Date: 2013-07-02T14:42:18+00:00

Actual source code: mpiaijcusp.cu
petsc-3.4.2 2013-07-02

      1: #include "petscconf.h"
      2: PETSC_CUDA_EXTERN_C_BEGIN
      3: #include <../src/mat/impls/aij/mpi/mpiaij.h>   /*I "petscmat.h" I*/
      4: PETSC_CUDA_EXTERN_C_END
     5:  #include mpicuspmatimpl.h

      9: PetscErrorCode  MatMPIAIJSetPreallocation_MPIAIJCUSP(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
     10: {
     11:   Mat_MPIAIJ *b = (Mat_MPIAIJ*)B->data;
     12: #if defined(PETSC_HAVE_TXPETSCGPU)
     13:   Mat_MPIAIJCUSP * cuspStruct = (Mat_MPIAIJCUSP*)b->spptr;
     14: #endif
     16:   PetscInt       i;

     19:   PetscLayoutSetUp(B->rmap);
     20:   PetscLayoutSetUp(B->cmap);
     21:   if (d_nnz) {
     22:     for (i=0; i<B->rmap->n; i++) {
     23:       if (d_nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"d_nnz cannot be less than 0: local row %D value %D",i,d_nnz[i]);
     24:     }
     25:   }
     26:   if (o_nnz) {
     27:     for (i=0; i<B->rmap->n; i++) {
     28:       if (o_nnz[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"o_nnz cannot be less than 0: local row %D value %D",i,o_nnz[i]);
     29:     }
     30:   }
     31:   if (!B->preallocated) {
     32:     /* Explicitly create 2 MATSEQAIJCUSP matrices. */
     33:     MatCreate(PETSC_COMM_SELF,&b->A);
     34:     MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
     35:     MatSetType(b->A,MATSEQAIJCUSP);
     36:     PetscLogObjectParent(B,b->A);
     37:     MatCreate(PETSC_COMM_SELF,&b->B);
     38:     MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
     39:     MatSetType(b->B,MATSEQAIJCUSP);
     40:     PetscLogObjectParent(B,b->B);
     41:   }
     42:   MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
     43:   MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
     44: #if defined(PETSC_HAVE_TXPETSCGPU)
     45:   ierr=MatCUSPSetFormat(b->A,MAT_CUSP_MULT,cuspStruct->diagGPUMatFormat);
     46:   ierr=MatCUSPSetFormat(b->B,MAT_CUSP_MULT,cuspStruct->offdiagGPUMatFormat);
     47: #endif
     48:   B->preallocated = PETSC_TRUE;
     49:   return(0);
     50: }

     54: PetscErrorCode  MatGetVecs_MPIAIJCUSP(Mat mat,Vec *right,Vec *left)
     55: {

     59:   if (right) {
     60:     VecCreate(PetscObjectComm((PetscObject)mat),right);
     61:     VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
     62:     VecSetBlockSize(*right,mat->rmap->bs);
     63:     VecSetType(*right,VECCUSP);
     64:     VecSetLayout(*right,mat->cmap);
     65:   }
     66:   if (left) {
     67:     VecCreate(PetscObjectComm((PetscObject)mat),left);
     68:     VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
     69:     VecSetBlockSize(*left,mat->rmap->bs);
     70:     VecSetType(*left,VECCUSP);
     71:     VecSetLayout(*left,mat->rmap);
     72:   }
     73:   return(0);
     74: }

     77: #if defined(PETSC_HAVE_TXPETSCGPU)
     80: PetscErrorCode MatMult_MPIAIJCUSP(Mat A,Vec xx,Vec yy)
     81: {
     82:   /* This multiplication sequence is different sequence
     83:      than the CPU version. In particular, the diagonal block
     84:      multiplication kernel is launched in one stream. Then,
     85:      in a separate stream, the data transfers from DeviceToHost
     86:      (with MPI messaging in between), then HostToDevice are
     87:      launched. Once the data transfer stream is synchronized,
     88:      to ensure messaging is complete, the MatMultAdd kernel
     89:      is launched in the original (MatMult) stream to protect
     90:      against race conditions.

     92:      This sequence should only be called for GPU computation. */
     93:   Mat_MPIAIJ     *a = (Mat_MPIAIJ*)A->data;
     95:   PetscInt       nt;

     98:   VecGetLocalSize(xx,&nt);
     99:   if (nt != A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Incompatible partition of A (%D) and xx (%D)",A->cmap->n,nt);
    100:   VecScatterInitializeForGPU(a->Mvctx,xx,SCATTER_FORWARD);
    101:   (*a->A->ops->mult)(a->A,xx,yy);
    102:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    103:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
    104:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
    105:   VecScatterFinalizeForGPU(a->Mvctx);
    106:   return(0);
    107: }
    108: #endif

    110: PetscErrorCode MatSetValuesBatch_MPIAIJCUSP(Mat J, PetscInt Ne, PetscInt Nl, PetscInt *elemRows, const PetscScalar *elemMats);

    112: #if defined(PETSC_HAVE_TXPETSCGPU)

    116: PetscErrorCode MatCUSPSetFormat_MPIAIJCUSP(Mat A,MatCUSPFormatOperation op,MatCUSPStorageFormat format)
    117: {
    118:   Mat_MPIAIJ     *a           = (Mat_MPIAIJ*)A->data;
    119:   Mat_MPIAIJCUSP * cuspStruct = (Mat_MPIAIJCUSP*)a->spptr;

    122:   switch (op) {
    123:   case MAT_CUSP_MULT_DIAG:
    124:     cuspStruct->diagGPUMatFormat = format;
    125:     break;
    126:   case MAT_CUSP_MULT_OFFDIAG:
    127:     cuspStruct->offdiagGPUMatFormat = format;
    128:     break;
    129:   case MAT_CUSP_ALL:
    130:     cuspStruct->diagGPUMatFormat    = format;
    131:     cuspStruct->offdiagGPUMatFormat = format;
    132:     break;
    133:   default:
    134:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unsupported operation %d for MatCUSPFormatOperation. Only MAT_CUSP_MULT_DIAG, MAT_CUSP_MULT_DIAG, and MAT_CUSP_MULT_ALL are currently supported.",op);
    135:   }
    136:   return(0);
    137: }

    141: PetscErrorCode MatSetFromOptions_MPIAIJCUSP(Mat A)
    142: {
    143:   MatCUSPStorageFormat format;
    144:   PetscErrorCode       ierr;
    145:   PetscBool            flg;

    148:   PetscOptionsHead("MPIAIJCUSP options");
    149:   PetscObjectOptionsBegin((PetscObject)A);
    150:   if (A->factortype==MAT_FACTOR_NONE) {
    151:     PetscOptionsEnum("-mat_cusp_mult_diag_storage_format","sets storage format of the diagonal blocks of (mpi)aijcusp gpu matrices for SpMV",
    152:                             "MatCUSPSetFormat",MatCUSPStorageFormats,(PetscEnum)MAT_CUSP_CSR,(PetscEnum*)&format,&flg);
    153:     if (flg) {
    154:       MatCUSPSetFormat(A,MAT_CUSP_MULT_DIAG,format);
    155:     }
    156:     PetscOptionsEnum("-mat_cusp_mult_offdiag_storage_format","sets storage format of the off-diagonal blocks (mpi)aijcusp gpu matrices for SpMV",
    157:                             "MatCUSPSetFormat",MatCUSPStorageFormats,(PetscEnum)MAT_CUSP_CSR,(PetscEnum*)&format,&flg);
    158:     if (flg) {
    159:       MatCUSPSetFormat(A,MAT_CUSP_MULT_OFFDIAG,format);
    160:     }
    161:     PetscOptionsEnum("-mat_cusp_storage_format","sets storage format of the diagonal and off-diagonal blocks (mpi)aijcusp gpu matrices for SpMV",
    162:                             "MatCUSPSetFormat",MatCUSPStorageFormats,(PetscEnum)MAT_CUSP_CSR,(PetscEnum*)&format,&flg);
    163:     if (flg) {
    164:       MatCUSPSetFormat(A,MAT_CUSP_ALL,format);
    165:     }
    166:   }
    167:   PetscOptionsEnd();
    168:   return(0);
    169: }
    170: #endif

    174: PetscErrorCode MatDestroy_MPIAIJCUSP(Mat A)
    175: {
    177: #if defined(PETSC_HAVE_TXPETSCGPU)
    178:   Mat_MPIAIJ     *a           = (Mat_MPIAIJ*)A->data;
    179:   Mat_MPIAIJCUSP * cuspStruct = (Mat_MPIAIJCUSP*)a->spptr;
    180: #endif

    183: #if defined(PETSC_HAVE_TXPETSCGPU)
    184:   try {
    185:     delete cuspStruct;
    186:   } catch(char *ex) {
    187:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Mat_MPIAIJCUSP error: %s", ex);
    188:   }
    189:   cuspStruct = 0;
    190: #endif
    191:   MatDestroy_MPIAIJ(A);
    192:   return(0);
    193: }

    197: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJCUSP(Mat A)
    198: {
    200: #if defined(PETSC_HAVE_TXPETSCGPU)
    201:   Mat_MPIAIJ     *a;
    202:   Mat_MPIAIJCUSP * cuspStruct;
    203: #endif

    206:   MatCreate_MPIAIJ(A);
    207:   PetscObjectComposeFunction((PetscObject)A,"MatMPIAIJSetPreallocation_C",MatMPIAIJSetPreallocation_MPIAIJCUSP);
    208:   A->ops->getvecs        = MatGetVecs_MPIAIJCUSP;
    209:   A->ops->setvaluesbatch = MatSetValuesBatch_MPIAIJCUSP;

    211: #if defined(PETSC_HAVE_TXPETSCGPU)
    212:   a          = (Mat_MPIAIJ*)A->data;
    213:   a->spptr   = new Mat_MPIAIJCUSP;
    214:   cuspStruct = (Mat_MPIAIJCUSP*)a->spptr;

    216:   cuspStruct->diagGPUMatFormat    = MAT_CUSP_CSR;
    217:   cuspStruct->offdiagGPUMatFormat = MAT_CUSP_CSR;

    219:   A->ops->mult           = MatMult_MPIAIJCUSP;
    220:   A->ops->setfromoptions = MatSetFromOptions_MPIAIJCUSP;
    221:   A->ops->destroy        = MatDestroy_MPIAIJCUSP;

    223:   PetscObjectComposeFunction((PetscObject)A,"MatCUSPSetFormat_C", MatCUSPSetFormat_MPIAIJCUSP);
    224: #endif
    225:   PetscObjectChangeTypeName((PetscObject)A,MATMPIAIJCUSP);
    226:   return(0);
    227: }

    230: /*@
    231:    MatCreateAIJCUSP - Creates a sparse matrix in AIJ (compressed row) format
    232:    (the default parallel PETSc format).  This matrix will ultimately pushed down
    233:    to NVidia GPUs and use the CUSP library for calculations. For good matrix
    234:    assembly performance the user should preallocate the matrix storage by setting
    235:    the parameter nz (or the array nnz).  By setting these parameters accurately,
    236:    performance during matrix assembly can be increased by more than a factor of 50.

    239:    Collective on MPI_Comm

    241:    Input Parameters:
    242: +  comm - MPI communicator, set to PETSC_COMM_SELF
    243: .  m - number of rows
    244: .  n - number of columns
    245: .  nz - number of nonzeros per row (same for all rows)
    246: -  nnz - array containing the number of nonzeros in the various rows
    247:          (possibly different for each row) or NULL

    249:    Output Parameter:
    250: .  A - the matrix

    252:    It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
    253:    MatXXXXSetPreallocation() paradigm instead of this routine directly.
    254:    [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]

    256:    Notes:
    257:    If nnz is given then nz is ignored

    259:    The AIJ format (also called the Yale sparse matrix format or
    260:    compressed row storage), is fully compatible with standard Fortran 77
    261:    storage.  That is, the stored row and column indices can begin at
    262:    either one (as in Fortran) or zero.  See the users' manual for details.

    264:    Specify the preallocated storage with either nz or nnz (not both).
    265:    Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
    266:    allocation.  For large problems you MUST preallocate memory or you
    267:    will get TERRIBLE performance, see the users' manual chapter on matrices.

    269:    By default, this format uses inodes (identical nodes) when possible, to
    270:    improve numerical efficiency of matrix-vector products and solves. We
    271:    search for consecutive rows with the same nonzero structure, thereby
    272:    reusing matrix information to achieve increased efficiency.

    274:    Level: intermediate

    276: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATMPIAIJCUSP, MATAIJCUSP
    277: @*/
    280: PetscErrorCode  MatCreateAIJCUSP(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt M,PetscInt N,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[],Mat *A)
    281: {
    283:   PetscMPIInt    size;

    286:   MatCreate(comm,A);
    287:   MatSetSizes(*A,m,n,M,N);
    288:   MPI_Comm_size(comm,&size);
    289:   if (size > 1) {
    290:     MatSetType(*A,MATMPIAIJCUSP);
    291:     MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
    292:   } else {
    293:     MatSetType(*A,MATSEQAIJCUSP);
    294:     MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
    295:   }
    296:   return(0);
    297: }

    299: /*M
    300:    MATAIJCUSP - MATMPIAIJCUSP= "aijcusp" = "mpiaijcusp" - A matrix type to be used for sparse matrices.

    302:    A matrix type type whose data resides on Nvidia GPUs. These matrices can be CSR format.
    303:    All matrix calculations are performed using the CUSP library. DIA and ELL
    304:    formats are ONLY available when using the 'txpetscgpu' package. Use --download-txpetscgpu
    305:    to build/install PETSc to use different GPU storage formats with CUSP matrix types.

    307:    This matrix type is identical to MATSEQAIJCUSP when constructed with a single process communicator,
    308:    and MATMPIAIJCUSP otherwise.  As a result, for single process communicators,
    309:    MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
    310:    for communicators controlling multiple processes.  It is recommended that you call both of
    311:    the above preallocation routines for simplicity.

    313:    Options Database Keys:
    314: +  -mat_type mpiaijcusp - sets the matrix type to "mpiaijcusp" during a call to MatSetFromOptions()
    315: .  -mat_cusp_storage_format csr - sets the storage format of diagonal and off-diagonal matrices during a call to MatSetFromOptions(). Other storage formats include dia (diagonal) or ell (ellpack) which are only available with 'txpetscgpu' package. Moreover this option is only available with the 'txpetscgpu' package.
    316: .  -mat_cusp_mult_diag_storage_format csr - sets the storage format of diagonal matrix during a call to MatSetFromOptions(). Other storage formats include dia (diagonal) or ell (ellpack) which are only available with 'txpetscgpu' package. Moreover this option is only available with the 'txpetscgpu' package.
    317: -  -mat_cusp_mult_offdiag_storage_format csr - sets the storage format of off-diagonal matrix during a call to MatSetFromOptions(). Other storage formats include dia (diagonal) or ell (ellpack) which are only available with 'txpetscgpu' package. Moreover this option is only available with the 'txpetscgpu' package.

    319:   Level: beginner

    321:  .seealso: MatCreateAIJCUSP(), MATSEQAIJCUSP, MatCreateSeqAIJCUSP(), MatCUSPSetFormat(), MatCUSPStorageFormat, MatCUSPFormatOperation
    322: M*/