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1 Introduction to Supercomputing at ARSC Kate Hedstrom, Arctic Region Supercomputing Center (ARSC) Jan, 2004.

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Presentation on theme: "1 Introduction to Supercomputing at ARSC Kate Hedstrom, Arctic Region Supercomputing Center (ARSC) Jan, 2004."— Presentation transcript:

1 1 Introduction to Supercomputing at ARSC Kate Hedstrom, Arctic Region Supercomputing Center (ARSC) Jan, 2004

2 2 Topics Introduction to Supercomputers at ARSC –Computers Accounts –Getting an account –Kerberos –Getting help Architectures of parallel computers –Programming models Running Jobs –Compilers –Storage –Interactive and batch

3 3 Introduction to ARSC Supercomputers They’re all Parallel Computers Three Classes: –Shared Memory –Distributed Memory –Distributed & Shared Memory

4 4 Cray X1: klondike 128 MSPs 4 MSP/node 4 Vector CPU/MSP, 800 MHz 512 GB Total 21 TB Disk 1600 GFLOPS peak NAC required

5 5 Cray SX-6: rime 8 500MHz NEC Vector CPUs 64 GB of shared memory 1 TB RAID-5 Disk 64 GFLOPS peak Only one in the USA On loan from Cray Non-NAC

6 6 Cray SV1ex: chilkoot 32 Vector CPUs, 500 MHz 32 GB Shared memory 2 TB Disk 64 GFLOPS peak NAC required

7 7 Cray T3E: yukon 272 CPUs, 450 MHz 256 MB per processor 69.6 GB total distributed memory 230 GFLOPS peak NAC required

8 8 IBM Power4: iceberg 2 nodes of 32 p690+s, 1.7 GHz (2 cabinets) 256 GB each 92 nodes of 8 p655+s, 1.5 GHz (6 cabinets) 6 nodes of 8 p655s 1.1 GHz (1 cabinet) 16 GB Mem/Node 22 TB Disk 5000 GFLOPS NAC required

9 9 IBM Regatta: iceflyer 8-way, 16GB front end coming soon GHz Power4 CPUs in –24-way SMP node –7-way interactive node –1 test node –32-way SMP node soon 256 GB Memory 217 GFLOPS Non-NAC

10 10 IBM SP Power3: icehawk 50 4-Way SMP Nodes => 200 CPUs, 375 MHz 2 GB Memory/Node 36 GB Disk/Node 264 GFLOPS peak for 176 CPUs (max per job) Leaving soon NAC required

11 11 Storing Files Robotic tape silos Two Sun storage servers Nanook –Non-NAC systems Seawolf –NAC systems

12 12 Accounts, Logging In Getting an Account/Project Doing a NAC Logging in with Kerberos

13 13 Getting an Account/Project Academic Applicant for resources is a PI: –Full time faculty or staff research person –Non-commercial work, must reside in USA –PI may add users to their project –http://www.arsc.edu/support/accounts/acquire.html DoD Applicant –http://www.hpcmo.hpc.mil/Htdocs/SAAA Commercial, Federal, State –Contact User Services Director –Barbara Horner-Miller, –Academic guidelines apply

14 14 Doing a National Agency Check (NAC) Required for HPCMO Resources only –Not required for workstations, Cray SX-6, or IBM Regatta Not a security clearance –But there are detailed questions covering last 5-7 years Electronic Personnel Security Questionnaire (EPSQ) –Windows only software Fill out EPSQ cover sheet –http://www.arsc.edu/support/policy/pdf/OPM_Cover.pdf Fingerprinting, Proof of Citizenship (passport, visa, etc.) –See

15 15 Logging in with Kerberos On non-ARSC systems, download kerberos5 client –http://www.arsc.edu/support/howtos/krbclients.html Used with SecureID –Uses a pin to generate a key at login time Login requires user name, pass phrase, & key –Don’t share your pin or SecureID with anyone Foreign Nationals or others with problems –Contact ARSC to use ssh to connect to ARSC gateway –Still need Kerberos & SecureID after connecting

16 16 SecureID

17 17 From ARSC System Enter username Enter for principle Enter pass phrase Enter SecureID passcode From that system: ssh iceflyer ssh handles X11 handshaking From ARSC System

18 18 From Your System Get Kerberos clients installed Get ticket kinit See tickets klist Login into arsc system krlogin -l username iceflyer ssh -l username iceflyer ktelnet -l username iceflyer

19 19 Rime and Rimegate Log into rimegate as usual, with your rimegate username (arscxxx) ssh -l arscksh rimegate Compile on rimegate (sxf90, sxc++) Log into rime from rimegate ssh rime Rimegate $HOME is /rimegate/users/username on rime

20 20 Supercomputer Architectures They’re all Parallel Computers Three Classes: –Shared Memory –Distributed Memory –Distributed & Shared Memory

21 21 Shared Memory Architecture Cray SV1, SX-6, IBM Regatta

22 22 Distributed Memory Architecture Cray T3E

23 23 Cluster Architecture IBM iceberg, icehawk, Cray X1 Scalable, distributed, shared-memory parallel processor

24 24 Programming Models Vector Processing –compiler detection or manual directives Threaded Processing (SMP) –OpenMP, Pthreads, java threads –shared memory only Distributed Processing (MPP) –message passing with MPI –shared or distributed memory

25 25 Vector Programming Vector CPUs are specialized for array/matrix operations –64-element (SV1, X1), 256-element (SX-6) Vector Registers –Operations proceed assembly-line fashion –High memory-to-CPU bandwidth Less CPU time wasted waiting for data from memory –Once loaded, produces one result per clock cycle Compiler does a lot of the work

26 26 Vector Programming Codes will run without modification. Cray compilers automatically detect loops which are safe to vectorize. Request listing file to find out what vectorized. Programmer can assist the compiler: –Directives and pragmas can force vectorization –Eliminate conditions which inhibit vectorization (e.g., subroutine calls and data dependencies in loops)

27 27 Threaded Programming on Shared-Memory Systems OpenMP –Directives/pragmas added to serial programs –A portable standard implemented on Cray (one node), SGI, IBM (one node), etc... Other Threaded Paradigms –Java Threads –Pthreads

28 28 OpenMP Fortran Example !$omp parallel do do n = 1,10000 A(n) = x * B(n) + c end do ___________________________________________________ On 2 CPUS, this pragma divides work as follows: CPU 1: do n = 1,5000 A(n) = x * B(n) + c end do CPU 2: do n = 5001,10000 A(n) = x * B(n) + c end do

29 29 OpenMP C Example #pragma omp parallel for for (n = 0; n < 10000; n++) A[n] = x * B[n] + c; ___________________________________________________ On 2 CPUS, this pragma divides work as follows: CPU 1: for (n = 0; n < 5000; n++) A[n] = x * B[n] + c; CPU 2: for (n = 5000; n < 10000; n++) A[n] = x * B[n] + c;

30 30 Threads Dynamically Appear and Disappear Number set by Environment

31 31 Distributed Processing Concept: 1) Divide the problem explicitly 2) CPUs Perform tasks concurrently 3) Recombine results 4) All processors may or may not be doing the same thing Branimir Gjetvaj

32 32 Distributed Processing Data needed by a given CPU must be stored in the memory associated with that CPU Performed on distributed or shared memory computer Multiple copies of code are running Messages/data are passed between CPUs Multi-level: can be combined with vector and/or OpenMP

33 33 Initialization Simple send/receive ! Processor 0 sends individual messages to others if (my_rank == 0) then do dest = 1, npes-1 call mpi_send(x, max_size, MPI_FLOAT, dest, 0, comm, ierr); end do else call mpi_recv(x, max_size, MPI_FLOAT, 0, 0, comm, status, ierr); end if call mpi_init(ierror) call mpi_comm_size (MPI_COMM_WORLD, npes, ierror); call mpi_comm_rank (MPI_COMM_WORLD, my_rank, ierror); Distributed Processing using MPI (Fortran)

34 34 Initialization Simple send/receive /* Processor 0 sends individual messages to others */ if (my_rank == 0) { for (dest = 1; dest < npes; dest++) { MPI_Send(x, max_size, MPI_FLOAT, dest, 0, comm); } else { MPI_Recv(x, max_size, MPI_FLOAT, 0, 0, comm, &status); } MPI_Init(&argc, &argv); MPI_Comm_size (MPI_COMM_WORLD, &npes); MPI_Comm_rank (MPI_COMM_WORLD, &my_rank); Distributed Processing using MPI (C)

35 35 Number of Processes Constant Number set by Environment

36 36 Message Passing Activity Example

37 37 Cluster Programming Shared-memory between processors on one node: –OpenMP, threads, or MPI Distributed-memory methods between processors on multiple nodes –MPI Mixed mode –MPI distributes to nodes, OpenMP within node

38 38 Programming Environments Compilers File Systems Running jobs –Interactive –Batch See individual machine documentation –http://www.arsc.edu/support/resources/hardware.html

39 39 Cray Compilers SV1, T3E –f90, cc, CC X1 –ftn, cc, CC SX-6 front end (rimegate) –sxf90, sxc++ SX-6 (rime) –f90, cc, c++ No extra flags for MPI, OpenMP

40 40 IBM Compilers Serial –xlf, xlf90, xlf95, xlc, xlC OpenMP –Add -qsmp=omp, _r extension for thread- safe libraries, e.g. xlf_r MPI –mpxlf, mpxlf90, mpxlf95, mpcc, mpCC Might be best to always use _r extension (mpxlf90_r)

41 41 File Systems Local storage –$HOME –/tmp or /wrktmp or /wrkdir -> $WRKDIR –/scratch -> $SCRATCH Permanent storage –$ARCHIVE Quotas –quota -v on Cray –qcheck on IBM

42 42 Running a job Get files from $ARCHIVE to system’s disk Keep source in $HOME, but run in $WRKDIR Use $SCRATCH for local-to-node temporary files, clean up before job ends Put results out to $ARCHIVE $WRKDIR is purged

43 43 Iceflyer Filesystems Smallish $HOME Larger /wrkdir/username $ARCHIVE for longterm storage, especially larger files qcheck to check quotas

44 44 SX6 Filesystems Separate from the rest of ARSC systems Rimegate has /home, /scratch Rime mounts them as /rimegate/home, /rimegate/scratch Rime has own home, /tmp, /atmp, etc.

45 45 Interactive Works on the command line Limits exist on resources (time, # cpus, memory) Good for debugging Larger jobs must be submitted to the batch system

46 46 Batch Schedulers Cray: NQS –Commands: qsub, qstat, qdel IBM: LoadLeveler –Commands: llclass, llq, llsubmit, llcancel, llmap, xloadl

47 47 NQS Script (rime) batch # job queue class /bin/ksh # which shell # stdout and stderr together 100 MW 30:00 # time requested h:m:s 8 # 8 cpus # required last command # beginning of shell script cd $QSUB_WORKDIR # cd to submission directory export F_PROGINF=DETAIL export OMP_NUM_THREADS=8./my_job

48 48 NQS Commands qstat to find out job status, list of queues qsub to submit job qdel to delete job from queue

49 49 LoadLeveler Script (iceflyer) #!/bin/ksh total_tasks = 4 node_usage = shared wall_clock_limit = 1:00:00 job_type = parallel output = out.$(jobid) error = err.$(jobid) class = large notification = error queue poe./my_job

50 50 Loadleveler Commands llclass to find list of classes llq to see list of jobs in queue llsubmit to submit job llcancel to delete job from queue llmap is local program to see load on machine xloadl X11 interface to loadleveler

51 51 Getting Help Consultants and Specialists are here to serve YOU –

52 52 Homework Make sure you can log into –iceflyer –rimegate –rime Ask consultants for help if necessary


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