USArray Data Processing WorkshopBig Iron and Parallel Processing
Scott Teige, PhDJuly 30, 2009
July 30, 2009USArray Data Processing Workshop
Overview• How big is “Big Iron”?• Where is it, what is it?• One system, the details• Parallelism, the way forward• Scaling and what it means to you• Programming techniques• Examples• Excercises
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What is the TeraGrid?• “… a nationally distributed
cyberinfrastructure that provides leading edge computational and data services for scientific discovery through research and education…”
• A document exists in your training account home directories.
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Some TeraGrid SystemsKraken NICS Cray 608 TF 128 TBRanger TACC Sun 579 123Abe NCSA Dell 89 9.4Lonestar TACC Dell 62 11.6
Steele Purdue Dell 60 12.4Queen Bee LONI Dell 50 5.3
Lincoln NCSA Dell 47 3.0BigRed IU IBM 30 6.0
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System LayoutKraken 2.30 GHz 66048 cores
Ranger 2.66 62976
Abe 2.33 9600
Lonestar 2.66 5840
Steele 2.33 7144
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AvailabilityKraken 608TFLOPS 96% Use 24.3 IdleTFRanger 579 91% 52.2Abe 89 90% 8.9Lonestar 62 92% 5.0Steele 60 67% 19.8Queen Bee 51 95% 2.5Lincoln 48 4% 45.6Big Red 31 83% 5.2
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Research CyberinfrastructureThe Big Picture:• Compute
Big Red (IBM e1350 Blade Center JS21)Quarry (IBM e1350 Blade Center HS21)
• StorageHPSSGPFSOpenAFSLustreLustre/WAN
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High Performance Systems• Big Red [TeraGrid System]
30 TFLOPS IBM JS21 SuSE Cluster 768 blades/3072 cores: 2.5 GHz PPC 970MP8GB Memory, 4 cores per bladeMyrinet 2000LoadLeveler & Moab
• Quarry [Future TeraGrid System]7 TFLOPS IBM HS21 RHEL Cluster140 blades/1120 cores: 2.0 GHz Intel Xeon 53358GB Memory, 8 cores per blade1Gb Ethernet (upgrading to 10Gb)PBS (Torque) & Moab
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July 30, 2009USArray Data Processing Workshop
Data Capacitor (AKA Lustre)High Performance Parallel File system
-ca 1.2PB spinning disk-local and WAN capabilities
SC07 Bandwidth Challenge Winner-moved 18.2 Gbps across a single 10Gbps link
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HPSS• High Performance Storage System• ca. 3 PB tape storage• 75 TB front-side disk cache• Ability to mirror data between IUPUI and
IUB campuses
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Serial vs. Parallel• Calculation• Flow Control• I/O
• Calculation• Flow Control• I/O• Synchronization• Communication
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A SerialProgram
F
1-F
F/N
1-F
S=1/(1-F+F/N)
Amdahl’s Law:
Special case, F=1
S=N, Ideal Scaling
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Speed for various scaling rules
S=Ne -(N-1)/q
“Paralyzable Process”
S>N
“Superlinear Scaling”
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MPI vs. OpenMP• MPI code may
execute across many nodes
• Entire program is replicated for each core (sections may or may not execute)
• Variables not shared• Typically requires
structural modification to code
• OpenMP code executes only on the set of cores sharing memory
• Sections of code may be parallel or serial
• Variables may be shared
• Incremental parallelization is easy
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Other methods exist:• Sockets• Explicit shared memory calls/operations• Pthreads• None are recommended
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export OMP_NUM_THREADS=8icc mp_baby.c -openmp -o mp_baby./mp_baby
#include <stdio.h>#include <omp.h>
int main(int argc, char *argv[]) {int iam = 0, np = 1;
#pragma omp parallel default(shared) private(iam, np){
#if defined (_OPENMP)np = omp_get_num_threads();iam = omp_get_thread_num();
#endifprintf("Hello from thread %d out of %d\n", iam, np);
}}
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PROGRAM DOT_PRODUCT
INTEGER N, CHUNKSIZE, CHUNK, IPARAMETER (N=100)PARAMETER (CHUNKSIZE=10)REAL A(N), B(N), RESULT
! Some initializationsDO I = 1, N
A(I) = I * 1.0B(I) = I * 2.0
ENDDORESULT= 0.0CHUNK = CHUNKSIZE
!$OMP PARALLEL DO!$OMP& DEFAULT(SHARED) PRIVATE(I)!$OMP& SCHEDULE(STATIC,CHUNK)!$OMP& REDUCTION(+:RESULT)
DO I = 1, NRESULT = RESULT + (A(I) * B(I))
ENDDO
!$OMP END PARALLEL DO NOWAIT
PRINT *, 'Final Result= ', RESULTEND
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Synchronization Constructs• MASTER: block executed only by master
thread• CRITICAL: block executed by one thread
at a time• BARRIER: each thread waits until all
threads reach the barrier• ORDERED: block executed sequentially
by threads
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Data Scope Attribute Clauses• SHARED: variable is shared across all
threads• PRIVATE: variable is replicated in each
thread• DEFAULT: change the default scoping of
all variables in a region
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Some Useful Library routines• omp_set_num_threads(integer)• omp_get_num_threads()• omp_get_max_threads()• omp_get_thread_num()• Others are implementation dependent
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OpenMP Advice• Always explicitly scope variables• Never branch into/out of a parallel region• Never put a barrier in an if block• Quarry is at OpenMP version <3.0, TASK
construct, for example, not there
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Exercise: OpenMP
• The example programs are in ~/OMP_F_examples or ~/OMP_C_examples
• Go to https://computing.llnl.gov/tutorials/openMP/excercise.html• Skip to step 4, compiler is “icc” or “ifort”• There is no evaluation form
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#include <stdio.h>#include <stdlib.h>#include <mpi.h>int myrank;int ntasks;
int main(int argc, char **argv){
/* Initialize MPI */MPI_Init(&argc, &argv);
/* get number of workers */MPI_Comm_size(MPI_COMM_WORLD, &ntasks);
/* Find out my identity in the default communicatoreach task gets a unique rank between 0 and ntasks-1 */
MPI_Comm_rank(MPI_COMM_WORLD, &myrank);
MPI_Barrier(MPI_COMM_WORLD);
fprintf(stdout,"Hello from MPI_BABY=%d\n",myrank);MPI_Finalize();exit(0);
}
… …
Node 1 Node 2 …
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mpicc mpi_baby.c –o mpi_baby
mpirun –np 8 mpi_baby
mpirun –np 32 –machinefile my_list mpi_baby
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C AUTHOR: Blaise Barneyprogram scatterinclude 'mpif.h'integer SIZEparameter(SIZE=4)integer numtasks, rank, sendcount, recvcount, source, ierrreal*4 sendbuf(SIZE,SIZE), recvbuf(SIZE)
C Fortran stores this array in column major order, so the C scatter will actually scatter columns, not rows.
data sendbuf /1.0, 2.0, 3.0, 4.0, & 5.0, 6.0, 7.0, 8.0,& 9.0, 10.0, 11.0, 12.0, & 13.0, 14.0, 15.0, 16.0 /call MPI_INIT(ierr)call MPI_COMM_RANK(MPI_COMM_WORLD, rank, ierr)call MPI_COMM_SIZE(MPI_COMM_WORLD, numtasks, ierr)if (numtasks .eq. SIZE) then
source = 1sendcount = SIZErecvcount = SIZEcall MPI_SCATTER(sendbuf, sendcount, MPI_REAL, recvbuf,
& recvcount, MPI_REAL, source, MPI_COMM_WORLD, ierr)print *, 'rank= ',rank,' Results: ',recvbuf
elseprint *, 'Must specify',SIZE,' processors. Terminating.'
endifcall MPI_FINALIZE(ierr)end
From the man page:
MPI_Scatter - Sends data from one task to all tasks in a group
…message is split into n equal segments, the ith segment is sent to the ith process in the group
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man -w MPIls /N/soft/linux-rhel4-x86_64/openmpi/1.3.1/intel-64/share/man/man3
MPI_AbortMPI_AllgatherMPI_AllreduceMPI_Alltoall...MPI_WaitMPI_WaitallMPI_WaitanyMPI_Waitsome
mpicc --showme/N/soft/linux-rhel4-x86_64/intel/cce/10.1.022/bin/icc \-I/N/soft/linux-rhel4-x86_64/openmpi/1.3.1/intel-64/include \-pthread -L/N/soft/linux-rhel4-x86_64/openmpi/1.3.1/intel-64/lib \-lmpi -lopen-rte -lopen-pal -ltorque -lnuma -ldl \-Wl,--export-dynamic -lnsl -lutil -ldl -Wl,-rpath -Wl,/usr/lib64
Some linux tricks to get more information:
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MPI cool stuff:• Bi-directional communication• Non-blocking communication• User defined types• Virtual topologies
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MPI Advice• Never put a barrier in an if block• Use care with non-blocking
communication, things can pile up fast
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So, can I use MPI with OpenMP?• Yes you can; extreme care is advised• Some implementations of MPI forbid it• You can get killed by “oversubscription”
real fast, I’ve seen time increase like N2
• But sometimes you must… some fftw libraries are OMP multithreaded, for example.
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Exercise: MPI• Examples are in ~/MPI_F_examples or ~/MPI_C_examples• Go to https://computing.llnl.gov/tutorials/mpi/exercise.html• Skip to step 6. MPI compilers are “mpif90” and “mpicc”, normal
(serial) compilers are “ifort” and “icc”.• Compile your code: “make all” (Overrides section 9)• To run an mpi code: “mpirun –np 8 <exe>” …or…• “mpirun –np 16 –machinefile <ask me> <exe>”• Skip section 12• There is no evaluation form.
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Where were those again?• https://computing.llnl.gov/tutorials/openMP/excercise.html• https://computing.llnl.gov/tutorials/mpi/exercise.html
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Acknowledgements
• This material is based upon work supported by the National Science Foundation under Grant Numbers 0116050 and 0521433. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation (NSF).
• This work was support in part by the Indiana Metabolomics and Cytomics Initiative (METACyt). METACyt is supported in part by Lilly Endowment, Inc.
• This work was support in part by the Indiana Genomics Initiative. The Indiana Genomics Initiative of Indiana University is supported in part by Lilly Endowment, Inc.
• This work was supported in part by Shared University Research grants from IBM, Inc. to Indiana University.