Actual source code: aijviennacl.cxx
petsc-3.7.3 2016-08-01
3: /*
4: Defines the basic matrix operations for the AIJ (compressed row)
5: matrix storage format.
6: */
8: #include <petscconf.h>
9: #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/
10: #include <petscbt.h>
11: #include <../src/vec/vec/impls/dvecimpl.h>
12: #include <petsc/private/vecimpl.h>
14: #include <../src/mat/impls/aij/seq/seqviennacl/viennaclmatimpl.h>
17: #include <algorithm>
18: #include <vector>
19: #include <string>
21: #include "viennacl/linalg/prod.hpp"
25: PetscErrorCode MatViennaCLCopyToGPU(Mat A)
26: {
28: Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL*)A->spptr;
29: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
30: PetscErrorCode ierr;
34: if (A->rmap->n > 0 && A->cmap->n > 0) { //some OpenCL SDKs have issues with buffers of size 0
35: if (A->valid_GPU_matrix == PETSC_VIENNACL_UNALLOCATED || A->valid_GPU_matrix == PETSC_VIENNACL_CPU) {
36: PetscLogEventBegin(MAT_ViennaCLCopyToGPU,A,0,0,0);
38: try {
39: PetscObjectViennaCLSetFromOptions((PetscObject)A); /* Allows to set device type before allocating any objects */
40: if (a->compressedrow.use) {
41: if (!viennaclstruct->compressed_mat) viennaclstruct->compressed_mat = new ViennaCLCompressedAIJMatrix();
43: // Since PetscInt is different from cl_uint, we have to convert:
44: viennacl::backend::mem_handle dummy;
46: viennacl::backend::typesafe_host_array<unsigned int> row_buffer; row_buffer.raw_resize(dummy, a->compressedrow.nrows+1);
47: for (PetscInt i=0; i<=a->compressedrow.nrows; ++i)
48: row_buffer.set(i, (a->compressedrow.i)[i]);
50: viennacl::backend::typesafe_host_array<unsigned int> row_indices; row_indices.raw_resize(dummy, a->compressedrow.nrows);
51: for (PetscInt i=0; i<a->compressedrow.nrows; ++i)
52: row_indices.set(i, (a->compressedrow.rindex)[i]);
54: viennacl::backend::typesafe_host_array<unsigned int> col_buffer; col_buffer.raw_resize(dummy, a->nz);
55: for (PetscInt i=0; i<a->nz; ++i)
56: col_buffer.set(i, (a->j)[i]);
58: viennaclstruct->compressed_mat->set(row_buffer.get(), row_indices.get(), col_buffer.get(), a->a, A->rmap->n, A->cmap->n, a->compressedrow.nrows, a->nz);
59: } else {
60: if (!viennaclstruct->mat) viennaclstruct->mat = new ViennaCLAIJMatrix();
62: // Since PetscInt is in general different from cl_uint, we have to convert:
63: viennacl::backend::mem_handle dummy;
65: viennacl::backend::typesafe_host_array<unsigned int> row_buffer; row_buffer.raw_resize(dummy, A->rmap->n+1);
66: for (PetscInt i=0; i<=A->rmap->n; ++i)
67: row_buffer.set(i, (a->i)[i]);
69: viennacl::backend::typesafe_host_array<unsigned int> col_buffer; col_buffer.raw_resize(dummy, a->nz);
70: for (PetscInt i=0; i<a->nz; ++i)
71: col_buffer.set(i, (a->j)[i]);
73: viennaclstruct->mat->set(row_buffer.get(), col_buffer.get(), a->a, A->rmap->n, A->cmap->n, a->nz);
74: }
75: ViennaCLWaitForGPU();
76: } catch(std::exception const & ex) {
77: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what());
78: }
80: // Create temporary vector for v += A*x:
81: if (viennaclstruct->tempvec) {
82: if (viennaclstruct->tempvec->size() != static_cast<std::size_t>(A->rmap->n)) {
83: delete (ViennaCLVector*)viennaclstruct->tempvec;
84: viennaclstruct->tempvec = new ViennaCLVector(A->rmap->n);
85: } else {
86: viennaclstruct->tempvec->clear();
87: }
88: } else {
89: viennaclstruct->tempvec = new ViennaCLVector(A->rmap->n);
90: }
92: A->valid_GPU_matrix = PETSC_VIENNACL_BOTH;
94: PetscLogEventEnd(MAT_ViennaCLCopyToGPU,A,0,0,0);
95: }
96: }
97: return(0);
98: }
102: PetscErrorCode MatViennaCLCopyFromGPU(Mat A, const ViennaCLAIJMatrix *Agpu)
103: {
104: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
105: PetscInt m = A->rmap->n;
106: PetscErrorCode ierr;
110: if (A->valid_GPU_matrix == PETSC_VIENNACL_UNALLOCATED) {
111: try {
112: if (a->compressedrow.use) SETERRQ(PETSC_COMM_WORLD, PETSC_ERR_ARG_WRONG, "ViennaCL: Cannot handle row compression for GPU matrices");
113: else {
115: if ((PetscInt)Agpu->size1() != m) SETERRQ2(PETSC_COMM_WORLD, PETSC_ERR_ARG_SIZ, "GPU matrix has %d rows, should be %d", Agpu->size1(), m);
116: a->nz = Agpu->nnz();
117: a->maxnz = a->nz; /* Since we allocate exactly the right amount */
118: A->preallocated = PETSC_TRUE;
119: if (a->singlemalloc) {
120: if (a->a) {PetscFree3(a->a,a->j,a->i);}
121: } else {
122: if (a->i) {PetscFree(a->i);}
123: if (a->j) {PetscFree(a->j);}
124: if (a->a) {PetscFree(a->a);}
125: }
126: PetscMalloc3(a->nz,&a->a,a->nz,&a->j,m+1,&a->i);
127: PetscLogObjectMemory((PetscObject)A, a->nz*(sizeof(PetscScalar)+sizeof(PetscInt))+(m+1)*sizeof(PetscInt));
129: a->singlemalloc = PETSC_TRUE;
131: /* Setup row lengths */
132: if (a->imax) {PetscFree2(a->imax,a->ilen);}
133: PetscMalloc2(m,&a->imax,m,&a->ilen);
134: PetscLogObjectMemory((PetscObject)A, 2*m*sizeof(PetscInt));
136: /* Copy data back from GPU */
137: viennacl::backend::typesafe_host_array<unsigned int> row_buffer; row_buffer.raw_resize(Agpu->handle1(), Agpu->size1() + 1);
139: // copy row array
140: viennacl::backend::memory_read(Agpu->handle1(), 0, row_buffer.raw_size(), row_buffer.get());
141: (a->i)[0] = row_buffer[0];
142: for (PetscInt i = 0; i < (PetscInt)Agpu->size1(); ++i) {
143: (a->i)[i+1] = row_buffer[i+1];
144: a->imax[i] = a->ilen[i] = a->i[i+1] - a->i[i]; //Set imax[] and ilen[] arrays at the same time as i[] for better cache reuse
145: }
147: // copy column indices
148: viennacl::backend::typesafe_host_array<unsigned int> col_buffer; col_buffer.raw_resize(Agpu->handle2(), Agpu->nnz());
149: viennacl::backend::memory_read(Agpu->handle2(), 0, col_buffer.raw_size(), col_buffer.get());
150: for (PetscInt i=0; i < (PetscInt)Agpu->nnz(); ++i)
151: (a->j)[i] = col_buffer[i];
153: // copy nonzero entries directly to destination (no conversion required)
154: viennacl::backend::memory_read(Agpu->handle(), 0, sizeof(PetscScalar)*Agpu->nnz(), a->a);
156: ViennaCLWaitForGPU();
157: /* TODO: Once a->diag is moved out of MatAssemblyEnd(), invalidate it here. */
158: }
159: } catch(std::exception const & ex) {
160: SETERRQ1(PETSC_COMM_SELF, PETSC_ERR_LIB, "ViennaCL error: %s", ex.what());
161: }
163: /* This assembly prevents resetting the flag to PETSC_VIENNACL_CPU and recopying */
164: MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);
165: MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);
167: A->valid_GPU_matrix = PETSC_VIENNACL_BOTH;
168: } else SETERRQ(PETSC_COMM_WORLD, PETSC_ERR_ARG_WRONG, "ViennaCL error: Only valid for unallocated GPU matrices");
169: return(0);
170: }
174: PetscErrorCode MatCreateVecs_SeqAIJViennaCL(Mat mat, Vec *right, Vec *left)
175: {
177: PetscInt rbs,cbs;
180: MatGetBlockSizes(mat,&rbs,&cbs);
181: if (right) {
182: VecCreate(PetscObjectComm((PetscObject)mat),right);
183: VecSetSizes(*right,mat->cmap->n,PETSC_DETERMINE);
184: VecSetBlockSize(*right,cbs);
185: VecSetType(*right,VECSEQVIENNACL);
186: PetscLayoutReference(mat->cmap,&(*right)->map);
187: }
188: if (left) {
189: VecCreate(PetscObjectComm((PetscObject)mat),left);
190: VecSetSizes(*left,mat->rmap->n,PETSC_DETERMINE);
191: VecSetBlockSize(*left,rbs);
192: VecSetType(*left,VECSEQVIENNACL);
193: PetscLayoutReference(mat->rmap,&(*left)->map);
194: }
195: return(0);
196: }
200: PetscErrorCode MatMult_SeqAIJViennaCL(Mat A,Vec xx,Vec yy)
201: {
202: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
203: PetscErrorCode ierr;
204: Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL*)A->spptr;
205: const ViennaCLVector *xgpu=NULL;
206: ViennaCLVector *ygpu=NULL;
209: if (A->rmap->n > 0 && A->cmap->n > 0) {
210: VecViennaCLGetArrayRead(xx,&xgpu);
211: VecViennaCLGetArrayWrite(yy,&ygpu);
212: try {
213: *ygpu = viennacl::linalg::prod(*viennaclstruct->mat,*xgpu);
214: ViennaCLWaitForGPU();
215: } catch (std::exception const & ex) {
216: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what());
217: }
218: VecViennaCLRestoreArrayRead(xx,&xgpu);
219: VecViennaCLRestoreArrayWrite(yy,&ygpu);
220: PetscLogFlops(2.0*a->nz - a->nonzerorowcnt);
221: }
222: return(0);
223: }
229: PetscErrorCode MatMultAdd_SeqAIJViennaCL(Mat A,Vec xx,Vec yy,Vec zz)
230: {
231: Mat_SeqAIJ *a = (Mat_SeqAIJ*)A->data;
232: PetscErrorCode ierr;
233: Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL*)A->spptr;
234: const ViennaCLVector *xgpu=NULL,*ygpu=NULL;
235: ViennaCLVector *zgpu=NULL;
238: if (A->rmap->n > 0 && A->cmap->n > 0) {
239: try {
240: VecViennaCLGetArrayRead(xx,&xgpu);
241: VecViennaCLGetArrayRead(yy,&ygpu);
242: VecViennaCLGetArrayWrite(zz,&zgpu);
244: if (a->compressedrow.use) {
245: ViennaCLVector temp = viennacl::linalg::prod(*viennaclstruct->compressed_mat, *xgpu);
246: *zgpu = *ygpu + temp;
247: ViennaCLWaitForGPU();
248: } else {
249: if (zz == xx || zz == yy) { //temporary required
250: ViennaCLVector temp = viennacl::linalg::prod(*viennaclstruct->mat, *xgpu);
251: *zgpu = *ygpu;
252: *zgpu += temp;
253: ViennaCLWaitForGPU();
254: } else {
255: *viennaclstruct->tempvec = viennacl::linalg::prod(*viennaclstruct->mat, *xgpu);
256: *zgpu = *ygpu + *viennaclstruct->tempvec;
257: ViennaCLWaitForGPU();
258: }
259: }
261: VecViennaCLRestoreArrayRead(xx,&xgpu);
262: VecViennaCLRestoreArrayRead(yy,&ygpu);
263: VecViennaCLRestoreArrayWrite(zz,&zgpu);
265: } catch(std::exception const & ex) {
266: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what());
267: }
268: PetscLogFlops(2.0*a->nz);
269: }
270: return(0);
271: }
275: PetscErrorCode MatAssemblyEnd_SeqAIJViennaCL(Mat A,MatAssemblyType mode)
276: {
280: MatAssemblyEnd_SeqAIJ(A,mode);
281: MatViennaCLCopyToGPU(A);
282: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
283: A->ops->mult = MatMult_SeqAIJViennaCL;
284: A->ops->multadd = MatMultAdd_SeqAIJViennaCL;
285: return(0);
286: }
288: /* --------------------------------------------------------------------------------*/
291: /*@
292: MatCreateSeqAIJViennaCL - Creates a sparse matrix in AIJ (compressed row) format
293: (the default parallel PETSc format). This matrix will ultimately be pushed down
294: to GPUs and use the ViennaCL library for calculations. For good matrix
295: assembly performance the user should preallocate the matrix storage by setting
296: the parameter nz (or the array nnz). By setting these parameters accurately,
297: performance during matrix assembly can be increased substantially.
300: Collective on MPI_Comm
302: Input Parameters:
303: + comm - MPI communicator, set to PETSC_COMM_SELF
304: . m - number of rows
305: . n - number of columns
306: . nz - number of nonzeros per row (same for all rows)
307: - nnz - array containing the number of nonzeros in the various rows
308: (possibly different for each row) or NULL
310: Output Parameter:
311: . A - the matrix
313: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
314: MatXXXXSetPreallocation() paradigm instead of this routine directly.
315: [MatXXXXSetPreallocation() is, for example, MatSeqAIJSetPreallocation]
317: Notes:
318: If nnz is given then nz is ignored
320: The AIJ format (also called the Yale sparse matrix format or
321: compressed row storage), is fully compatible with standard Fortran 77
322: storage. That is, the stored row and column indices can begin at
323: either one (as in Fortran) or zero. See the users' manual for details.
325: Specify the preallocated storage with either nz or nnz (not both).
326: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
327: allocation. For large problems you MUST preallocate memory or you
328: will get TERRIBLE performance, see the users' manual chapter on matrices.
330: Level: intermediate
332: .seealso: MatCreate(), MatCreateAIJ(), MatCreateAIJCUSP(), MatSetValues(), MatSeqAIJSetColumnIndices(), MatCreateSeqAIJWithArrays(), MatCreateAIJ()
334: @*/
335: PetscErrorCode MatCreateSeqAIJViennaCL(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt nz,const PetscInt nnz[],Mat *A)
336: {
340: MatCreate(comm,A);
341: MatSetSizes(*A,m,n,m,n);
342: MatSetType(*A,MATSEQAIJVIENNACL);
343: MatSeqAIJSetPreallocation_SeqAIJ(*A,nz,(PetscInt*)nnz);
344: return(0);
345: }
350: PetscErrorCode MatDestroy_SeqAIJViennaCL(Mat A)
351: {
353: Mat_SeqAIJViennaCL *viennaclcontainer = (Mat_SeqAIJViennaCL*)A->spptr;
356: try {
357: if (viennaclcontainer) {
358: delete viennaclcontainer->tempvec;
359: delete viennaclcontainer->mat;
360: delete viennaclcontainer->compressed_mat;
361: delete viennaclcontainer;
362: }
363: A->valid_GPU_matrix = PETSC_VIENNACL_UNALLOCATED;
364: } catch(std::exception const & ex) {
365: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_LIB,"ViennaCL error: %s", ex.what());
366: }
367: /* this next line is because MatDestroy tries to PetscFree spptr if it is not zero, and PetscFree only works if the memory was allocated with PetscNew or PetscMalloc, which don't call the constructor */
368: A->spptr = 0;
369: MatDestroy_SeqAIJ(A);
370: return(0);
371: }
376: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJViennaCL(Mat B)
377: {
379: Mat_SeqAIJ *aij;
382: MatCreate_SeqAIJ(B);
383: aij = (Mat_SeqAIJ*)B->data;
384: aij->inode.use = PETSC_FALSE;
385: B->ops->mult = MatMult_SeqAIJViennaCL;
386: B->ops->multadd = MatMultAdd_SeqAIJViennaCL;
387: B->spptr = new Mat_SeqAIJViennaCL();
389: ((Mat_SeqAIJViennaCL*)B->spptr)->tempvec = NULL;
390: ((Mat_SeqAIJViennaCL*)B->spptr)->mat = NULL;
391: ((Mat_SeqAIJViennaCL*)B->spptr)->compressed_mat = NULL;
393: B->ops->assemblyend = MatAssemblyEnd_SeqAIJViennaCL;
394: B->ops->destroy = MatDestroy_SeqAIJViennaCL;
395: B->ops->getvecs = MatCreateVecs_SeqAIJViennaCL;
397: PetscObjectChangeTypeName((PetscObject)B,MATSEQAIJVIENNACL);
399: B->valid_GPU_matrix = PETSC_VIENNACL_UNALLOCATED;
400: return(0);
401: }
404: /*M
405: MATSEQAIJVIENNACL - MATAIJVIENNACL = "aijviennacl" = "seqaijviennacl" - A matrix type to be used for sparse matrices.
407: A matrix type type whose data resides on GPUs. These matrices are in CSR format by
408: default. All matrix calculations are performed using the ViennaCL library.
410: Options Database Keys:
411: + -mat_type aijviennacl - sets the matrix type to "seqaijviennacl" during a call to MatSetFromOptions()
412: . -mat_viennacl_storage_format csr - sets the storage format of matrices for MatMult during a call to MatSetFromOptions().
413: - -mat_viennacl_mult_storage_format csr - sets the storage format of matrices for MatMult during a call to MatSetFromOptions().
415: Level: beginner
417: .seealso: MatCreateSeqAIJViennaCL(), MATAIJVIENNACL, MatCreateAIJViennaCL()
418: M*/