Charles J. Antonelli LSAIT Advocacy and Research Support /14cja

61 References 1. 2. 3. 1. 2.CAC supported Flux software, (accessed August 2013) 3.J. L. Gustafson, “Reevaluating Amdahl’s Law,” chapter for book, Supercomputers and Artificial Intelligence, edited by Kai Hwang, (accessed November 2011). 4.Mark D. Hill and Michael R. Marty, “Amdahl’s Law in the Multicore Era,” IEEE Computer, vol. 41, no. 7, pp , July (accessed November 2011). 5.InfiniBand, (accessed August 2011). 6.Intel C and C++ Compiler 1.1 User and Reference Guide, (accessed August 2011). 7.Intel Fortran Compiler 11.1 User and Reference Guide, (accessed August 2011). 8.Lustre file system, (accessed August 2011). 9.Torque User’s Manual, (accessed August 2011). 10.Jurg van Vliet & Flvia Paginelli, Programming Amazon EC2,’Reilly Media, ISBN /14cja

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