Actual source code: mpiaijcusp.cu
petsc-3.7.1 2016-05-15
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      1: #define PETSC_SKIP_COMPLEX
      2: #define PETSC_SKIP_SPINLOCK

      4: #include <petscconf.h>
      5: #include <../src/mat/impls/aij/mpi/mpiaij.h>   /*I "petscmat.h" I*/
      6: #include <../src/mat/impls/aij/mpi/mpicusp/mpicuspmatimpl.h>

     10: PetscErrorCode  MatMPIAIJSetPreallocation_MPIAIJCUSP(Mat B,PetscInt d_nz,const PetscInt d_nnz[],PetscInt o_nz,const PetscInt o_nnz[])
     11: {
     12:   Mat_MPIAIJ *b = (Mat_MPIAIJ*)B->data;
     13:   Mat_MPIAIJCUSP * cuspStruct = (Mat_MPIAIJCUSP*)b->spptr;
     15:   PetscInt       i;

     18:   PetscLayoutSetUp(B->rmap);
     19:   PetscLayoutSetUp(B->cmap);
     20:   if (d_nnz) {
     21:     for (i=0; i<B->rmap->n; i++) {
     22:       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]);
     23:     }
     24:   }
     25:   if (o_nnz) {
     26:     for (i=0; i<B->rmap->n; i++) {
     27:       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]);
     28:     }
     29:   }
     30:   if (!B->preallocated) {
     31:     /* Explicitly create 2 MATSEQAIJCUSP matrices. */
     32:     MatCreate(PETSC_COMM_SELF,&b->A);
     33:     MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
     34:     MatSetType(b->A,MATSEQAIJCUSP);
     35:     PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
     36:     MatCreate(PETSC_COMM_SELF,&b->B);
     37:     MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
     38:     MatSetType(b->B,MATSEQAIJCUSP);
     39:     PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);
     40:   }
     41:   MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
     42:   MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
     43:   MatCUSPSetFormat(b->A,MAT_CUSP_MULT,cuspStruct->diagGPUMatFormat);
     44:   MatCUSPSetFormat(b->B,MAT_CUSP_MULT,cuspStruct->offdiagGPUMatFormat);
     45:   MatCUSPSetStream(b->A,cuspStruct->stream);
     46:   MatCUSPSetStream(b->B,cuspStruct->stream);
     47:   B->preallocated = PETSC_TRUE;
     48:   return(0);
     49: }

     53: PetscErrorCode  MatCreateVecs_MPIAIJCUSP(Mat mat,Vec *right,Vec *left)
     54: {
     56:   PetscInt rbs,cbs;

     59:   MatGetBlockSizes(mat,&rbs,&cbs);
     60:   if (right) {
     61:     VecCreate(PetscObjectComm((PetscObject)mat),right);
     62:     VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
     63:     VecSetBlockSize(*right,cbs);
     64:     VecSetType(*right,VECCUSP);
     65:     VecSetLayout(*right,mat->cmap);
     66:   }
     67:   if (left) {
     68:     VecCreate(PetscObjectComm((PetscObject)mat),left);
     69:     VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
     70:     VecSetBlockSize(*left,rbs);
     71:     VecSetType(*left,VECCUSP);
     72:     VecSetLayout(*left,mat->rmap);
     73:   }
     74:   return(0);
     75: }

     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: }

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

    113: PetscErrorCode MatCUSPSetFormat_MPIAIJCUSP(Mat A,MatCUSPFormatOperation op,MatCUSPStorageFormat format)
    114: {
    115:   Mat_MPIAIJ     *a           = (Mat_MPIAIJ*)A->data;
    116:   Mat_MPIAIJCUSP * cuspStruct = (Mat_MPIAIJCUSP*)a->spptr;

    119:   switch (op) {
    120:   case MAT_CUSP_MULT_DIAG:
    121:     cuspStruct->diagGPUMatFormat = format;
    122:     break;
    123:   case MAT_CUSP_MULT_OFFDIAG:
    124:     cuspStruct->offdiagGPUMatFormat = format;
    125:     break;
    126:   case MAT_CUSP_ALL:
    127:     cuspStruct->diagGPUMatFormat    = format;
    128:     cuspStruct->offdiagGPUMatFormat = format;
    129:     break;
    130:   default:
    131:     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);
    132:   }
    133:   return(0);
    134: }

    138: PetscErrorCode MatSetFromOptions_MPIAIJCUSP(PetscOptionItems *PetscOptionsObject,Mat A)
    139: {
    140:   MatCUSPStorageFormat format;
    141:   PetscErrorCode       ierr;
    142:   PetscBool            flg;
    143:   Mat_MPIAIJ           *a = (Mat_MPIAIJ*)A->data;
    144:   Mat_MPIAIJCUSP       *cuspStruct = (Mat_MPIAIJCUSP*)a->spptr;

    147:   MatSetFromOptions_MPIAIJ(PetscOptionsObject,A);

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

    174: PetscErrorCode MatDestroy_MPIAIJCUSP(Mat A)
    175: {
    177:   Mat_MPIAIJ     *a           = (Mat_MPIAIJ*)A->data;
    178:   Mat_MPIAIJCUSP *cuspStruct = (Mat_MPIAIJCUSP*)a->spptr;
    179:   cudaError_t    err=cudaSuccess;

    182:   try {
    183:     err = cudaStreamDestroy(cuspStruct->stream);
    184:     if (err!=cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Mat_MPIAIJCUSP error: %s", cudaGetErrorString(err));
    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:   MatDestroy_MPIAIJ(A);
    191:   return(0);
    192: }

    196: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJCUSP(Mat A)
    197: {
    199:   Mat_MPIAIJ     *a;
    200:   Mat_MPIAIJCUSP * cuspStruct;
    201:   cudaError_t    err=cudaSuccess;

    204:   MatCreate_MPIAIJ(A);
    205:   PetscObjectComposeFunction((PetscObject)A,"MatMPIAIJSetPreallocation_C",MatMPIAIJSetPreallocation_MPIAIJCUSP);
    206:   A->ops->getvecs        = MatCreateVecs_MPIAIJCUSP;
    207:   A->ops->setvaluesbatch = MatSetValuesBatch_MPIAIJCUSP;

    209:   a          = (Mat_MPIAIJ*)A->data;
    210:   a->spptr   = new Mat_MPIAIJCUSP;
    211:   cuspStruct = (Mat_MPIAIJCUSP*)a->spptr;

    213:   cuspStruct->diagGPUMatFormat    = MAT_CUSP_CSR;
    214:   cuspStruct->offdiagGPUMatFormat = MAT_CUSP_CSR;
    215:   err = cudaStreamCreate(&(cuspStruct->stream));
    216:   if (err!=cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Mat_MPIAIJCUSP error: %s", cudaGetErrorString(err));

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

    222:   PetscObjectComposeFunction((PetscObject)A,"MatCUSPSetFormat_C", MatCUSPSetFormat_MPIAIJCUSP);
    223:   PetscObjectChangeTypeName((PetscObject)A,MATMPIAIJCUSP);
    224:   return(0);
    225: }

    228: /*@
    229:    MatCreateAIJCUSP - Creates a sparse matrix in AIJ (compressed row) format
    230:    (the default parallel PETSc format).  This matrix will ultimately pushed down
    231:    to NVidia GPUs and use the CUSP library for calculations. For good matrix
    232:    assembly performance the user should preallocate the matrix storage by setting
    233:    the parameter nz (or the array nnz).  By setting these parameters accurately,
    234:    performance during matrix assembly can be increased by more than a factor of 50.

    237:    Collective on MPI_Comm

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

    247:    Output Parameter:
    248: .  A - the matrix

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

    254:    Notes:
    255:    If nnz is given then nz is ignored

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

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

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

    272:    Level: intermediate

    274: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATMPIAIJCUSP, MATAIJCUSP
    275: @*/
    278: 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)
    279: {
    281:   PetscMPIInt    size;

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

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

    300:    A matrix type type whose data resides on Nvidia GPUs. These matrices can be CSR format.
    301:    All matrix calculations are performed using the CUSP library. DIA and ELL
    302:    formats are also available

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

    310:    Options Database Keys:
    311: +  -mat_type mpiaijcusp - sets the matrix type to "mpiaijcusp" during a call to MatSetFromOptions()
    312: .  -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).
    313: .  -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).
    314: -  -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).

    316:   Level: beginner

    318:  .seealso: MatCreateAIJCUSP(), MATSEQAIJCUSP, MatCreateSeqAIJCUSP(), MatCUSPSetFormat(), MatCUSPStorageFormat, MatCUSPFormatOperation
    319: M*/