Actual source code: mkl_cpardiso.c
petsc-3.10.5 2019-03-28
1: #if defined(PETSC_HAVE_LIBMKL_INTEL_ILP64)
2: #define MKL_ILP64
3: #endif
5: #include <../src/mat/impls/aij/seq/aij.h> /*I "petscmat.h" I*/
6: #include <../src/mat/impls/aij/mpi/mpiaij.h>
8: #include <stdio.h>
9: #include <stdlib.h>
10: #include <math.h>
11: #if defined(PETSC_HAVE_LIBMKL_INTEL_ILP64)
12: #define MKL_ILP64
13: #endif
14: #include <mkl.h>
15: #include <mkl_cluster_sparse_solver.h>
17: /*
18: * Possible mkl_cpardiso phases that controls the execution of the solver.
19: * For more information check mkl_cpardiso manual.
20: */
21: #define JOB_ANALYSIS 11
22: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION 12
23: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 13
24: #define JOB_NUMERICAL_FACTORIZATION 22
25: #define JOB_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 23
26: #define JOB_SOLVE_ITERATIVE_REFINEMENT 33
27: #define JOB_SOLVE_FORWARD_SUBSTITUTION 331
28: #define JOB_SOLVE_DIAGONAL_SUBSTITUTION 332
29: #define JOB_SOLVE_BACKWARD_SUBSTITUTION 333
30: #define JOB_RELEASE_OF_LU_MEMORY 0
31: #define JOB_RELEASE_OF_ALL_MEMORY -1
33: #define IPARM_SIZE 64
34: #define INT_TYPE MKL_INT
36: static const char *Err_MSG_CPardiso(int errNo){
37: switch (errNo) {
38: case -1:
39: return "input inconsistent"; break;
40: case -2:
41: return "not enough memory"; break;
42: case -3:
43: return "reordering problem"; break;
44: case -4:
45: return "zero pivot, numerical factorization or iterative refinement problem"; break;
46: case -5:
47: return "unclassified (internal) error"; break;
48: case -6:
49: return "preordering failed (matrix types 11, 13 only)"; break;
50: case -7:
51: return "diagonal matrix problem"; break;
52: case -8:
53: return "32-bit integer overflow problem"; break;
54: case -9:
55: return "not enough memory for OOC"; break;
56: case -10:
57: return "problems with opening OOC temporary files"; break;
58: case -11:
59: return "read/write problems with the OOC data file"; break;
60: default :
61: return "unknown error";
62: }
63: }
65: /*
66: * Internal data structure.
67: * For more information check mkl_cpardiso manual.
68: */
70: typedef struct {
72: /* Configuration vector */
73: INT_TYPE iparm[IPARM_SIZE];
75: /*
76: * Internal mkl_cpardiso memory location.
77: * After the first call to mkl_cpardiso do not modify pt, as that could cause a serious memory leak.
78: */
79: void *pt[IPARM_SIZE];
81: MPI_Comm comm_mkl_cpardiso;
83: /* Basic mkl_cpardiso info*/
84: INT_TYPE phase, maxfct, mnum, mtype, n, nrhs, msglvl, err;
86: /* Matrix structure */
87: PetscScalar *a;
89: INT_TYPE *ia, *ja;
91: /* Number of non-zero elements */
92: INT_TYPE nz;
94: /* Row permutaton vector*/
95: INT_TYPE *perm;
97: /* Define is matrix preserve sparce structure. */
98: MatStructure matstruc;
100: PetscErrorCode (*ConvertToTriples)(Mat, MatReuse, PetscInt*, PetscInt**, PetscInt**, PetscScalar**);
102: /* True if mkl_cpardiso function have been used. */
103: PetscBool CleanUp;
104: } Mat_MKL_CPARDISO;
106: /*
107: * Copy the elements of matrix A.
108: * Input:
109: * - Mat A: MATSEQAIJ matrix
110: * - int shift: matrix index.
111: * - 0 for c representation
112: * - 1 for fortran representation
113: * - MatReuse reuse:
114: * - MAT_INITIAL_MATRIX: Create a new aij representation
115: * - MAT_REUSE_MATRIX: Reuse all aij representation and just change values
116: * Output:
117: * - int *nnz: Number of nonzero-elements.
118: * - int **r pointer to i index
119: * - int **c pointer to j elements
120: * - MATRIXTYPE **v: Non-zero elements
121: */
122: PetscErrorCode MatCopy_seqaij_seqaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
123: {
124: Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data;
127: *v=aa->a;
128: if (reuse == MAT_INITIAL_MATRIX) {
129: *r = (INT_TYPE*)aa->i;
130: *c = (INT_TYPE*)aa->j;
131: *nnz = aa->nz;
132: }
133: return(0);
134: }
136: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
137: {
138: const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
139: PetscErrorCode ierr;
140: PetscInt rstart,nz,i,j,countA,countB;
141: PetscInt *row,*col;
142: const PetscScalar *av, *bv;
143: PetscScalar *val;
144: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
145: Mat_SeqAIJ *aa = (Mat_SeqAIJ*)(mat->A)->data;
146: Mat_SeqAIJ *bb = (Mat_SeqAIJ*)(mat->B)->data;
147: PetscInt colA_start,jB,jcol;
150: ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart;
151: av=aa->a; bv=bb->a;
153: garray = mat->garray;
155: if (reuse == MAT_INITIAL_MATRIX) {
156: nz = aa->nz + bb->nz;
157: *nnz = nz;
158: PetscMalloc((nz*(sizeof(PetscInt)+sizeof(PetscScalar)) + (m+1)*sizeof(PetscInt)), &row);
159: col = row + m + 1;
160: val = (PetscScalar*)(col + nz);
161: *r = row; *c = col; *v = val;
162: row[0] = 0;
163: } else {
164: row = *r; col = *c; val = *v;
165: }
167: nz = 0;
168: for (i=0; i<m; i++) {
169: row[i] = nz;
170: countA = ai[i+1] - ai[i];
171: countB = bi[i+1] - bi[i];
172: ajj = aj + ai[i]; /* ptr to the beginning of this row */
173: bjj = bj + bi[i];
175: /* B part, smaller col index */
176: colA_start = rstart + ajj[0]; /* the smallest global col index of A */
177: jB = 0;
178: for (j=0; j<countB; j++) {
179: jcol = garray[bjj[j]];
180: if (jcol > colA_start) {
181: jB = j;
182: break;
183: }
184: col[nz] = jcol;
185: val[nz++] = *bv++;
186: if (j==countB-1) jB = countB;
187: }
189: /* A part */
190: for (j=0; j<countA; j++) {
191: col[nz] = rstart + ajj[j];
192: val[nz++] = *av++;
193: }
195: /* B part, larger col index */
196: for (j=jB; j<countB; j++) {
197: col[nz] = garray[bjj[j]];
198: val[nz++] = *bv++;
199: }
200: }
201: row[m] = nz;
203: return(0);
204: }
206: /*
207: * Free memory for Mat_MKL_CPARDISO structure and pointers to objects.
208: */
209: PetscErrorCode MatDestroy_MKL_CPARDISO(Mat A)
210: {
211: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data;
212: PetscErrorCode ierr;
215: /* Terminate instance, deallocate memories */
216: if (mat_mkl_cpardiso->CleanUp) {
217: mat_mkl_cpardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;
219: cluster_sparse_solver (
220: mat_mkl_cpardiso->pt,
221: &mat_mkl_cpardiso->maxfct,
222: &mat_mkl_cpardiso->mnum,
223: &mat_mkl_cpardiso->mtype,
224: &mat_mkl_cpardiso->phase,
225: &mat_mkl_cpardiso->n,
226: NULL,
227: NULL,
228: NULL,
229: mat_mkl_cpardiso->perm,
230: &mat_mkl_cpardiso->nrhs,
231: mat_mkl_cpardiso->iparm,
232: &mat_mkl_cpardiso->msglvl,
233: NULL,
234: NULL,
235: &mat_mkl_cpardiso->comm_mkl_cpardiso,
236: (int*)&mat_mkl_cpardiso->err);
237: }
239: if (mat_mkl_cpardiso->ConvertToTriples == MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO) {
240: PetscFree(mat_mkl_cpardiso->ia);
241: }
242: MPI_Comm_free(&(mat_mkl_cpardiso->comm_mkl_cpardiso));
243: PetscFree(A->data);
245: /* clear composed functions */
246: PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
247: PetscObjectComposeFunction((PetscObject)A,"MatMkl_CPardisoSetCntl_C",NULL);
248: return(0);
249: }
251: /*
252: * Computes Ax = b
253: */
254: PetscErrorCode MatSolve_MKL_CPARDISO(Mat A,Vec b,Vec x)
255: {
256: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(A)->data;
257: PetscErrorCode ierr;
258: PetscScalar *xarray;
259: const PetscScalar *barray;
262: mat_mkl_cpardiso->nrhs = 1;
263: VecGetArray(x,&xarray);
264: VecGetArrayRead(b,&barray);
266: /* solve phase */
267: /*-------------*/
268: mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
269: cluster_sparse_solver (
270: mat_mkl_cpardiso->pt,
271: &mat_mkl_cpardiso->maxfct,
272: &mat_mkl_cpardiso->mnum,
273: &mat_mkl_cpardiso->mtype,
274: &mat_mkl_cpardiso->phase,
275: &mat_mkl_cpardiso->n,
276: mat_mkl_cpardiso->a,
277: mat_mkl_cpardiso->ia,
278: mat_mkl_cpardiso->ja,
279: mat_mkl_cpardiso->perm,
280: &mat_mkl_cpardiso->nrhs,
281: mat_mkl_cpardiso->iparm,
282: &mat_mkl_cpardiso->msglvl,
283: (void*)barray,
284: (void*)xarray,
285: &mat_mkl_cpardiso->comm_mkl_cpardiso,
286: (int*)&mat_mkl_cpardiso->err);
288: if (mat_mkl_cpardiso->err < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_CPARDISO: err=%d, msg = \"%s\". Please check manual\n",mat_mkl_cpardiso->err,Err_MSG_CPardiso(mat_mkl_cpardiso->err));
290: VecRestoreArray(x,&xarray);
291: VecRestoreArrayRead(b,&barray);
292: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
293: return(0);
294: }
296: PetscErrorCode MatSolveTranspose_MKL_CPARDISO(Mat A,Vec b,Vec x)
297: {
298: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data;
299: PetscErrorCode ierr;
302: #if defined(PETSC_USE_COMPLEX)
303: mat_mkl_cpardiso->iparm[12 - 1] = 1;
304: #else
305: mat_mkl_cpardiso->iparm[12 - 1] = 2;
306: #endif
307: MatSolve_MKL_CPARDISO(A,b,x);
308: mat_mkl_cpardiso->iparm[12 - 1] = 0;
309: return(0);
310: }
312: PetscErrorCode MatMatSolve_MKL_CPARDISO(Mat A,Mat B,Mat X)
313: {
314: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(A)->data;
315: PetscErrorCode ierr;
316: PetscScalar *xarray;
317: const PetscScalar *barray;
320: MatGetSize(B,NULL,(PetscInt*)&mat_mkl_cpardiso->nrhs);
322: if(mat_mkl_cpardiso->nrhs > 0){
323: MatDenseGetArrayRead(B,&barray);
324: MatDenseGetArray(X,&xarray);
326: /* solve phase */
327: /*-------------*/
328: mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
329: cluster_sparse_solver (
330: mat_mkl_cpardiso->pt,
331: &mat_mkl_cpardiso->maxfct,
332: &mat_mkl_cpardiso->mnum,
333: &mat_mkl_cpardiso->mtype,
334: &mat_mkl_cpardiso->phase,
335: &mat_mkl_cpardiso->n,
336: mat_mkl_cpardiso->a,
337: mat_mkl_cpardiso->ia,
338: mat_mkl_cpardiso->ja,
339: mat_mkl_cpardiso->perm,
340: &mat_mkl_cpardiso->nrhs,
341: mat_mkl_cpardiso->iparm,
342: &mat_mkl_cpardiso->msglvl,
343: (void*)barray,
344: (void*)xarray,
345: &mat_mkl_cpardiso->comm_mkl_cpardiso,
346: (int*)&mat_mkl_cpardiso->err);
347: if (mat_mkl_cpardiso->err < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_CPARDISO: err=%d, msg = \"%s\". Please check manual\n",mat_mkl_cpardiso->err,Err_MSG_CPardiso(mat_mkl_cpardiso->err));
348: MatDenseRestoreArrayRead(B,&barray);
349: MatDenseRestoreArray(X,&xarray);
351: }
352: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
353: return(0);
355: }
357: /*
358: * LU Decomposition
359: */
360: PetscErrorCode MatFactorNumeric_MKL_CPARDISO(Mat F,Mat A,const MatFactorInfo *info)
361: {
362: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(F)->data;
363: PetscErrorCode ierr;
366: mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
367: (*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_REUSE_MATRIX,&mat_mkl_cpardiso->nz,&mat_mkl_cpardiso->ia,&mat_mkl_cpardiso->ja,&mat_mkl_cpardiso->a);
369: mat_mkl_cpardiso->phase = JOB_NUMERICAL_FACTORIZATION;
370: cluster_sparse_solver (
371: mat_mkl_cpardiso->pt,
372: &mat_mkl_cpardiso->maxfct,
373: &mat_mkl_cpardiso->mnum,
374: &mat_mkl_cpardiso->mtype,
375: &mat_mkl_cpardiso->phase,
376: &mat_mkl_cpardiso->n,
377: mat_mkl_cpardiso->a,
378: mat_mkl_cpardiso->ia,
379: mat_mkl_cpardiso->ja,
380: mat_mkl_cpardiso->perm,
381: &mat_mkl_cpardiso->nrhs,
382: mat_mkl_cpardiso->iparm,
383: &mat_mkl_cpardiso->msglvl,
384: NULL,
385: NULL,
386: &mat_mkl_cpardiso->comm_mkl_cpardiso,
387: &mat_mkl_cpardiso->err);
388: if (mat_mkl_cpardiso->err < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_CPARDISO: err=%d, msg = \"%s\". Please check manual\n",mat_mkl_cpardiso->err,Err_MSG_CPardiso(mat_mkl_cpardiso->err));
390: mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
391: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
392: return(0);
393: }
395: /* Sets mkl_cpardiso options from the options database */
396: PetscErrorCode PetscSetMKL_CPARDISOFromOptions(Mat F, Mat A)
397: {
398: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO*)F->data;
399: PetscErrorCode ierr;
400: PetscInt icntl;
401: PetscBool flg;
402: int threads;
405: PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MKL_CPARDISO Options","Mat");
406: PetscOptionsInt("-mat_mkl_cpardiso_65","Number of threads to use","None",threads,&threads,&flg);
407: if (flg) mkl_set_num_threads(threads);
409: PetscOptionsInt("-mat_mkl_cpardiso_66","Maximum number of factors with identical sparsity structure that must be kept in memory at the same time","None",mat_mkl_cpardiso->maxfct,&icntl,&flg);
410: if (flg) mat_mkl_cpardiso->maxfct = icntl;
412: PetscOptionsInt("-mat_mkl_cpardiso_67","Indicates the actual matrix for the solution phase","None",mat_mkl_cpardiso->mnum,&icntl,&flg);
413: if (flg) mat_mkl_cpardiso->mnum = icntl;
415: PetscOptionsInt("-mat_mkl_cpardiso_68","Message level information","None",mat_mkl_cpardiso->msglvl,&icntl,&flg);
416: if (flg) mat_mkl_cpardiso->msglvl = icntl;
418: PetscOptionsInt("-mat_mkl_cpardiso_69","Defines the matrix type","None",mat_mkl_cpardiso->mtype,&icntl,&flg);
419: if(flg){
420: mat_mkl_cpardiso->mtype = icntl;
421: #if defined(PETSC_USE_REAL_SINGLE)
422: mat_mkl_cpardiso->iparm[27] = 1;
423: #else
424: mat_mkl_cpardiso->iparm[27] = 0;
425: #endif
426: mat_mkl_cpardiso->iparm[34] = 1;
427: }
428: PetscOptionsInt("-mat_mkl_cpardiso_1","Use default values","None",mat_mkl_cpardiso->iparm[0],&icntl,&flg);
430: if(flg && icntl != 0){
431: PetscOptionsInt("-mat_mkl_cpardiso_2","Fill-in reducing ordering for the input matrix","None",mat_mkl_cpardiso->iparm[1],&icntl,&flg);
432: if (flg) mat_mkl_cpardiso->iparm[1] = icntl;
434: PetscOptionsInt("-mat_mkl_cpardiso_4","Preconditioned CGS/CG","None",mat_mkl_cpardiso->iparm[3],&icntl,&flg);
435: if (flg) mat_mkl_cpardiso->iparm[3] = icntl;
437: PetscOptionsInt("-mat_mkl_cpardiso_5","User permutation","None",mat_mkl_cpardiso->iparm[4],&icntl,&flg);
438: if (flg) mat_mkl_cpardiso->iparm[4] = icntl;
440: PetscOptionsInt("-mat_mkl_cpardiso_6","Write solution on x","None",mat_mkl_cpardiso->iparm[5],&icntl,&flg);
441: if (flg) mat_mkl_cpardiso->iparm[5] = icntl;
443: PetscOptionsInt("-mat_mkl_cpardiso_8","Iterative refinement step","None",mat_mkl_cpardiso->iparm[7],&icntl,&flg);
444: if (flg) mat_mkl_cpardiso->iparm[7] = icntl;
446: PetscOptionsInt("-mat_mkl_cpardiso_10","Pivoting perturbation","None",mat_mkl_cpardiso->iparm[9],&icntl,&flg);
447: if (flg) mat_mkl_cpardiso->iparm[9] = icntl;
449: PetscOptionsInt("-mat_mkl_cpardiso_11","Scaling vectors","None",mat_mkl_cpardiso->iparm[10],&icntl,&flg);
450: if (flg) mat_mkl_cpardiso->iparm[10] = icntl;
452: PetscOptionsInt("-mat_mkl_cpardiso_12","Solve with transposed or conjugate transposed matrix A","None",mat_mkl_cpardiso->iparm[11],&icntl,&flg);
453: if (flg) mat_mkl_cpardiso->iparm[11] = icntl;
455: PetscOptionsInt("-mat_mkl_cpardiso_13","Improved accuracy using (non-) symmetric weighted matching","None",mat_mkl_cpardiso->iparm[12],&icntl,
456: &flg);
457: if (flg) mat_mkl_cpardiso->iparm[12] = icntl;
459: PetscOptionsInt("-mat_mkl_cpardiso_18","Numbers of non-zero elements","None",mat_mkl_cpardiso->iparm[17],&icntl,
460: &flg);
461: if (flg) mat_mkl_cpardiso->iparm[17] = icntl;
463: PetscOptionsInt("-mat_mkl_cpardiso_19","Report number of floating point operations","None",mat_mkl_cpardiso->iparm[18],&icntl,&flg);
464: if (flg) mat_mkl_cpardiso->iparm[18] = icntl;
466: PetscOptionsInt("-mat_mkl_cpardiso_21","Pivoting for symmetric indefinite matrices","None",mat_mkl_cpardiso->iparm[20],&icntl,&flg);
467: if (flg) mat_mkl_cpardiso->iparm[20] = icntl;
469: PetscOptionsInt("-mat_mkl_cpardiso_24","Parallel factorization control","None",mat_mkl_cpardiso->iparm[23],&icntl,&flg);
470: if (flg) mat_mkl_cpardiso->iparm[23] = icntl;
472: PetscOptionsInt("-mat_mkl_cpardiso_25","Parallel forward/backward solve control","None",mat_mkl_cpardiso->iparm[24],&icntl,&flg);
473: if (flg) mat_mkl_cpardiso->iparm[24] = icntl;
475: PetscOptionsInt("-mat_mkl_cpardiso_27","Matrix checker","None",mat_mkl_cpardiso->iparm[26],&icntl,&flg);
476: if (flg) mat_mkl_cpardiso->iparm[26] = icntl;
478: PetscOptionsInt("-mat_mkl_cpardiso_31","Partial solve and computing selected components of the solution vectors","None",mat_mkl_cpardiso->iparm[30],&icntl,&flg);
479: if (flg) mat_mkl_cpardiso->iparm[30] = icntl;
481: PetscOptionsInt("-mat_mkl_cpardiso_34","Optimal number of threads for conditional numerical reproducibility (CNR) mode","None",mat_mkl_cpardiso->iparm[33],&icntl,&flg);
482: if (flg) mat_mkl_cpardiso->iparm[33] = icntl;
484: PetscOptionsInt("-mat_mkl_cpardiso_60","Intel MKL_CPARDISO mode","None",mat_mkl_cpardiso->iparm[59],&icntl,&flg);
485: if (flg) mat_mkl_cpardiso->iparm[59] = icntl;
486: }
488: PetscOptionsEnd();
489: return(0);
490: }
492: PetscErrorCode PetscInitialize_MKL_CPARDISO(Mat A, Mat_MKL_CPARDISO *mat_mkl_cpardiso)
493: {
494: PetscErrorCode ierr;
495: PetscMPIInt size;
499: MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mat_mkl_cpardiso->comm_mkl_cpardiso));
500: MPI_Comm_size(mat_mkl_cpardiso->comm_mkl_cpardiso, &size);
502: mat_mkl_cpardiso->CleanUp = PETSC_FALSE;
503: mat_mkl_cpardiso->maxfct = 1;
504: mat_mkl_cpardiso->mnum = 1;
505: mat_mkl_cpardiso->n = A->rmap->N;
506: mat_mkl_cpardiso->msglvl = 0;
507: mat_mkl_cpardiso->nrhs = 1;
508: mat_mkl_cpardiso->err = 0;
509: mat_mkl_cpardiso->phase = -1;
510: #if defined(PETSC_USE_COMPLEX)
511: mat_mkl_cpardiso->mtype = 13;
512: #else
513: mat_mkl_cpardiso->mtype = 11;
514: #endif
516: #if defined(PETSC_USE_REAL_SINGLE)
517: mat_mkl_cpardiso->iparm[27] = 1;
518: #else
519: mat_mkl_cpardiso->iparm[27] = 0;
520: #endif
522: mat_mkl_cpardiso->iparm[34] = 1; /* C style */
524: mat_mkl_cpardiso->iparm[ 0] = 1; /* Solver default parameters overriden with provided by iparm */
525: mat_mkl_cpardiso->iparm[ 1] = 2; /* Use METIS for fill-in reordering */
526: mat_mkl_cpardiso->iparm[ 5] = 0; /* Write solution into x */
527: mat_mkl_cpardiso->iparm[ 7] = 2; /* Max number of iterative refinement steps */
528: mat_mkl_cpardiso->iparm[ 9] = 13; /* Perturb the pivot elements with 1E-13 */
529: mat_mkl_cpardiso->iparm[10] = 1; /* Use nonsymmetric permutation and scaling MPS */
530: mat_mkl_cpardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
531: mat_mkl_cpardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
532: mat_mkl_cpardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
533: mat_mkl_cpardiso->iparm[26] = 1; /* Check input data for correctness */
535: mat_mkl_cpardiso->iparm[39] = 0;
536: if (size > 1) {
537: mat_mkl_cpardiso->iparm[39] = 2;
538: mat_mkl_cpardiso->iparm[40] = A->rmap->rstart;
539: mat_mkl_cpardiso->iparm[41] = A->rmap->rend-1;
540: }
541: mat_mkl_cpardiso->perm = 0;
542: return(0);
543: }
545: /*
546: * Symbolic decomposition. Mkl_Pardiso analysis phase.
547: */
548: PetscErrorCode MatLUFactorSymbolic_AIJMKL_CPARDISO(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
549: {
550: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO*)F->data;
551: PetscErrorCode ierr;
554: mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
556: /* Set MKL_CPARDISO options from the options database */
557: PetscSetMKL_CPARDISOFromOptions(F,A);
558: (*mat_mkl_cpardiso->ConvertToTriples)(A,MAT_INITIAL_MATRIX,&mat_mkl_cpardiso->nz,&mat_mkl_cpardiso->ia,&mat_mkl_cpardiso->ja,&mat_mkl_cpardiso->a);
560: mat_mkl_cpardiso->n = A->rmap->N;
562: /* analysis phase */
563: /*----------------*/
564: mat_mkl_cpardiso->phase = JOB_ANALYSIS;
566: cluster_sparse_solver (
567: mat_mkl_cpardiso->pt,
568: &mat_mkl_cpardiso->maxfct,
569: &mat_mkl_cpardiso->mnum,
570: &mat_mkl_cpardiso->mtype,
571: &mat_mkl_cpardiso->phase,
572: &mat_mkl_cpardiso->n,
573: mat_mkl_cpardiso->a,
574: mat_mkl_cpardiso->ia,
575: mat_mkl_cpardiso->ja,
576: mat_mkl_cpardiso->perm,
577: &mat_mkl_cpardiso->nrhs,
578: mat_mkl_cpardiso->iparm,
579: &mat_mkl_cpardiso->msglvl,
580: NULL,
581: NULL,
582: &mat_mkl_cpardiso->comm_mkl_cpardiso,
583: (int*)&mat_mkl_cpardiso->err);
585: if (mat_mkl_cpardiso->err < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_LIB,"Error reported by MKL_CPARDISO: err=%d, msg = \"%s\".Check manual\n",mat_mkl_cpardiso->err,Err_MSG_CPardiso(mat_mkl_cpardiso->err));
587: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
588: F->ops->lufactornumeric = MatFactorNumeric_MKL_CPARDISO;
589: F->ops->solve = MatSolve_MKL_CPARDISO;
590: F->ops->solvetranspose = MatSolveTranspose_MKL_CPARDISO;
591: F->ops->matsolve = MatMatSolve_MKL_CPARDISO;
592: return(0);
593: }
595: PetscErrorCode MatView_MKL_CPARDISO(Mat A, PetscViewer viewer)
596: {
597: PetscErrorCode ierr;
598: PetscBool iascii;
599: PetscViewerFormat format;
600: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data;
601: PetscInt i;
604: /* check if matrix is mkl_cpardiso type */
605: if (A->ops->solve != MatSolve_MKL_CPARDISO) return(0);
607: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
608: if (iascii) {
609: PetscViewerGetFormat(viewer,&format);
610: if (format == PETSC_VIEWER_ASCII_INFO) {
611: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO run parameters:\n");
612: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO phase: %d \n",mat_mkl_cpardiso->phase);
613: for(i = 1; i <= 64; i++){
614: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO iparm[%d]: %d \n",i, mat_mkl_cpardiso->iparm[i - 1]);
615: }
616: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO maxfct: %d \n", mat_mkl_cpardiso->maxfct);
617: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO mnum: %d \n", mat_mkl_cpardiso->mnum);
618: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO mtype: %d \n", mat_mkl_cpardiso->mtype);
619: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO n: %d \n", mat_mkl_cpardiso->n);
620: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO nrhs: %d \n", mat_mkl_cpardiso->nrhs);
621: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO msglvl: %d \n", mat_mkl_cpardiso->msglvl);
622: }
623: }
624: return(0);
625: }
627: PetscErrorCode MatGetInfo_MKL_CPARDISO(Mat A, MatInfoType flag, MatInfo *info)
628: {
629: Mat_MKL_CPARDISO *mat_mkl_cpardiso =(Mat_MKL_CPARDISO*)A->data;
632: info->block_size = 1.0;
633: info->nz_allocated = mat_mkl_cpardiso->nz + 0.0;
634: info->nz_unneeded = 0.0;
635: info->assemblies = 0.0;
636: info->mallocs = 0.0;
637: info->memory = 0.0;
638: info->fill_ratio_given = 0;
639: info->fill_ratio_needed = 0;
640: info->factor_mallocs = 0;
641: return(0);
642: }
644: PetscErrorCode MatMkl_CPardisoSetCntl_MKL_CPARDISO(Mat F,PetscInt icntl,PetscInt ival)
645: {
646: Mat_MKL_CPARDISO *mat_mkl_cpardiso =(Mat_MKL_CPARDISO*)F->data;
649: if(icntl <= 64){
650: mat_mkl_cpardiso->iparm[icntl - 1] = ival;
651: } else {
652: if(icntl == 65) mkl_set_num_threads((int)ival);
653: else if(icntl == 66) mat_mkl_cpardiso->maxfct = ival;
654: else if(icntl == 67) mat_mkl_cpardiso->mnum = ival;
655: else if(icntl == 68) mat_mkl_cpardiso->msglvl = ival;
656: else if(icntl == 69){
657: mat_mkl_cpardiso->mtype = ival;
658: #if defined(PETSC_USE_REAL_SINGLE)
659: mat_mkl_cpardiso->iparm[27] = 1;
660: #else
661: mat_mkl_cpardiso->iparm[27] = 0;
662: #endif
663: mat_mkl_cpardiso->iparm[34] = 1;
664: }
665: }
666: return(0);
667: }
669: /*@
670: MatMkl_CPardisoSetCntl - Set Mkl_Pardiso parameters
672: Logically Collective on Mat
674: Input Parameters:
675: + F - the factored matrix obtained by calling MatGetFactor()
676: . icntl - index of Mkl_Pardiso parameter
677: - ival - value of Mkl_Pardiso parameter
679: Options Database:
680: . -mat_mkl_cpardiso_<icntl> <ival>
682: Level: Intermediate
684: Notes:
685: This routine cannot be used if you are solving the linear system with TS, SNES, or KSP, only if you directly call MatGetFactor() so use the options
686: database approach when working with TS, SNES, or KSP.
688: References:
689: . Mkl_Pardiso Users' Guide
691: .seealso: MatGetFactor()
692: @*/
693: PetscErrorCode MatMkl_CPardisoSetCntl(Mat F,PetscInt icntl,PetscInt ival)
694: {
698: PetscTryMethod(F,"MatMkl_CPardisoSetCntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
699: return(0);
700: }
702: static PetscErrorCode MatFactorGetSolverType_mkl_cpardiso(Mat A, MatSolverType *type)
703: {
705: *type = MATSOLVERMKL_CPARDISO;
706: return(0);
707: }
709: /* MatGetFactor for MPI AIJ matrices */
710: static PetscErrorCode MatGetFactor_mpiaij_mkl_cpardiso(Mat A,MatFactorType ftype,Mat *F)
711: {
712: Mat B;
713: PetscErrorCode ierr;
714: Mat_MKL_CPARDISO *mat_mkl_cpardiso;
715: PetscBool isSeqAIJ;
718: /* Create the factorization matrix */
720: PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
721: MatCreate(PetscObjectComm((PetscObject)A),&B);
722: MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
723: PetscStrallocpy("mkl_cpardiso",&((PetscObject)B)->type_name);
724: MatSetUp(B);
726: PetscNewLog(B,&mat_mkl_cpardiso);
728: if (isSeqAIJ) mat_mkl_cpardiso->ConvertToTriples = MatCopy_seqaij_seqaij_MKL_CPARDISO;
729: else mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO;
731: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_CPARDISO;
732: B->ops->destroy = MatDestroy_MKL_CPARDISO;
734: B->ops->view = MatView_MKL_CPARDISO;
735: B->ops->getinfo = MatGetInfo_MKL_CPARDISO;
737: B->factortype = ftype;
738: B->assembled = PETSC_TRUE; /* required by -ksp_view */
740: B->data = mat_mkl_cpardiso;
742: /* set solvertype */
743: PetscFree(B->solvertype);
744: PetscStrallocpy(MATSOLVERMKL_CPARDISO,&B->solvertype);
746: PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mkl_cpardiso);
747: PetscObjectComposeFunction((PetscObject)B,"MatMkl_CPardisoSetCntl_C",MatMkl_CPardisoSetCntl_MKL_CPARDISO);
748: PetscInitialize_MKL_CPARDISO(A, mat_mkl_cpardiso);
750: *F = B;
751: return(0);
752: }
754: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MKL_CPardiso(void)
755: {
757:
759: MatSolverTypeRegister(MATSOLVERMKL_CPARDISO,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_mpiaij_mkl_cpardiso);
760: MatSolverTypeRegister(MATSOLVERMKL_CPARDISO,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_mpiaij_mkl_cpardiso);
761: return(0);
762: }