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Parallel ISDS Chris Hans 29 November 2004.

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Presentation on theme: "Parallel ISDS Chris Hans 29 November 2004."— Presentation transcript:

1 Parallel ISDS Chris Hans 29 November 2004

2 Parallel ISDS2 Organization  Basics of Parallel Computing Structural Computational  Coding for Distributed Computing  Examples  Resources at Duke CSEM Cluster

3 29 November 2004Parallel ISDS3 Basics of Distributed Computing  Online Tutorial (Livermore Nat. Lab.)  Serial Computing: one computer, one CPU  Parallel Computing: multiple computers working at the same time

4 29 November 2004Parallel ISDS4 Various Setups  Collection of Workstations PVM (Parallel Virtual Machine) R (rpvm, Rmpi, SNOW) LAM-MPI (Local Area Multicomputer) Matlab  Dedicated Cluster Master/Slave model MPI

5 29 November 2004Parallel ISDS5 Network Layout  Basic layout: Each node has: CPU(s), memory

6 29 November 2004Parallel ISDS6 Designing a Parallel Program Do the nodes need to interact? “Embarrassingly Parallel” Very little communication -- Monte Carlo “Shamelessly Parallel” Communication Needed -- Heat diffusion models -- Spatial models

7 29 November 2004Parallel ISDS7 Message Passing Interface  “MPI” Standard Easy to use functions that manage the communication between nodes.

8 29 November 2004Parallel ISDS8 Master/Slave Model  Organize the layout: Slaves

9 29 November 2004Parallel ISDS9 Master/Slave Model  “Master” divides the task into pieces…  …while slaves “listen” to the network, waiting for work.  Master sends out the work to be done…  …slaves do the work…  …while the Master waits for the answers.  Slaves return the results.

10 29 November 2004Parallel ISDS10 Example: Monte Carlo

11 29 November 2004Parallel ISDS11 Example: Monte Carlo Master Slave 1 Draw N/3 values Compute Return Slave 2 Same Slave 3 Same

12 29 November 2004Parallel ISDS12 Code  Write ONE program Same for master and slaves  Run program on EACH node Each program has to figure out if it’s the master or a slave MPI

13 29 November 2004Parallel ISDS13 Pseudo Code LOAD DATA; GET ID; IF(ID==MASTER) { MASTER(); } ELSE { SLAVE(); }... LOAD DATA; GET ID; IF(ID==MASTER) { MASTER(); } ELSE { SLAVE(); }... LOAD DATA; GET ID; IF(ID==MASTER) { MASTER(); } ELSE { SLAVE(); }... LOAD DATA; GET ID; IF(ID==MASTER) { MASTER(); } ELSE { SLAVE(); }... MasterSlave 1Slave 2Slave 3

14 29 November 2004Parallel ISDS14 MASTER { // Find # of nodes GET NP; for(i in 1:NP) { “Tell process i to compute the mean for 1000 samples” } RET=RES=0; // Wait for results WHILE(RET

15 29 November 2004Parallel ISDS15 PRINT RES; RETURN TO MAIN ; }... FINALIZE(); }... FINALIZE(); }... FINALIZE(); }... FINALIZE(); } MasterSlave 1Slave 2Slave 3 Pseudo Code

16 29 November 2004Parallel ISDS16 Example: C++ Code  Large dataset N=40, p = 1,000 One outcome variable, y  Calculate R 2 for all 1-var regression  parallel_R2.cpp

17 29 November 2004Parallel ISDS17 CSEM Duke  Computational Science, Engineering and Medicine Cluster  Shared machines Core Nodes Contributed Nodes

18 29 November 2004Parallel ISDS18 CSEM Cluster Details  4 Dual processing head nodes  64 Dual processing shared nodes Intel Xeon 2.8 GHz  40 Dual processing “stat” nodes Intel Xeon 3.1 GHz  161 Dual processing “other” nodes Owners get priority

19 29 November 2004Parallel ISDS19

20 29 November 2004Parallel ISDS20 Using the Cluster  Access limited ssh –l cmh27  Data stored locally on cluster  Compile using mpicc, mpif77, mpif90  Cluster uses SGE Queuing System

21 29 November 2004Parallel ISDS21 Queuing System  Submit your job with requests memory usage number of CPUs (nodes)  Assigns nodes and schedules job Least-loaded machines fitting requirements  Jobs run outside of SGE are killed

22 29 November 2004Parallel ISDS22 Compiling Linked libraries, etc… g++ -c parallel_R2.cpp -I/opt/mpich-1.2.5/include -L/opt/mpich-1.2.5/lib mpicc parallel_R2.o -o parallel_R2.exe -lstdc++ -lg2c -lm

23 29 November 2004Parallel ISDS23 Submitting a Job  Create a queue script: #!/bin/tcsh # #$ -S /bin/tcsh -cwd #$ -M -m b #$ -M -m e #$ -o parallel_R2.out -j y #$ -pe low-all 5 cd /home/stat/username/ mpirun -np $NSLOTS -machinefile $TMPDIR/machines parallel_R2.exe me when the job is submitted and when it finishes Output file Priority and number of nodes requested Your Home Directory on CSEM Cluster

24 29 November 2004Parallel ISDS24 Submitting a Job  Type: cmh27]$ qsub parallel_R2.q Your job ("parallel_R2.q") has been submitted.  Monitoring

25 29 November 2004Parallel ISDS25 Downloadable Files You can download the slides, example C++ code and queuing script at:

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