Actual source code: baijmkl.c
1: /*
2: Defines basic operations for the MATSEQBAIJMKL matrix class.
3: Uses sparse BLAS operations from the Intel Math Kernel Library (MKL)
4: wherever possible. If used MKL version is older than 11.3 PETSc default
5: code for sparse matrix operations is used.
6: */
8: #include <../src/mat/impls/baij/seq/baij.h>
9: #include <../src/mat/impls/baij/seq/baijmkl/baijmkl.h>
10: #if defined(PETSC_HAVE_MKL_INTEL_ILP64)
11: #define MKL_ILP64
12: #endif
13: #include <mkl_spblas.h>
15: static PetscBool PetscSeqBAIJSupportsZeroBased(void)
16: {
17: static PetscBool set = PETSC_FALSE, value;
18: int n = 1, ia[1], ja[1];
19: float a[1];
20: sparse_status_t status;
21: sparse_matrix_t A;
23: if (!set) {
24: status = mkl_sparse_s_create_bsr(&A, SPARSE_INDEX_BASE_ZERO, SPARSE_LAYOUT_COLUMN_MAJOR, (MKL_INT)n, (MKL_INT)n, (MKL_INT)n, (MKL_INT *)ia, (MKL_INT *)ia, (MKL_INT *)ja, a);
25: value = (status != SPARSE_STATUS_NOT_SUPPORTED) ? PETSC_TRUE : PETSC_FALSE;
26: (void)mkl_sparse_destroy(A);
27: set = PETSC_TRUE;
28: }
29: return value;
30: }
32: typedef struct {
33: PetscBool sparse_optimized; /* If PETSC_TRUE, then mkl_sparse_optimize() has been called. */
34: sparse_matrix_t bsrA; /* "Handle" used by SpMV2 inspector-executor routines. */
35: struct matrix_descr descr;
36: PetscInt *ai1;
37: PetscInt *aj1;
38: } Mat_SeqBAIJMKL;
40: static PetscErrorCode MatAssemblyEnd_SeqBAIJMKL(Mat A, MatAssemblyType mode);
42: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJMKL_SeqBAIJ(Mat A, MatType type, MatReuse reuse, Mat *newmat)
43: {
44: /* This routine is only called to convert a MATBAIJMKL to its base PETSc type, */
45: /* so we will ignore 'MatType type'. */
46: Mat B = *newmat;
47: Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
49: PetscFunctionBegin;
50: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));
52: /* Reset the original function pointers. */
53: B->ops->duplicate = MatDuplicate_SeqBAIJ;
54: B->ops->assemblyend = MatAssemblyEnd_SeqBAIJ;
55: B->ops->destroy = MatDestroy_SeqBAIJ;
56: B->ops->multtranspose = MatMultTranspose_SeqBAIJ;
57: B->ops->multtransposeadd = MatMultTransposeAdd_SeqBAIJ;
58: B->ops->scale = MatScale_SeqBAIJ;
59: B->ops->diagonalscale = MatDiagonalScale_SeqBAIJ;
60: B->ops->axpy = MatAXPY_SeqBAIJ;
62: switch (A->rmap->bs) {
63: case 1:
64: B->ops->mult = MatMult_SeqBAIJ_1;
65: B->ops->multadd = MatMultAdd_SeqBAIJ_1;
66: break;
67: case 2:
68: B->ops->mult = MatMult_SeqBAIJ_2;
69: B->ops->multadd = MatMultAdd_SeqBAIJ_2;
70: break;
71: case 3:
72: B->ops->mult = MatMult_SeqBAIJ_3;
73: B->ops->multadd = MatMultAdd_SeqBAIJ_3;
74: break;
75: case 4:
76: B->ops->mult = MatMult_SeqBAIJ_4;
77: B->ops->multadd = MatMultAdd_SeqBAIJ_4;
78: break;
79: case 5:
80: B->ops->mult = MatMult_SeqBAIJ_5;
81: B->ops->multadd = MatMultAdd_SeqBAIJ_5;
82: break;
83: case 6:
84: B->ops->mult = MatMult_SeqBAIJ_6;
85: B->ops->multadd = MatMultAdd_SeqBAIJ_6;
86: break;
87: case 7:
88: B->ops->mult = MatMult_SeqBAIJ_7;
89: B->ops->multadd = MatMultAdd_SeqBAIJ_7;
90: break;
91: case 11:
92: B->ops->mult = MatMult_SeqBAIJ_11;
93: B->ops->multadd = MatMultAdd_SeqBAIJ_11;
94: break;
95: case 15:
96: B->ops->mult = MatMult_SeqBAIJ_15_ver1;
97: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
98: break;
99: default:
100: B->ops->mult = MatMult_SeqBAIJ_N;
101: B->ops->multadd = MatMultAdd_SeqBAIJ_N;
102: break;
103: }
104: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaijmkl_seqbaij_C", NULL));
106: /* Free everything in the Mat_SeqBAIJMKL data structure. Currently, this
107: * simply involves destroying the MKL sparse matrix handle and then freeing
108: * the spptr pointer. */
109: if (reuse == MAT_INITIAL_MATRIX) baijmkl = (Mat_SeqBAIJMKL *)B->spptr;
111: if (baijmkl->sparse_optimized) PetscCallExternal(mkl_sparse_destroy, baijmkl->bsrA);
112: PetscCall(PetscFree2(baijmkl->ai1, baijmkl->aj1));
113: PetscCall(PetscFree(B->spptr));
115: /* Change the type of B to MATSEQBAIJ. */
116: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJ));
118: *newmat = B;
119: PetscFunctionReturn(PETSC_SUCCESS);
120: }
122: static PetscErrorCode MatDestroy_SeqBAIJMKL(Mat A)
123: {
124: Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
126: PetscFunctionBegin;
127: if (baijmkl) {
128: /* Clean up everything in the Mat_SeqBAIJMKL data structure, then free A->spptr. */
129: if (baijmkl->sparse_optimized) PetscCallExternal(mkl_sparse_destroy, baijmkl->bsrA);
130: PetscCall(PetscFree2(baijmkl->ai1, baijmkl->aj1));
131: PetscCall(PetscFree(A->spptr));
132: }
134: /* Change the type of A back to SEQBAIJ and use MatDestroy_SeqBAIJ()
135: * to destroy everything that remains. */
136: PetscCall(PetscObjectChangeTypeName((PetscObject)A, MATSEQBAIJ));
137: PetscCall(MatDestroy_SeqBAIJ(A));
138: PetscFunctionReturn(PETSC_SUCCESS);
139: }
141: static PetscErrorCode MatSeqBAIJMKL_create_mkl_handle(Mat A)
142: {
143: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
144: Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
145: PetscInt mbs, nbs, nz, bs;
146: MatScalar *aa;
147: PetscInt *aj, *ai;
148: PetscInt i;
150: PetscFunctionBegin;
151: if (baijmkl->sparse_optimized) {
152: /* Matrix has been previously assembled and optimized. Must destroy old
153: * matrix handle before running the optimization step again. */
154: PetscCall(PetscFree2(baijmkl->ai1, baijmkl->aj1));
155: PetscCallMKL(mkl_sparse_destroy(baijmkl->bsrA));
156: }
157: baijmkl->sparse_optimized = PETSC_FALSE;
159: /* Now perform the SpMV2 setup and matrix optimization. */
160: baijmkl->descr.type = SPARSE_MATRIX_TYPE_GENERAL;
161: baijmkl->descr.mode = SPARSE_FILL_MODE_LOWER;
162: baijmkl->descr.diag = SPARSE_DIAG_NON_UNIT;
163: mbs = a->mbs;
164: nbs = a->nbs;
165: nz = a->nz;
166: bs = A->rmap->bs;
167: aa = a->a;
169: if ((nz != 0) & !A->structure_only) {
170: /* Create a new, optimized sparse matrix handle only if the matrix has nonzero entries.
171: * The MKL sparse-inspector executor routines don't like being passed an empty matrix. */
172: if (PetscSeqBAIJSupportsZeroBased()) {
173: aj = a->j;
174: ai = a->i;
175: PetscCallMKL(mkl_sparse_x_create_bsr(&baijmkl->bsrA, SPARSE_INDEX_BASE_ZERO, SPARSE_LAYOUT_COLUMN_MAJOR, (MKL_INT)mbs, (MKL_INT)nbs, (MKL_INT)bs, (MKL_INT *)ai, (MKL_INT *)(ai + 1), (MKL_INT *)aj, aa));
176: } else {
177: PetscCall(PetscMalloc2(mbs + 1, &ai, nz, &aj));
178: for (i = 0; i < mbs + 1; i++) ai[i] = a->i[i] + 1;
179: for (i = 0; i < nz; i++) aj[i] = a->j[i] + 1;
180: aa = a->a;
181: PetscCallMKL(mkl_sparse_x_create_bsr(&baijmkl->bsrA, SPARSE_INDEX_BASE_ONE, SPARSE_LAYOUT_COLUMN_MAJOR, (MKL_INT)mbs, (MKL_INT)nbs, (MKL_INT)bs, (MKL_INT *)ai, (MKL_INT *)(ai + 1), (MKL_INT *)aj, aa));
182: baijmkl->ai1 = ai;
183: baijmkl->aj1 = aj;
184: }
185: PetscCallMKL(mkl_sparse_set_mv_hint(baijmkl->bsrA, SPARSE_OPERATION_NON_TRANSPOSE, baijmkl->descr, 1000));
186: PetscCallMKL(mkl_sparse_set_memory_hint(baijmkl->bsrA, SPARSE_MEMORY_AGGRESSIVE));
187: PetscCallMKL(mkl_sparse_optimize(baijmkl->bsrA));
188: baijmkl->sparse_optimized = PETSC_TRUE;
189: }
190: PetscFunctionReturn(PETSC_SUCCESS);
191: }
193: static PetscErrorCode MatDuplicate_SeqBAIJMKL(Mat A, MatDuplicateOption op, Mat *M)
194: {
195: Mat_SeqBAIJMKL *baijmkl;
196: Mat_SeqBAIJMKL *baijmkl_dest;
198: PetscFunctionBegin;
199: PetscCall(MatDuplicate_SeqBAIJ(A, op, M));
200: baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
201: PetscCall(PetscNew(&baijmkl_dest));
202: (*M)->spptr = (void *)baijmkl_dest;
203: PetscCall(PetscMemcpy(baijmkl_dest, baijmkl, sizeof(Mat_SeqBAIJMKL)));
204: baijmkl_dest->sparse_optimized = PETSC_FALSE;
205: PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
206: PetscFunctionReturn(PETSC_SUCCESS);
207: }
209: static PetscErrorCode MatMult_SeqBAIJMKL_SpMV2(Mat A, Vec xx, Vec yy)
210: {
211: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
212: Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
213: const PetscScalar *x;
214: PetscScalar *y;
216: PetscFunctionBegin;
217: /* If there are no nonzero entries, zero yy and return immediately. */
218: if (!a->nz) {
219: PetscCall(VecSet(yy, 0.0));
220: PetscFunctionReturn(PETSC_SUCCESS);
221: }
223: PetscCall(VecGetArrayRead(xx, &x));
224: PetscCall(VecGetArray(yy, &y));
226: /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
227: * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
228: * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
229: if (!baijmkl->sparse_optimized) PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
231: /* Call MKL SpMV2 executor routine to do the MatMult. */
232: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_NON_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 0.0, y));
234: PetscCall(PetscLogFlops(2.0 * a->bs2 * a->nz - a->nonzerorowcnt * A->rmap->bs));
235: PetscCall(VecRestoreArrayRead(xx, &x));
236: PetscCall(VecRestoreArray(yy, &y));
237: PetscFunctionReturn(PETSC_SUCCESS);
238: }
240: static PetscErrorCode MatMultTranspose_SeqBAIJMKL_SpMV2(Mat A, Vec xx, Vec yy)
241: {
242: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
243: Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
244: const PetscScalar *x;
245: PetscScalar *y;
247: PetscFunctionBegin;
248: /* If there are no nonzero entries, zero yy and return immediately. */
249: if (!a->nz) {
250: PetscCall(VecSet(yy, 0.0));
251: PetscFunctionReturn(PETSC_SUCCESS);
252: }
254: PetscCall(VecGetArrayRead(xx, &x));
255: PetscCall(VecGetArray(yy, &y));
257: /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
258: * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
259: * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
260: if (!baijmkl->sparse_optimized) PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
262: /* Call MKL SpMV2 executor routine to do the MatMultTranspose. */
263: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 0.0, y));
265: PetscCall(PetscLogFlops(2.0 * a->bs2 * a->nz - a->nonzerorowcnt * A->rmap->bs));
266: PetscCall(VecRestoreArrayRead(xx, &x));
267: PetscCall(VecRestoreArray(yy, &y));
268: PetscFunctionReturn(PETSC_SUCCESS);
269: }
271: static PetscErrorCode MatMultAdd_SeqBAIJMKL_SpMV2(Mat A, Vec xx, Vec yy, Vec zz)
272: {
273: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
274: Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
275: const PetscScalar *x;
276: PetscScalar *y, *z;
277: PetscInt m = a->mbs * A->rmap->bs;
278: PetscInt i;
280: PetscFunctionBegin;
281: /* If there are no nonzero entries, set zz = yy and return immediately. */
282: if (!a->nz) {
283: PetscCall(VecCopy(yy, zz));
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: PetscCall(VecGetArrayRead(xx, &x));
288: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
290: /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
291: * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
292: * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
293: if (!baijmkl->sparse_optimized) PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
295: /* Call MKL sparse BLAS routine to do the MatMult. */
296: if (zz == yy) {
297: /* If zz and yy are the same vector, we can use mkl_sparse_x_mv, which calculates y = alpha*A*x + beta*y,
298: * with alpha and beta both set to 1.0. */
299: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_NON_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 1.0, z));
300: } else {
301: /* zz and yy are different vectors, so we call mkl_sparse_x_mv with alpha=1.0 and beta=0.0, and then
302: * we add the contents of vector yy to the result; MKL sparse BLAS does not have a MatMultAdd equivalent. */
303: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_NON_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 0.0, z));
304: for (i = 0; i < m; i++) z[i] += y[i];
305: }
307: PetscCall(PetscLogFlops(2.0 * a->bs2 * a->nz));
308: PetscCall(VecRestoreArrayRead(xx, &x));
309: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
310: PetscFunctionReturn(PETSC_SUCCESS);
311: }
313: static PetscErrorCode MatMultTransposeAdd_SeqBAIJMKL_SpMV2(Mat A, Vec xx, Vec yy, Vec zz)
314: {
315: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
316: Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
317: const PetscScalar *x;
318: PetscScalar *y, *z;
319: PetscInt n = a->nbs * A->rmap->bs;
320: PetscInt i;
321: /* Variables not in MatMultTransposeAdd_SeqBAIJ. */
323: PetscFunctionBegin;
324: /* If there are no nonzero entries, set zz = yy and return immediately. */
325: if (!a->nz) {
326: PetscCall(VecCopy(yy, zz));
327: PetscFunctionReturn(PETSC_SUCCESS);
328: }
330: PetscCall(VecGetArrayRead(xx, &x));
331: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
333: /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
334: * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
335: * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
336: if (!baijmkl->sparse_optimized) PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
338: /* Call MKL sparse BLAS routine to do the MatMult. */
339: if (zz == yy) {
340: /* If zz and yy are the same vector, we can use mkl_sparse_x_mv, which calculates y = alpha*A*x + beta*y,
341: * with alpha and beta both set to 1.0. */
342: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 1.0, z));
343: } else {
344: /* zz and yy are different vectors, so we call mkl_sparse_x_mv with alpha=1.0 and beta=0.0, and then
345: * we add the contents of vector yy to the result; MKL sparse BLAS does not have a MatMultAdd equivalent. */
346: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 0.0, z));
347: for (i = 0; i < n; i++) z[i] += y[i];
348: }
350: PetscCall(PetscLogFlops(2.0 * a->bs2 * a->nz));
351: PetscCall(VecRestoreArrayRead(xx, &x));
352: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
353: PetscFunctionReturn(PETSC_SUCCESS);
354: }
356: static PetscErrorCode MatScale_SeqBAIJMKL(Mat inA, PetscScalar alpha)
357: {
358: PetscFunctionBegin;
359: PetscCall(MatScale_SeqBAIJ(inA, alpha));
360: PetscCall(MatSeqBAIJMKL_create_mkl_handle(inA));
361: PetscFunctionReturn(PETSC_SUCCESS);
362: }
364: static PetscErrorCode MatDiagonalScale_SeqBAIJMKL(Mat A, Vec ll, Vec rr)
365: {
366: PetscFunctionBegin;
367: PetscCall(MatDiagonalScale_SeqBAIJ(A, ll, rr));
368: PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
369: PetscFunctionReturn(PETSC_SUCCESS);
370: }
372: static PetscErrorCode MatAXPY_SeqBAIJMKL(Mat Y, PetscScalar a, Mat X, MatStructure str)
373: {
374: PetscFunctionBegin;
375: PetscCall(MatAXPY_SeqBAIJ(Y, a, X, str));
376: if (str == SAME_NONZERO_PATTERN) {
377: /* MatAssemblyEnd() is not called if SAME_NONZERO_PATTERN, so we need to force update of the MKL matrix handle. */
378: PetscCall(MatSeqBAIJMKL_create_mkl_handle(Y));
379: }
380: PetscFunctionReturn(PETSC_SUCCESS);
381: }
382: /* MatConvert_SeqBAIJ_SeqBAIJMKL converts a SeqBAIJ matrix into a
383: * SeqBAIJMKL matrix. This routine is called by the MatCreate_SeqMKLBAIJ()
384: * routine, but can also be used to convert an assembled SeqBAIJ matrix
385: * into a SeqBAIJMKL one. */
386: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat A, MatType type, MatReuse reuse, Mat *newmat)
387: {
388: Mat B = *newmat;
389: Mat_SeqBAIJMKL *baijmkl;
390: PetscBool sametype;
392: PetscFunctionBegin;
393: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));
395: PetscCall(PetscObjectTypeCompare((PetscObject)A, type, &sametype));
396: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
398: PetscCall(PetscNew(&baijmkl));
399: B->spptr = (void *)baijmkl;
401: /* Set function pointers for methods that we inherit from BAIJ but override.
402: * We also parse some command line options below, since those determine some of the methods we point to. */
403: B->ops->assemblyend = MatAssemblyEnd_SeqBAIJMKL;
405: baijmkl->sparse_optimized = PETSC_FALSE;
407: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatScale_C", MatScale_SeqBAIJMKL));
408: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaijmkl_seqbaij_C", MatConvert_SeqBAIJMKL_SeqBAIJ));
410: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJMKL));
411: *newmat = B;
412: PetscFunctionReturn(PETSC_SUCCESS);
413: }
415: static PetscErrorCode MatAssemblyEnd_SeqBAIJMKL(Mat A, MatAssemblyType mode)
416: {
417: PetscFunctionBegin;
418: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
419: PetscCall(MatAssemblyEnd_SeqBAIJ(A, mode));
420: PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
421: A->ops->destroy = MatDestroy_SeqBAIJMKL;
422: A->ops->mult = MatMult_SeqBAIJMKL_SpMV2;
423: A->ops->multtranspose = MatMultTranspose_SeqBAIJMKL_SpMV2;
424: A->ops->multadd = MatMultAdd_SeqBAIJMKL_SpMV2;
425: A->ops->multtransposeadd = MatMultTransposeAdd_SeqBAIJMKL_SpMV2;
426: A->ops->scale = MatScale_SeqBAIJMKL;
427: A->ops->diagonalscale = MatDiagonalScale_SeqBAIJMKL;
428: A->ops->axpy = MatAXPY_SeqBAIJMKL;
429: A->ops->duplicate = MatDuplicate_SeqBAIJMKL;
430: PetscFunctionReturn(PETSC_SUCCESS);
431: }
433: /*@
434: MatCreateSeqBAIJMKL - Creates a sparse matrix of type `MATSEQBAIJMKL`.
435: This type inherits from `MATSEQBAIJ` and is largely identical, but uses sparse BLAS
436: routines from Intel MKL whenever possible.
438: Input Parameters:
439: + comm - MPI communicator, set to `PETSC_COMM_SELF`
440: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
441: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
442: . m - number of rows
443: . n - number of columns
444: . nz - number of nonzero blocks per block row (same for all rows)
445: - nnz - array containing the number of nonzero blocks in the various block rows
446: (possibly different for each block row) or `NULL`
448: Output Parameter:
449: . A - the matrix
451: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
452: MatXXXXSetPreallocation() paradigm instead of this routine directly.
453: [MatXXXXSetPreallocation() is, for example, `MatSeqBAIJSetPreallocation()`]
455: Options Database Keys:
456: + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower)
457: - -mat_block_size - size of the blocks to use
459: Level: intermediate
461: Notes:
462: The number of rows and columns must be divisible by blocksize.
464: If the `nnz` parameter is given then the `nz` parameter is ignored
466: A nonzero block is any block that as 1 or more nonzeros in it
468: `MatMult()`, `MatMultAdd()`, `MatMultTranspose()`, and `MatMultTransposeAdd()`
469: operations are currently supported.
470: If the installed version of MKL supports the "SpMV2" sparse
471: inspector-executor routines, then those are used by default.
472: Default PETSc kernels are used otherwise.
474: The `MATSEQBAIJ` format is fully compatible with standard Fortran
475: storage. That is, the stored row and column indices can begin at
476: either one (as in Fortran) or zero. See the users' manual for details.
478: Specify the preallocated storage with either nz or nnz (not both).
479: Set nz = `PETSC_DEFAULT` and nnz = NULL for PETSc to control dynamic memory
480: allocation. See [Sparse Matrices](sec_matsparse) for details.
481: matrices.
483: .seealso: [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`
484: @*/
485: PetscErrorCode MatCreateSeqBAIJMKL(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
486: {
487: PetscFunctionBegin;
488: PetscCall(MatCreate(comm, A));
489: PetscCall(MatSetSizes(*A, m, n, m, n));
490: PetscCall(MatSetType(*A, MATSEQBAIJMKL));
491: PetscCall(MatSeqBAIJSetPreallocation_SeqBAIJ(*A, bs, nz, (PetscInt *)nnz));
492: PetscFunctionReturn(PETSC_SUCCESS);
493: }
495: PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJMKL(Mat A)
496: {
497: PetscFunctionBegin;
498: PetscCall(MatSetType(A, MATSEQBAIJ));
499: PetscCall(MatConvert_SeqBAIJ_SeqBAIJMKL(A, MATSEQBAIJMKL, MAT_INPLACE_MATRIX, &A));
500: PetscFunctionReturn(PETSC_SUCCESS);
501: }