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Introduction to Parallel Computing Intel Math Kernel Library

Introduction to Parallel Computing Intel Math Kernel Library. Huan -Ting Yen, Department of Mathematics, National Taiwan University 2011/07/22. Parallel Computing. What is parallel computing?. Traditionally, software has been written for serial computation:. What is parallel computing?.

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Introduction to Parallel Computing Intel Math Kernel Library

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  1. IntroductiontoParallelComputingIntelMathKernelLibrary Huan-TingYen, Department of Mathematics, National Taiwan University2011/07/22

  2. ParallelComputing IntroductiontoParallelComputing

  3. Whatisparallelcomputing? • Traditionally,softwarehasbeenwrittenforserialcomputation: IntroductiontoParallelComputing

  4. Whatisparallelcomputing? • Inthesimplestsense,parallelcomputingisthesimultaneoususeofmultiplecomputeresourcestosolveacomputationalproblem: IntroductiontoParallelComputing

  5. Resource • Thecomputeresource • Asinglecomputerwithmultipleprocessors; • Anarbitrarynumberofcomputersconnectedbyanetwork; • Acombinationofboth. Core 1 Core 2 Core 3 Core 4 thread 1 thread 2 thread 3 thread 4 IntroductiontoParallelComputing

  6. Resource • Thecomputeresource • Asinglecomputerwithmultipleprocessors; • Anarbitrarynumberofcomputersconnectedbyanetwork; • Acombinationofboth. several threads several threads several threads several threads core1 core2 core3 core4 IntroductiontoParallelComputing

  7. Resource • Thecomputeresource • Asinglecomputerwithmultipleprocessors; • Anarbitrarynumberofcomputersconnectedbyanetwork; • Acombinationofboth. IntroductiontoParallelComputing

  8. Resource • Thecomputeresource • Asinglecomputerwithmultipleprocessors; • Anarbitrarynumberofcomputersconnectedbyanetwork; • Acombinationofboth. IntroductiontoParallelComputing

  9. Whyuseparallelcomputing? • Theprimaryreasonsforusingparallelcomputing: • Savetime–wallclocktime • Solvelargerproblems • Provideconcurrency(domanythingsatthesametime) • Otherreasonsmightinclude: • Takingadvantageofnon-localresources • Costsavings • Overcomingmemoryconstraints IntroductiontoParallelComputing

  10. Amdahl’sLaw • Speedupofaparallelprogramislimitedbyamountof serialworks. IntroductiontoParallelComputing

  11. Amdahl’sLaw • Speedupofaparallelprogramislimitedbyamountof serialworks. IntroductiontoParallelComputing

  12. Flynn’sTaxonomy • Classificationforparallelcomputersandprograms IntroductiontoParallelComputing

  13. Flynn’sTaxonomy • Classificationforparallelcomputersandprograms IntroductiontoParallelComputing

  14. Flynn’sTaxonomy • Classificationforparallelcomputersandprograms IntroductiontoParallelComputing

  15. IntelMathKernelLibrary IntroductiontoParallelComputing

  16. Overview • The Intel® Math Kernel Library (Intel® MKL) provides Fortran routines and functions that perform a wide variety of operations on vectors and matrices including sparse matrices. The library also includes fast Fourier transform (FFT) functions, as well as vector mathematical and vector statistical functions with Fortran and C interfaces. • The versions of Intel MKL intended for Windows* and Linux* operating systems also include ScaLAPACK software and Cluster FFT software for solving respective computational problems on distributed-memory parallel computers. Intel MKL Quickstart

  17. Intel MKL: Intel Math Kernel Library • Functionality • BLASandSparseBLASRoutines • LAPACKRoutines:LinearEquations • LAPACKRoutines:EigenvalueProblems • ScaLAPACK • SparseSolverRoutines • Fast Fourier Transforms • ClusterFast Fourier Transforms Intel MKL Quickstart

  18. SystemRequirements(Hardware) • Hardware: • Intel® Core™ processor family • Intel® Xeon® processor family • Intel® Pentium® 4processor family • Intel® Pentium®lllprocessor • Intel® Pentium®processor(300MHzorfaster) • Intel® Celeron® processor • AMD Athlon* and Opteron* processors • HowdoyouknowthatinformationabouttheCPUs? • $cat/proc/cpuinfo Intel MKL Quickstart

  19. SystemRequirements(Software) • Followingisthelistofsupposedoperatingsystem: • RedHat* Enterprise Linux* 3, 4, 5 • RedHat* Fedora* 9 • Debian* GNU/Linux4.0 • Ubuntu* 8.04 • Howdoyouknowthatinformationabouttheoperatingsystem? • $cat/etc/*release • FollowingisthelistofsupposedC/C++andFortrancompilers: • Intel® Fortran Compiler 10.1 for Linux* • Intel® C++ Compiler 10.1 for Linux* • GNUCompilerCollection(gcc,g77,gfortran4.2.0) Intel MKL Quickstart

  20. InstallingIntelMKLonaLinux* System • Tools&Downloads • http://software.intel.com/en-us/(google“intelsoftware”) Intel MKL Quickstart

  21. InstallingIntelMKLonaLinux* System Intel MKL Quickstart

  22. InstallingIntelMKLonaLinux* System Intel MKL Quickstart

  23. InstallingIntelMKLonaLinux* System Intel MKL Quickstart

  24. InstallingIntelMKLonaLinux* System Intel MKL Quickstart

  25. InstallingIntelMKLonaLinux* System Intel MKL Quickstart

  26. InstallingIntelMKLonaLinux* System Intel MKL Quickstart

  27. InstallingIntelMKLonaLinux* System • user@host:~/software$wget“URL” • user@host:~/software$ll • $tar–zxvfl_mkl_p_10.2.x.yyy.tar.gz Intel MKL Quickstart

  28. InstallingIntelMKLonaLinux* System • cdl_mkl_p_10.2.x.yyy • ./install.sh Intel MKL Quickstart

  29. InstallingIntelMKLonaLinux* System Intel MKL Quickstart

  30. InstallingIntelMKLonaLinux* System Intel MKL Quickstart

  31. Some Examples Intel MKL Quickstart

  32. Example • Brief examples to • BLASLevel1Routines(vector-vectoroperations) • BLASLevel2Routines(matrix-vectoroperations) • BLASLevel3Routines(matrix-matrixoperations) • Compute the LU factorization of a matrix(LAPACK) • Solve linear system(LAPACK) • Solve eigensystem(LAPACK) • Fast Fourier Transforms Intel MKL Quickstart

  33. Example • Brief examples to • BLASLevel1Routines(vector-vectoroperations) • BLASLevel2Routines(matrix-vectoroperations) • BLASLevel3Routines(matrix-matrixoperations) • Compute the LU factorization of a matrix(LAPACK) • Solve linear system(LAPACK) • Solve eigensystem(LAPACK) • Fast Fourier Transforms Intel MKL Quickstart

  34. Ex1.Thecomplexdot product() #include<stdio.h> #include "mkl_blas.h” #define N5 typedefstruct{ doublere; doubleim; }mkl_complex; intmain() { intn, incx= 1, incy= 1, i; mkl_complexx[N], y[N], res; voidzdotc(); n = N; for( i = 0; i < n; i++ ){ x[i].re = (double)i; x[i].im = (double)i * 2.0; y[i].re = (double)(n - i); y[i].im = (double)i * 2.0; } zdotc( &res, &n, x, &incx, y, &incy); printf( “The complex dot product is: (%6.2f, %6.2f)\n", res.re, res.im ); return0; } Intel MKL Quickstart

  35. ?dotc • Computesadotproductofaconjugatevectorwithanothervector. • Description:Theroutineisdeclaredin • Fortran77:mkl_blas.fi • Fortran95:blas.f90 • C:mkl_blas.h • InputParameters(zdotc(&res,&n,x,&incx,y,&incy)) • n: The length of two vectors. • incx: Specifies the increment for the elements of x • incy: Specifies the increment for the elements of y • outputParameters(zdotc(&res,&n,x,&inca,y,&incb)) • res:The final result Intel MKL Quickstart

  36. Makefile (Sequential) Test:blas_c CC=icc MKL_HOME = /home/opt/intel/mkl/10.2.2.025 MKL_INCLUDE = $(MKL_HOME)/include MKL_PATH = $(MKL_HOME)/lib/em64t EXE=blas_c.exe blas_c: $(CC) -o $(EXE) blas_c.c-I$(MKL_INCLUDE) -L$(MKL_PATH) -lmkl_intel_lp64 -lmkl_sequential-lmkl_core-lpthread Intel MKL Quickstart

  37. Makefile (Parallel) Test=blas_c CC=icc MKL_HOME = /home/opt/intel/mkl/10.2.2.025 MKL_INCLUDE = $(MKL_HOME)/include MKL_PATH = $(MKL_HOME)/lib/em64t EXE=blas_c.exe blas_c: $(CC) -o $(EXE) blas_c.c-I$(MKL_INCLUDE) -L$(MKL_PATH) -Wl,--start-group-lmkl_intel_lp64 -lmkl_core -lmkl_intel_thred-Wl,--end-group–liomp5-lpthread Intel MKL Quickstart

  38. ?dotc • Computesadotproductofaconjugatevectorwithanothervector. • Description:Theroutineisdeclaredin • Fortran77:mkl_blas.fi • Fortran95:blas.f90 • C:mkl_blas.h • InputParameters(zdotc(&res,&n,x,&inca,y,&incb)) • n: The length of two vectors. • incx: Specifies the increment for the elements of x • incy: Specifies the increment for the elements of y • outputParameters(zdotc(&res,&n,x,&inca,y,&incb)) • res:The final result Intel MKL Quickstart

  39. BLASRoutines • RoutinesNamingConventions • BLASBroutinenameshavethefollowingstructure: <character><name><mode>() • The<character>filedindicatesthedatatype: sreal,singleprecision ccomplex,singleprecision dreal,doubleprecision zcomplex,doubleprecision • The<mode>filedindicatesthedatatype: cconjugatedvector uunconjugatedvector gGivensrotation. Intel MKL Quickstart

  40. BLASRoutines • RoutinesNamingConventions • BLASBroutinenameshavethefollowingstructure: <character><name><mode>() • InBLASlevel2and3,<name>filedindicatesthematrixtype: gegeneralmatrix gbgeneralbandmatrix sysymmetricmatrix sbsymmetricbandmatrix heHermitianmatrix hbHermitianbandmatrix trtriangularmatrix tbtriangularbandmatrix Intel MKL Quickstart

  41. BLASLevel1Routines Intel MKL Quickstart

  42. Example • Brief examples to • BLASLevel1Routines(vector-vectoroperations) • BLASLevel2Routines(matrix-vectoroperations) • BLASLevel3Routines(matrix-matrixoperations) • Compute the LU factorization of a matrix(LAPACK) • Solve linear system(LAPACK) • Solve eigensystem(LAPACK) • Fast Fourier Transforms Intel MKL Quickstart

  43. Ex2-1.Matrix-vectorproduct #include "mkl_blas.h” intmain() { intm, n, incx, incy, lda, idxi, idxj; doublealpha, beta, *x, *y, *A ; chartrans; m = 3; n = 3; incx = 1; incy = 1; lda = m; alpha = 1.0; beta = 1.0; trans = 'n’; x = (double*)malloc(sizeof(double)*n); y = (double*)malloc(sizeof(double)*n); A = (double*)malloc(sizeof(double)*m*n); Intel MKL Quickstart

  44. Ex2-2.Matrix-vectorproduct for( idxi = 0; idxi < n; idxi++ ){ *(x+idxi) = 1.0; *(y+idxi) = 1.0; } for( idxi = 0; idxi < m; idxi++ ) for( idxj = 0; idxj < n; idxj++) *(A+idxi*m+idxj) = (double)(idxi+1) + idxj; dgemv(&trans, &m, &n, &alpha, A, &lda, x, &incx, &beta, y, &incy); return 0; } Intel MKL Quickstart

  45. ?gemv • Computesamatrix-vectorproductusingageneralmatrix. • Description:Theroutineisdeclaredin • Fortran77:mkl_blas.fi • Fortran95:blas.f90 • C:mkl_blas.h • InputParameters dgemv(&trans,&m,&n,&alpha,A,&lda,x,&incx,&beta,y,&incy) • trans: iftrans=‘N’,‘n’,then iftrans=‘T’,‘t’,then iftrans=‘C’,‘c’,then • m: ThenumberofrowsofthematrixA. Intel MKL Quickstart

  46. ?gemv • InputParameters • n: Thenumberofcolumnsofthematrix • lda:Thefirstdimensionofmatrix,lda=max(1,m) • incx: Specifies the increment for the elements of x • incy: Specifies the increment for the elements of y • outputParameters • y:Updatedvectory. Intel MKL Quickstart

  47. Ex2. Result Introduction to MATLAB

  48. BLASLevel2Routines Intel MKL Quickstart

  49. Example • Brief examples to • BLASLevel1Routines(vector-vectoroperations) • BLASLevel2Routines(matrix-vectoroperations) • BLASLevel3Routines(matrix-matrixoperations) • Compute the LU factorization of a matrix(LAPACK) • Solve linear system(LAPACK) • Solve eigensystem(LAPACK) • Fast Fourier Transforms Intel MKL Quickstart

  50. Ex3-1.Matrix-Matrixproduct #include "mkl_blas.h” intmain() { intm, n, k, lda, ldb, ldc, idxi, idxj; doublealpha, beta, *A, *B, *C ; chartransa, transb; m = 3; n = 3; k = 3; lda= m; ldb= k; ldc = m; alpha = 1.0; beta = 1.0; transa ='n’; transb= 'n’; Intel MKL Quickstart

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