Actual source code: mpiaijcusparse.cu

petsc-dev 2014-02-02
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  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 <mpicusparsematimpl.h>


 10: PetscErrorCode  MatMPIAIJSetPreallocation_MPIAIJCUSPARSE(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_MPIAIJCUSPARSE * cusparseStruct = (Mat_MPIAIJCUSPARSE*)b->spptr;
 14:   PetscErrorCode     ierr;
 15:   PetscInt           i;

 18:   if (d_nz == PETSC_DEFAULT || d_nz == PETSC_DECIDE) d_nz = 5;
 19:   if (o_nz == PETSC_DEFAULT || o_nz == PETSC_DECIDE) o_nz = 2;
 20:   if (d_nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"d_nz cannot be less than 0: value %D",d_nz);
 21:   if (o_nz < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"o_nz cannot be less than 0: value %D",o_nz);

 23:   PetscLayoutSetUp(B->rmap);
 24:   PetscLayoutSetUp(B->cmap);
 25:   if (d_nnz) {
 26:     for (i=0; i<B->rmap->n; i++) {
 27:       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]);
 28:     }
 29:   }
 30:   if (o_nnz) {
 31:     for (i=0; i<B->rmap->n; i++) {
 32:       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]);
 33:     }
 34:   }
 35:   if (!B->preallocated) {
 36:     /* Explicitly create 2 MATSEQAIJCUSPARSE matrices. */
 37:     MatCreate(PETSC_COMM_SELF,&b->A);
 38:     MatSetSizes(b->A,B->rmap->n,B->cmap->n,B->rmap->n,B->cmap->n);
 39:     MatSetType(b->A,MATSEQAIJCUSPARSE);
 40:     PetscLogObjectParent((PetscObject)B,(PetscObject)b->A);
 41:     MatCreate(PETSC_COMM_SELF,&b->B);
 42:     MatSetSizes(b->B,B->rmap->n,B->cmap->N,B->rmap->n,B->cmap->N);
 43:     MatSetType(b->B,MATSEQAIJCUSPARSE);
 44:     PetscLogObjectParent((PetscObject)B,(PetscObject)b->B);
 45:   }
 46:   MatSeqAIJSetPreallocation(b->A,d_nz,d_nnz);
 47:   MatSeqAIJSetPreallocation(b->B,o_nz,o_nnz);
 48:   MatCUSPARSESetFormat(b->A,MAT_CUSPARSE_MULT,cusparseStruct->diagGPUMatFormat);
 49:   MatCUSPARSESetFormat(b->B,MAT_CUSPARSE_MULT,cusparseStruct->offdiagGPUMatFormat);
 50:   MatCUSPARSESetHandle(b->A,cusparseStruct->handle);
 51:   MatCUSPARSESetHandle(b->B,cusparseStruct->handle);
 52:   MatCUSPARSESetStream(b->A,cusparseStruct->stream);
 53:   MatCUSPARSESetStream(b->B,cusparseStruct->stream);

 55:   B->preallocated = PETSC_TRUE;
 56:   return(0);
 57: }

 61: PetscErrorCode  MatGetVecs_MPIAIJCUSPARSE(Mat mat,Vec *right,Vec *left)
 62: {

 66:   if (right) {
 67:     VecCreate(PetscObjectComm((PetscObject)mat),right);
 68:     VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
 69:     VecSetBlockSize(*right,mat->rmap->bs);
 70:     VecSetType(*right,VECCUSP);
 71:     VecSetLayout(*right,mat->cmap);
 72:   }
 73:   if (left) {
 74:     VecCreate(PetscObjectComm((PetscObject)mat),left);
 75:     VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
 76:     VecSetBlockSize(*left,mat->rmap->bs);
 77:     VecSetType(*left,VECCUSP);
 78:     VecSetLayout(*left,mat->rmap);


 81:   }
 82:   return(0);
 83: }


 88: PetscErrorCode MatMult_MPIAIJCUSPARSE(Mat A,Vec xx,Vec yy)
 89: {
 90:   /* This multiplication sequence is different sequence
 91:      than the CPU version. In particular, the diagonal block
 92:      multiplication kernel is launched in one stream. Then,
 93:      in a separate stream, the data transfers from DeviceToHost
 94:      (with MPI messaging in between), then HostToDevice are
 95:      launched. Once the data transfer stream is synchronized,
 96:      to ensure messaging is complete, the MatMultAdd kernel
 97:      is launched in the original (MatMult) stream to protect
 98:      against race conditions.

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

106:   VecGetLocalSize(xx,&nt);
107:   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);
108:   VecScatterInitializeForGPU(a->Mvctx,xx,SCATTER_FORWARD);
109:   (*a->A->ops->mult)(a->A,xx,yy);
110:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
111:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
112:   (*a->B->ops->multadd)(a->B,a->lvec,yy,yy);
113:   VecScatterFinalizeForGPU(a->Mvctx);
114:   return(0);
115: }

119: PetscErrorCode MatMultTranspose_MPIAIJCUSPARSE(Mat A,Vec xx,Vec yy)
120: {
121:   /* This multiplication sequence is different sequence
122:      than the CPU version. In particular, the diagonal block
123:      multiplication kernel is launched in one stream. Then,
124:      in a separate stream, the data transfers from DeviceToHost
125:      (with MPI messaging in between), then HostToDevice are
126:      launched. Once the data transfer stream is synchronized,
127:      to ensure messaging is complete, the MatMultAdd kernel
128:      is launched in the original (MatMult) stream to protect
129:      against race conditions.

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

137:   VecGetLocalSize(xx,&nt);
138:   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);
139:   VecScatterInitializeForGPU(a->Mvctx,xx,SCATTER_FORWARD);
140:   (*a->A->ops->multtranspose)(a->A,xx,yy);
141:   VecScatterBegin(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
142:   VecScatterEnd(a->Mvctx,xx,a->lvec,INSERT_VALUES,SCATTER_FORWARD);
143:   (*a->B->ops->multtransposeadd)(a->B,a->lvec,yy,yy);
144:   VecScatterFinalizeForGPU(a->Mvctx);
145:   return(0);
146: }

150: PetscErrorCode MatCUSPARSESetFormat_MPIAIJCUSPARSE(Mat A,MatCUSPARSEFormatOperation op,MatCUSPARSEStorageFormat format)
151: {
152:   Mat_MPIAIJ         *a               = (Mat_MPIAIJ*)A->data;
153:   Mat_MPIAIJCUSPARSE * cusparseStruct = (Mat_MPIAIJCUSPARSE*)a->spptr;

156:   switch (op) {
157:   case MAT_CUSPARSE_MULT_DIAG:
158:     cusparseStruct->diagGPUMatFormat = format;
159:     break;
160:   case MAT_CUSPARSE_MULT_OFFDIAG:
161:     cusparseStruct->offdiagGPUMatFormat = format;
162:     break;
163:   case MAT_CUSPARSE_ALL:
164:     cusparseStruct->diagGPUMatFormat    = format;
165:     cusparseStruct->offdiagGPUMatFormat = format;
166:     break;
167:   default:
168:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unsupported operation %d for MatCUSPARSEFormatOperation. Only MAT_CUSPARSE_MULT_DIAG, MAT_CUSPARSE_MULT_DIAG, and MAT_CUSPARSE_MULT_ALL are currently supported.",op);
169:   }
170:   return(0);
171: }

175: PetscErrorCode MatSetFromOptions_MPIAIJCUSPARSE(Mat A)
176: {
177:   MatCUSPARSEStorageFormat format;
178:   PetscErrorCode           ierr;
179:   PetscBool                flg;

182:   PetscOptionsHead("MPIAIJCUSPARSE options");
183:   PetscObjectOptionsBegin((PetscObject)A);
184:   if (A->factortype==MAT_FACTOR_NONE) {
185:     PetscOptionsEnum("-mat_cusparse_mult_diag_storage_format","sets storage format of the diagonal blocks of (mpi)aijcusparse gpu matrices for SpMV",
186:                             "MatCUSPARSESetFormat",MatCUSPARSEStorageFormats,(PetscEnum)MAT_CUSPARSE_CSR,(PetscEnum*)&format,&flg);
187:     if (flg) {
188:       MatCUSPARSESetFormat(A,MAT_CUSPARSE_MULT_DIAG,format);
189:     }
190:     PetscOptionsEnum("-mat_cusparse_mult_offdiag_storage_format","sets storage format of the off-diagonal blocks (mpi)aijcusparse gpu matrices for SpMV",
191:                             "MatCUSPARSESetFormat",MatCUSPARSEStorageFormats,(PetscEnum)MAT_CUSPARSE_CSR,(PetscEnum*)&format,&flg);
192:     if (flg) {
193:       MatCUSPARSESetFormat(A,MAT_CUSPARSE_MULT_OFFDIAG,format);
194:     }
195:     PetscOptionsEnum("-mat_cusparse_storage_format","sets storage format of the diagonal and off-diagonal blocks (mpi)aijcusparse gpu matrices for SpMV",
196:                             "MatCUSPARSESetFormat",MatCUSPARSEStorageFormats,(PetscEnum)MAT_CUSPARSE_CSR,(PetscEnum*)&format,&flg);
197:     if (flg) {
198:       MatCUSPARSESetFormat(A,MAT_CUSPARSE_ALL,format);
199:     }
200:   }
201:   PetscOptionsEnd();
202:   return(0);
203: }

207: PetscErrorCode MatDestroy_MPIAIJCUSPARSE(Mat A)
208: {
209:   PetscErrorCode     ierr;
210:   Mat_MPIAIJ         *a              = (Mat_MPIAIJ*)A->data;
211:   Mat_MPIAIJCUSPARSE *cusparseStruct = (Mat_MPIAIJCUSPARSE*)a->spptr;
212:   cudaError_t        err;
213:   cusparseStatus_t   stat;

216:   try {
217:     MatCUSPARSEClearHandle(a->A);
218:     MatCUSPARSEClearHandle(a->B);
219:     stat = cusparseDestroy(cusparseStruct->handle);CHKERRCUSP(stat);
220:     err = cudaStreamDestroy(cusparseStruct->stream);CHKERRCUSP(err);
221:     delete cusparseStruct;
222:   } catch(char *ex) {
223:     SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"Mat_MPIAIJCUSPARSE error: %s", ex);
224:   }
225:   cusparseStruct = 0;

227:   MatDestroy_MPIAIJ(A);
228:   return(0);
229: }

233: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJCUSPARSE(Mat A)
234: {
235:   PetscErrorCode     ierr;
236:   Mat_MPIAIJ         *a;
237:   Mat_MPIAIJCUSPARSE * cusparseStruct;
238:   cudaError_t        err;
239:   cusparseStatus_t   stat;

242:   MatCreate_MPIAIJ(A);
243:   PetscObjectComposeFunction((PetscObject)A,"MatMPIAIJSetPreallocation_C",MatMPIAIJSetPreallocation_MPIAIJCUSPARSE);
244:   a        = (Mat_MPIAIJ*)A->data;
245:   a->spptr = new Mat_MPIAIJCUSPARSE;

247:   cusparseStruct                      = (Mat_MPIAIJCUSPARSE*)a->spptr;
248:   cusparseStruct->diagGPUMatFormat    = MAT_CUSPARSE_CSR;
249:   cusparseStruct->offdiagGPUMatFormat = MAT_CUSPARSE_CSR;
250:   stat = cusparseCreate(&(cusparseStruct->handle));CHKERRCUSP(stat);
251:   err = cudaStreamCreate(&(cusparseStruct->stream));CHKERRCUSP(err);

253:   A->ops->getvecs        = MatGetVecs_MPIAIJCUSPARSE;
254:   A->ops->mult           = MatMult_MPIAIJCUSPARSE;
255:   A->ops->multtranspose  = MatMultTranspose_MPIAIJCUSPARSE;
256:   A->ops->setfromoptions = MatSetFromOptions_MPIAIJCUSPARSE;
257:   A->ops->destroy        = MatDestroy_MPIAIJCUSPARSE;

259:   PetscObjectChangeTypeName((PetscObject)A,MATMPIAIJCUSPARSE);
260:   PetscObjectComposeFunction((PetscObject)A,"MatCUSPARSESetFormat_C",  MatCUSPARSESetFormat_MPIAIJCUSPARSE);
261:   return(0);
262: }

264: /*@
265:    MatCreateAIJCUSPARSE - Creates a sparse matrix in AIJ (compressed row) format
266:    (the default parallel PETSc format).  This matrix will ultimately pushed down
267:    to NVidia GPUs and use the CUSPARSE library for calculations. For good matrix
268:    assembly performance the user should preallocate the matrix storage by setting
269:    the parameter nz (or the array nnz).  By setting these parameters accurately,
270:    performance during matrix assembly can be increased by more than a factor of 50.

272:    Collective on MPI_Comm

274:    Input Parameters:
275: +  comm - MPI communicator, set to PETSC_COMM_SELF
276: .  m - number of rows
277: .  n - number of columns
278: .  nz - number of nonzeros per row (same for all rows)
279: -  nnz - array containing the number of nonzeros in the various rows
280:          (possibly different for each row) or NULL

282:    Output Parameter:
283: .  A - the matrix

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

289:    Notes:
290:    If nnz is given then nz is ignored

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

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

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

307:    Level: intermediate

309: .seealso: MatCreate(), MatCreateAIJ(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ(), MATMPIAIJCUSPARSE, MATAIJCUSPARSE
310: @*/
313: PetscErrorCode  MatCreateAIJCUSPARSE(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)
314: {
316:   PetscMPIInt    size;

319:   MatCreate(comm,A);
320:   MatSetSizes(*A,m,n,M,N);
321:   MPI_Comm_size(comm,&size);
322:   if (size > 1) {
323:     MatSetType(*A,MATMPIAIJCUSPARSE);
324:     MatMPIAIJSetPreallocation(*A,d_nz,d_nnz,o_nz,o_nnz);
325:   } else {
326:     MatSetType(*A,MATSEQAIJCUSPARSE);
327:     MatSeqAIJSetPreallocation(*A,d_nz,d_nnz);
328:   }
329:   return(0);
330: }

332: /*M
333:    MATAIJCUSPARSE - MATMPIAIJCUSPARSE = "aijcusparse" = "mpiaijcusparse" - A matrix type to be used for sparse matrices.

335:    A matrix type type whose data resides on Nvidia GPUs. These matrices can be in either
336:    CSR, ELL, or Hybrid format. The ELL and HYB formats require CUDA 4.2 or later.
337:    All matrix calculations are performed on Nvidia GPUs using the CUSPARSE library.

339:    This matrix type is identical to MATSEQAIJCUSPARSE when constructed with a single process communicator,
340:    and MATMPIAIJCUSPARSE otherwise.  As a result, for single process communicators,
341:    MatSeqAIJSetPreallocation is supported, and similarly MatMPIAIJSetPreallocation is supported
342:    for communicators controlling multiple processes.  It is recommended that you call both of
343:    the above preallocation routines for simplicity.

345:    Options Database Keys:
346: +  -mat_type mpiaijcusparse - sets the matrix type to "mpiaijcusparse" during a call to MatSetFromOptions()
347: .  -mat_cusparse_storage_format csr - sets the storage format of diagonal and off-diagonal matrices during a call to MatSetFromOptions(). Other options include ell (ellpack) or hyb (hybrid).
348: .  -mat_cusparse_mult_diag_storage_format csr - sets the storage format of diagonal matrix during a call to MatSetFromOptions(). Other options include ell (ellpack) or hyb (hybrid).
349: -  -mat_cusparse_mult_offdiag_storage_format csr - sets the storage format of off-diagonal matrix during a call to MatSetFromOptions(). Other options include ell (ellpack) or hyb (hybrid).

351:   Level: beginner

353:  .seealso: MatCreateAIJCUSPARSE(), MATSEQAIJCUSPARSE, MatCreateSeqAIJCUSPARSE(), MatCUSPARSESetFormat(), MatCUSPARSEStorageFormat, MatCUSPARSEFormatOperation
354: M
355: M*/