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;
279: PetscFunctionBegin;
280: /* If there are no nonzero entries, set zz = yy and return immediately. */
281: if (!a->nz) {
282: PetscCall(VecCopy(yy, zz));
283: PetscFunctionReturn(PETSC_SUCCESS);
284: }
286: PetscCall(VecGetArrayRead(xx, &x));
287: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
289: /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
290: * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
291: * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
292: if (!baijmkl->sparse_optimized) PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
294: /* Call MKL sparse BLAS routine to do the MatMult. */
295: if (zz == yy) {
296: /* If zz and yy are the same vector, we can use mkl_sparse_x_mv, which calculates y = alpha*A*x + beta*y,
297: * with alpha and beta both set to 1.0. */
298: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_NON_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 1.0, z));
299: } else {
300: /* zz and yy are different vectors, so we call mkl_sparse_x_mv with alpha=1.0 and beta=0.0, and then
301: * we add the contents of vector yy to the result; MKL sparse BLAS does not have a MatMultAdd equivalent. */
302: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_NON_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 0.0, z));
303: for (PetscInt i = 0; i < m; i++) z[i] += y[i];
304: }
306: PetscCall(PetscLogFlops(2.0 * a->bs2 * a->nz));
307: PetscCall(VecRestoreArrayRead(xx, &x));
308: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
309: PetscFunctionReturn(PETSC_SUCCESS);
310: }
312: static PetscErrorCode MatMultTransposeAdd_SeqBAIJMKL_SpMV2(Mat A, Vec xx, Vec yy, Vec zz)
313: {
314: Mat_SeqBAIJ *a = (Mat_SeqBAIJ *)A->data;
315: Mat_SeqBAIJMKL *baijmkl = (Mat_SeqBAIJMKL *)A->spptr;
316: const PetscScalar *x;
317: PetscScalar *y, *z;
318: PetscInt n = a->nbs * A->rmap->bs;
319: /* Variables not in MatMultTransposeAdd_SeqBAIJ. */
321: PetscFunctionBegin;
322: /* If there are no nonzero entries, set zz = yy and return immediately. */
323: if (!a->nz) {
324: PetscCall(VecCopy(yy, zz));
325: PetscFunctionReturn(PETSC_SUCCESS);
326: }
328: PetscCall(VecGetArrayRead(xx, &x));
329: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
331: /* In some cases, we get to this point without mkl_sparse_optimize() having been called, so we check and then call
332: * it if needed. Eventually, when everything in PETSc is properly updating the matrix state, we should probably
333: * take a "lazy" approach to creation/updating of the MKL matrix handle and plan to always do it here (when needed). */
334: if (!baijmkl->sparse_optimized) PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
336: /* Call MKL sparse BLAS routine to do the MatMult. */
337: if (zz == yy) {
338: /* If zz and yy are the same vector, we can use mkl_sparse_x_mv, which calculates y = alpha*A*x + beta*y,
339: * with alpha and beta both set to 1.0. */
340: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 1.0, z));
341: } else {
342: /* zz and yy are different vectors, so we call mkl_sparse_x_mv with alpha=1.0 and beta=0.0, and then
343: * we add the contents of vector yy to the result; MKL sparse BLAS does not have a MatMultAdd equivalent. */
344: PetscCallMKL(mkl_sparse_x_mv(SPARSE_OPERATION_TRANSPOSE, 1.0, baijmkl->bsrA, baijmkl->descr, x, 0.0, z));
345: for (PetscInt i = 0; i < n; i++) z[i] += y[i];
346: }
348: PetscCall(PetscLogFlops(2.0 * a->bs2 * a->nz));
349: PetscCall(VecRestoreArrayRead(xx, &x));
350: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
351: PetscFunctionReturn(PETSC_SUCCESS);
352: }
354: static PetscErrorCode MatScale_SeqBAIJMKL(Mat inA, PetscScalar alpha)
355: {
356: PetscFunctionBegin;
357: PetscCall(MatScale_SeqBAIJ(inA, alpha));
358: PetscCall(MatSeqBAIJMKL_create_mkl_handle(inA));
359: PetscFunctionReturn(PETSC_SUCCESS);
360: }
362: static PetscErrorCode MatDiagonalScale_SeqBAIJMKL(Mat A, Vec ll, Vec rr)
363: {
364: PetscFunctionBegin;
365: PetscCall(MatDiagonalScale_SeqBAIJ(A, ll, rr));
366: PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
367: PetscFunctionReturn(PETSC_SUCCESS);
368: }
370: static PetscErrorCode MatAXPY_SeqBAIJMKL(Mat Y, PetscScalar a, Mat X, MatStructure str)
371: {
372: PetscFunctionBegin;
373: PetscCall(MatAXPY_SeqBAIJ(Y, a, X, str));
374: if (str == SAME_NONZERO_PATTERN) {
375: /* MatAssemblyEnd() is not called if SAME_NONZERO_PATTERN, so we need to force update of the MKL matrix handle. */
376: PetscCall(MatSeqBAIJMKL_create_mkl_handle(Y));
377: }
378: PetscFunctionReturn(PETSC_SUCCESS);
379: }
380: /* MatConvert_SeqBAIJ_SeqBAIJMKL converts a SeqBAIJ matrix into a
381: * SeqBAIJMKL matrix. This routine is called by the MatCreate_SeqMKLBAIJ()
382: * routine, but can also be used to convert an assembled SeqBAIJ matrix
383: * into a SeqBAIJMKL one. */
384: PETSC_INTERN PetscErrorCode MatConvert_SeqBAIJ_SeqBAIJMKL(Mat A, MatType type, MatReuse reuse, Mat *newmat)
385: {
386: Mat B = *newmat;
387: Mat_SeqBAIJMKL *baijmkl;
388: PetscBool sametype;
390: PetscFunctionBegin;
391: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));
393: PetscCall(PetscObjectTypeCompare((PetscObject)A, type, &sametype));
394: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
396: PetscCall(PetscNew(&baijmkl));
397: B->spptr = (void *)baijmkl;
399: /* Set function pointers for methods that we inherit from BAIJ but override.
400: * We also parse some command line options below, since those determine some of the methods we point to. */
401: B->ops->assemblyend = MatAssemblyEnd_SeqBAIJMKL;
403: baijmkl->sparse_optimized = PETSC_FALSE;
405: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatScale_C", MatScale_SeqBAIJMKL));
406: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqbaijmkl_seqbaij_C", MatConvert_SeqBAIJMKL_SeqBAIJ));
408: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQBAIJMKL));
409: *newmat = B;
410: PetscFunctionReturn(PETSC_SUCCESS);
411: }
413: static PetscErrorCode MatAssemblyEnd_SeqBAIJMKL(Mat A, MatAssemblyType mode)
414: {
415: PetscFunctionBegin;
416: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
417: PetscCall(MatAssemblyEnd_SeqBAIJ(A, mode));
418: PetscCall(MatSeqBAIJMKL_create_mkl_handle(A));
419: A->ops->destroy = MatDestroy_SeqBAIJMKL;
420: A->ops->mult = MatMult_SeqBAIJMKL_SpMV2;
421: A->ops->multtranspose = MatMultTranspose_SeqBAIJMKL_SpMV2;
422: A->ops->multadd = MatMultAdd_SeqBAIJMKL_SpMV2;
423: A->ops->multtransposeadd = MatMultTransposeAdd_SeqBAIJMKL_SpMV2;
424: A->ops->scale = MatScale_SeqBAIJMKL;
425: A->ops->diagonalscale = MatDiagonalScale_SeqBAIJMKL;
426: A->ops->axpy = MatAXPY_SeqBAIJMKL;
427: A->ops->duplicate = MatDuplicate_SeqBAIJMKL;
428: PetscFunctionReturn(PETSC_SUCCESS);
429: }
431: /*@
432: MatCreateSeqBAIJMKL - Creates a sparse matrix of type `MATSEQBAIJMKL`.
433: This type inherits from `MATSEQBAIJ` and is largely identical, but uses sparse BLAS
434: routines from Intel MKL whenever possible.
436: Input Parameters:
437: + comm - MPI communicator, set to `PETSC_COMM_SELF`
438: . bs - size of block, the blocks are ALWAYS square. One can use `MatSetBlockSizes()` to set a different row and column blocksize but the row
439: blocksize always defines the size of the blocks. The column blocksize sets the blocksize of the vectors obtained with `MatCreateVecs()`
440: . m - number of rows
441: . n - number of columns
442: . nz - number of nonzero blocks per block row (same for all rows)
443: - nnz - array containing the number of nonzero blocks in the various block rows
444: (possibly different for each block row) or `NULL`
446: Output Parameter:
447: . A - the matrix
449: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
450: MatXXXXSetPreallocation() paradigm instead of this routine directly.
451: [MatXXXXSetPreallocation() is, for example, `MatSeqBAIJSetPreallocation()`]
453: Options Database Keys:
454: + -mat_no_unroll - uses code that does not unroll the loops in the block calculations (much slower)
455: - -mat_block_size - size of the blocks to use
457: Level: intermediate
459: Notes:
460: The number of rows and columns must be divisible by blocksize.
462: If the `nnz` parameter is given then the `nz` parameter is ignored
464: A nonzero block is any block that as 1 or more nonzeros in it
466: `MatMult()`, `MatMultAdd()`, `MatMultTranspose()`, and `MatMultTransposeAdd()`
467: operations are currently supported.
468: If the installed version of MKL supports the "SpMV2" sparse
469: inspector-executor routines, then those are used by default.
470: Default PETSc kernels are used otherwise.
472: The `MATSEQBAIJ` format is fully compatible with standard Fortran
473: storage. That is, the stored row and column indices can begin at
474: either one (as in Fortran) or zero. See the users' manual for details.
476: Specify the preallocated storage with either nz or nnz (not both).
477: Set nz = `PETSC_DEFAULT` and nnz = NULL for PETSc to control dynamic memory
478: allocation. See [Sparse Matrices](sec_matsparse) for details.
479: matrices.
481: .seealso: [Sparse Matrices](sec_matsparse), `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatCreateBAIJ()`
482: @*/
483: PetscErrorCode MatCreateSeqBAIJMKL(MPI_Comm comm, PetscInt bs, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
484: {
485: PetscFunctionBegin;
486: PetscCall(MatCreate(comm, A));
487: PetscCall(MatSetSizes(*A, m, n, m, n));
488: PetscCall(MatSetType(*A, MATSEQBAIJMKL));
489: PetscCall(MatSeqBAIJSetPreallocation_SeqBAIJ(*A, bs, nz, (PetscInt *)nnz));
490: PetscFunctionReturn(PETSC_SUCCESS);
491: }
493: PETSC_EXTERN PetscErrorCode MatCreate_SeqBAIJMKL(Mat A)
494: {
495: PetscFunctionBegin;
496: PetscCall(MatSetType(A, MATSEQBAIJ));
497: PetscCall(MatConvert_SeqBAIJ_SeqBAIJMKL(A, MATSEQBAIJMKL, MAT_INPLACE_MATRIX, &A));
498: PetscFunctionReturn(PETSC_SUCCESS);
499: }