Actual source code: mkl_cpardiso.c
petsc-3.9.4 2018-09-11
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: #include <mkl.h>
12: #include <mkl_cluster_sparse_solver.h>
14: /*
15: * Possible mkl_cpardiso phases that controls the execution of the solver.
16: * For more information check mkl_cpardiso manual.
17: */
18: #define JOB_ANALYSIS 11
19: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION 12
20: #define JOB_ANALYSIS_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 13
21: #define JOB_NUMERICAL_FACTORIZATION 22
22: #define JOB_NUMERICAL_FACTORIZATION_SOLVE_ITERATIVE_REFINEMENT 23
23: #define JOB_SOLVE_ITERATIVE_REFINEMENT 33
24: #define JOB_SOLVE_FORWARD_SUBSTITUTION 331
25: #define JOB_SOLVE_DIAGONAL_SUBSTITUTION 332
26: #define JOB_SOLVE_BACKWARD_SUBSTITUTION 333
27: #define JOB_RELEASE_OF_LU_MEMORY 0
28: #define JOB_RELEASE_OF_ALL_MEMORY -1
30: #define IPARM_SIZE 64
31: #define INT_TYPE MKL_INT
33: static const char *Err_MSG_CPardiso(int errNo){
34: switch (errNo) {
35: case -1:
36: return "input inconsistent"; break;
37: case -2:
38: return "not enough memory"; break;
39: case -3:
40: return "reordering problem"; break;
41: case -4:
42: return "zero pivot, numerical factorization or iterative refinement problem"; break;
43: case -5:
44: return "unclassified (internal) error"; break;
45: case -6:
46: return "preordering failed (matrix types 11, 13 only)"; break;
47: case -7:
48: return "diagonal matrix problem"; break;
49: case -8:
50: return "32-bit integer overflow problem"; break;
51: case -9:
52: return "not enough memory for OOC"; break;
53: case -10:
54: return "problems with opening OOC temporary files"; break;
55: case -11:
56: return "read/write problems with the OOC data file"; break;
57: default :
58: return "unknown error";
59: }
60: }
62: /*
63: * Internal data structure.
64: * For more information check mkl_cpardiso manual.
65: */
67: typedef struct {
69: /* Configuration vector */
70: INT_TYPE iparm[IPARM_SIZE];
72: /*
73: * Internal mkl_cpardiso memory location.
74: * After the first call to mkl_cpardiso do not modify pt, as that could cause a serious memory leak.
75: */
76: void *pt[IPARM_SIZE];
78: MPI_Comm comm_mkl_cpardiso;
80: /* Basic mkl_cpardiso info*/
81: INT_TYPE phase, maxfct, mnum, mtype, n, nrhs, msglvl, err;
83: /* Matrix structure */
84: PetscScalar *a;
86: INT_TYPE *ia, *ja;
88: /* Number of non-zero elements */
89: INT_TYPE nz;
91: /* Row permutaton vector*/
92: INT_TYPE *perm;
94: /* Define is matrix preserve sparce structure. */
95: MatStructure matstruc;
97: PetscErrorCode (*ConvertToTriples)(Mat, MatReuse, PetscInt*, PetscInt**, PetscInt**, PetscScalar**);
99: /* True if mkl_cpardiso function have been used. */
100: PetscBool CleanUp;
101: } Mat_MKL_CPARDISO;
103: /*
104: * Copy the elements of matrix A.
105: * Input:
106: * - Mat A: MATSEQAIJ matrix
107: * - int shift: matrix index.
108: * - 0 for c representation
109: * - 1 for fortran representation
110: * - MatReuse reuse:
111: * - MAT_INITIAL_MATRIX: Create a new aij representation
112: * - MAT_REUSE_MATRIX: Reuse all aij representation and just change values
113: * Output:
114: * - int *nnz: Number of nonzero-elements.
115: * - int **r pointer to i index
116: * - int **c pointer to j elements
117: * - MATRIXTYPE **v: Non-zero elements
118: */
119: PetscErrorCode MatCopy_seqaij_seqaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
120: {
121: Mat_SeqAIJ *aa=(Mat_SeqAIJ*)A->data;
124: *v=aa->a;
125: if (reuse == MAT_INITIAL_MATRIX) {
126: *r = (INT_TYPE*)aa->i;
127: *c = (INT_TYPE*)aa->j;
128: *nnz = aa->nz;
129: }
130: return(0);
131: }
133: PetscErrorCode MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO(Mat A, MatReuse reuse, PetscInt *nnz, PetscInt **r, PetscInt **c, PetscScalar **v)
134: {
135: const PetscInt *ai, *aj, *bi, *bj,*garray,m=A->rmap->n,*ajj,*bjj;
136: PetscErrorCode ierr;
137: PetscInt rstart,nz,i,j,countA,countB;
138: PetscInt *row,*col;
139: const PetscScalar *av, *bv;
140: PetscScalar *val;
141: Mat_MPIAIJ *mat = (Mat_MPIAIJ*)A->data;
142: Mat_SeqAIJ *aa = (Mat_SeqAIJ*)(mat->A)->data;
143: Mat_SeqAIJ *bb = (Mat_SeqAIJ*)(mat->B)->data;
144: PetscInt colA_start,jB,jcol;
147: ai=aa->i; aj=aa->j; bi=bb->i; bj=bb->j; rstart= A->rmap->rstart;
148: av=aa->a; bv=bb->a;
150: garray = mat->garray;
152: if (reuse == MAT_INITIAL_MATRIX) {
153: nz = aa->nz + bb->nz;
154: *nnz = nz;
155: PetscMalloc((nz*(sizeof(PetscInt)+sizeof(PetscScalar)) + (m+1)*sizeof(PetscInt)), &row);
156: col = row + m + 1;
157: val = (PetscScalar*)(col + nz);
158: *r = row; *c = col; *v = val;
159: row[0] = 0;
160: } else {
161: row = *r; col = *c; val = *v;
162: }
164: nz = 0;
165: for (i=0; i<m; i++) {
166: row[i] = nz;
167: countA = ai[i+1] - ai[i];
168: countB = bi[i+1] - bi[i];
169: ajj = aj + ai[i]; /* ptr to the beginning of this row */
170: bjj = bj + bi[i];
172: /* B part, smaller col index */
173: colA_start = rstart + ajj[0]; /* the smallest global col index of A */
174: jB = 0;
175: for (j=0; j<countB; j++) {
176: jcol = garray[bjj[j]];
177: if (jcol > colA_start) {
178: jB = j;
179: break;
180: }
181: col[nz] = jcol;
182: val[nz++] = *bv++;
183: if (j==countB-1) jB = countB;
184: }
186: /* A part */
187: for (j=0; j<countA; j++) {
188: col[nz] = rstart + ajj[j];
189: val[nz++] = *av++;
190: }
192: /* B part, larger col index */
193: for (j=jB; j<countB; j++) {
194: col[nz] = garray[bjj[j]];
195: val[nz++] = *bv++;
196: }
197: }
198: row[m] = nz;
200: return(0);
201: }
203: /*
204: * Free memory for Mat_MKL_CPARDISO structure and pointers to objects.
205: */
206: PetscErrorCode MatDestroy_MKL_CPARDISO(Mat A)
207: {
208: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data;
209: PetscErrorCode ierr;
212: /* Terminate instance, deallocate memories */
213: if (mat_mkl_cpardiso->CleanUp) {
214: mat_mkl_cpardiso->phase = JOB_RELEASE_OF_ALL_MEMORY;
216: cluster_sparse_solver (
217: mat_mkl_cpardiso->pt,
218: &mat_mkl_cpardiso->maxfct,
219: &mat_mkl_cpardiso->mnum,
220: &mat_mkl_cpardiso->mtype,
221: &mat_mkl_cpardiso->phase,
222: &mat_mkl_cpardiso->n,
223: NULL,
224: NULL,
225: NULL,
226: mat_mkl_cpardiso->perm,
227: &mat_mkl_cpardiso->nrhs,
228: mat_mkl_cpardiso->iparm,
229: &mat_mkl_cpardiso->msglvl,
230: NULL,
231: NULL,
232: &mat_mkl_cpardiso->comm_mkl_cpardiso,
233: (int*)&mat_mkl_cpardiso->err);
234: }
236: if (mat_mkl_cpardiso->ConvertToTriples == MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO) {
237: PetscFree(mat_mkl_cpardiso->ia);
238: }
239: MPI_Comm_free(&(mat_mkl_cpardiso->comm_mkl_cpardiso));
240: PetscFree(A->data);
242: /* clear composed functions */
243: PetscObjectComposeFunction((PetscObject)A,"MatFactorGetSolverType_C",NULL);
244: PetscObjectComposeFunction((PetscObject)A,"MatMkl_CPardisoSetCntl_C",NULL);
245: return(0);
246: }
248: /*
249: * Computes Ax = b
250: */
251: PetscErrorCode MatSolve_MKL_CPARDISO(Mat A,Vec b,Vec x)
252: {
253: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(A)->data;
254: PetscErrorCode ierr;
255: PetscScalar *xarray;
256: const PetscScalar *barray;
259: mat_mkl_cpardiso->nrhs = 1;
260: VecGetArray(x,&xarray);
261: VecGetArrayRead(b,&barray);
263: /* solve phase */
264: /*-------------*/
265: mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
266: cluster_sparse_solver (
267: mat_mkl_cpardiso->pt,
268: &mat_mkl_cpardiso->maxfct,
269: &mat_mkl_cpardiso->mnum,
270: &mat_mkl_cpardiso->mtype,
271: &mat_mkl_cpardiso->phase,
272: &mat_mkl_cpardiso->n,
273: mat_mkl_cpardiso->a,
274: mat_mkl_cpardiso->ia,
275: mat_mkl_cpardiso->ja,
276: mat_mkl_cpardiso->perm,
277: &mat_mkl_cpardiso->nrhs,
278: mat_mkl_cpardiso->iparm,
279: &mat_mkl_cpardiso->msglvl,
280: (void*)barray,
281: (void*)xarray,
282: &mat_mkl_cpardiso->comm_mkl_cpardiso,
283: (int*)&mat_mkl_cpardiso->err);
285: 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));
287: VecRestoreArray(x,&xarray);
288: VecRestoreArrayRead(b,&barray);
289: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
290: return(0);
291: }
293: PetscErrorCode MatSolveTranspose_MKL_CPARDISO(Mat A,Vec b,Vec x)
294: {
295: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data;
296: PetscErrorCode ierr;
299: #if defined(PETSC_USE_COMPLEX)
300: mat_mkl_cpardiso->iparm[12 - 1] = 1;
301: #else
302: mat_mkl_cpardiso->iparm[12 - 1] = 2;
303: #endif
304: MatSolve_MKL_CPARDISO(A,b,x);
305: mat_mkl_cpardiso->iparm[12 - 1] = 0;
306: return(0);
307: }
309: PetscErrorCode MatMatSolve_MKL_CPARDISO(Mat A,Mat B,Mat X)
310: {
311: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(A)->data;
312: PetscErrorCode ierr;
313: PetscScalar *xarray;
314: const PetscScalar *barray;
317: MatGetSize(B,NULL,(PetscInt*)&mat_mkl_cpardiso->nrhs);
319: if(mat_mkl_cpardiso->nrhs > 0){
320: MatDenseGetArrayRead(B,&barray);
321: MatDenseGetArray(X,&xarray);
323: /* solve phase */
324: /*-------------*/
325: mat_mkl_cpardiso->phase = JOB_SOLVE_ITERATIVE_REFINEMENT;
326: cluster_sparse_solver (
327: mat_mkl_cpardiso->pt,
328: &mat_mkl_cpardiso->maxfct,
329: &mat_mkl_cpardiso->mnum,
330: &mat_mkl_cpardiso->mtype,
331: &mat_mkl_cpardiso->phase,
332: &mat_mkl_cpardiso->n,
333: mat_mkl_cpardiso->a,
334: mat_mkl_cpardiso->ia,
335: mat_mkl_cpardiso->ja,
336: mat_mkl_cpardiso->perm,
337: &mat_mkl_cpardiso->nrhs,
338: mat_mkl_cpardiso->iparm,
339: &mat_mkl_cpardiso->msglvl,
340: (void*)barray,
341: (void*)xarray,
342: &mat_mkl_cpardiso->comm_mkl_cpardiso,
343: (int*)&mat_mkl_cpardiso->err);
344: 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));
345: MatDenseRestoreArrayRead(B,&barray);
346: MatDenseRestoreArray(X,&xarray);
348: }
349: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
350: return(0);
352: }
354: /*
355: * LU Decomposition
356: */
357: PetscErrorCode MatFactorNumeric_MKL_CPARDISO(Mat F,Mat A,const MatFactorInfo *info)
358: {
359: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)(F)->data;
360: PetscErrorCode ierr;
363: mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
364: (*mat_mkl_cpardiso->ConvertToTriples)(A, MAT_REUSE_MATRIX,&mat_mkl_cpardiso->nz,&mat_mkl_cpardiso->ia,&mat_mkl_cpardiso->ja,&mat_mkl_cpardiso->a);
366: mat_mkl_cpardiso->phase = JOB_NUMERICAL_FACTORIZATION;
367: cluster_sparse_solver (
368: mat_mkl_cpardiso->pt,
369: &mat_mkl_cpardiso->maxfct,
370: &mat_mkl_cpardiso->mnum,
371: &mat_mkl_cpardiso->mtype,
372: &mat_mkl_cpardiso->phase,
373: &mat_mkl_cpardiso->n,
374: mat_mkl_cpardiso->a,
375: mat_mkl_cpardiso->ia,
376: mat_mkl_cpardiso->ja,
377: mat_mkl_cpardiso->perm,
378: &mat_mkl_cpardiso->nrhs,
379: mat_mkl_cpardiso->iparm,
380: &mat_mkl_cpardiso->msglvl,
381: NULL,
382: NULL,
383: &mat_mkl_cpardiso->comm_mkl_cpardiso,
384: &mat_mkl_cpardiso->err);
385: 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));
387: mat_mkl_cpardiso->matstruc = SAME_NONZERO_PATTERN;
388: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
389: return(0);
390: }
392: /* Sets mkl_cpardiso options from the options database */
393: PetscErrorCode PetscSetMKL_CPARDISOFromOptions(Mat F, Mat A)
394: {
395: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO*)F->data;
396: PetscErrorCode ierr;
397: PetscInt icntl;
398: PetscBool flg;
399: int threads;
402: PetscOptionsBegin(PetscObjectComm((PetscObject)A),((PetscObject)A)->prefix,"MKL_CPARDISO Options","Mat");
403: PetscOptionsInt("-mat_mkl_cpardiso_65","Number of threads to use","None",threads,&threads,&flg);
404: if (flg) mkl_set_num_threads(threads);
406: 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);
407: if (flg) mat_mkl_cpardiso->maxfct = icntl;
409: PetscOptionsInt("-mat_mkl_cpardiso_67","Indicates the actual matrix for the solution phase","None",mat_mkl_cpardiso->mnum,&icntl,&flg);
410: if (flg) mat_mkl_cpardiso->mnum = icntl;
412: PetscOptionsInt("-mat_mkl_cpardiso_68","Message level information","None",mat_mkl_cpardiso->msglvl,&icntl,&flg);
413: if (flg) mat_mkl_cpardiso->msglvl = icntl;
415: PetscOptionsInt("-mat_mkl_cpardiso_69","Defines the matrix type","None",mat_mkl_cpardiso->mtype,&icntl,&flg);
416: if(flg){
417: mat_mkl_cpardiso->mtype = icntl;
418: #if defined(PETSC_USE_REAL_SINGLE)
419: mat_mkl_cpardiso->iparm[27] = 1;
420: #else
421: mat_mkl_cpardiso->iparm[27] = 0;
422: #endif
423: mat_mkl_cpardiso->iparm[34] = 1;
424: }
425: PetscOptionsInt("-mat_mkl_cpardiso_1","Use default values","None",mat_mkl_cpardiso->iparm[0],&icntl,&flg);
427: if(flg && icntl != 0){
428: PetscOptionsInt("-mat_mkl_cpardiso_2","Fill-in reducing ordering for the input matrix","None",mat_mkl_cpardiso->iparm[1],&icntl,&flg);
429: if (flg) mat_mkl_cpardiso->iparm[1] = icntl;
431: PetscOptionsInt("-mat_mkl_cpardiso_4","Preconditioned CGS/CG","None",mat_mkl_cpardiso->iparm[3],&icntl,&flg);
432: if (flg) mat_mkl_cpardiso->iparm[3] = icntl;
434: PetscOptionsInt("-mat_mkl_cpardiso_5","User permutation","None",mat_mkl_cpardiso->iparm[4],&icntl,&flg);
435: if (flg) mat_mkl_cpardiso->iparm[4] = icntl;
437: PetscOptionsInt("-mat_mkl_cpardiso_6","Write solution on x","None",mat_mkl_cpardiso->iparm[5],&icntl,&flg);
438: if (flg) mat_mkl_cpardiso->iparm[5] = icntl;
440: PetscOptionsInt("-mat_mkl_cpardiso_8","Iterative refinement step","None",mat_mkl_cpardiso->iparm[7],&icntl,&flg);
441: if (flg) mat_mkl_cpardiso->iparm[7] = icntl;
443: PetscOptionsInt("-mat_mkl_cpardiso_10","Pivoting perturbation","None",mat_mkl_cpardiso->iparm[9],&icntl,&flg);
444: if (flg) mat_mkl_cpardiso->iparm[9] = icntl;
446: PetscOptionsInt("-mat_mkl_cpardiso_11","Scaling vectors","None",mat_mkl_cpardiso->iparm[10],&icntl,&flg);
447: if (flg) mat_mkl_cpardiso->iparm[10] = icntl;
449: PetscOptionsInt("-mat_mkl_cpardiso_12","Solve with transposed or conjugate transposed matrix A","None",mat_mkl_cpardiso->iparm[11],&icntl,&flg);
450: if (flg) mat_mkl_cpardiso->iparm[11] = icntl;
452: PetscOptionsInt("-mat_mkl_cpardiso_13","Improved accuracy using (non-) symmetric weighted matching","None",mat_mkl_cpardiso->iparm[12],&icntl,
453: &flg);
454: if (flg) mat_mkl_cpardiso->iparm[12] = icntl;
456: PetscOptionsInt("-mat_mkl_cpardiso_18","Numbers of non-zero elements","None",mat_mkl_cpardiso->iparm[17],&icntl,
457: &flg);
458: if (flg) mat_mkl_cpardiso->iparm[17] = icntl;
460: PetscOptionsInt("-mat_mkl_cpardiso_19","Report number of floating point operations","None",mat_mkl_cpardiso->iparm[18],&icntl,&flg);
461: if (flg) mat_mkl_cpardiso->iparm[18] = icntl;
463: PetscOptionsInt("-mat_mkl_cpardiso_21","Pivoting for symmetric indefinite matrices","None",mat_mkl_cpardiso->iparm[20],&icntl,&flg);
464: if (flg) mat_mkl_cpardiso->iparm[20] = icntl;
466: PetscOptionsInt("-mat_mkl_cpardiso_24","Parallel factorization control","None",mat_mkl_cpardiso->iparm[23],&icntl,&flg);
467: if (flg) mat_mkl_cpardiso->iparm[23] = icntl;
469: PetscOptionsInt("-mat_mkl_cpardiso_25","Parallel forward/backward solve control","None",mat_mkl_cpardiso->iparm[24],&icntl,&flg);
470: if (flg) mat_mkl_cpardiso->iparm[24] = icntl;
472: PetscOptionsInt("-mat_mkl_cpardiso_27","Matrix checker","None",mat_mkl_cpardiso->iparm[26],&icntl,&flg);
473: if (flg) mat_mkl_cpardiso->iparm[26] = icntl;
475: PetscOptionsInt("-mat_mkl_cpardiso_31","Partial solve and computing selected components of the solution vectors","None",mat_mkl_cpardiso->iparm[30],&icntl,&flg);
476: if (flg) mat_mkl_cpardiso->iparm[30] = icntl;
478: PetscOptionsInt("-mat_mkl_cpardiso_34","Optimal number of threads for conditional numerical reproducibility (CNR) mode","None",mat_mkl_cpardiso->iparm[33],&icntl,&flg);
479: if (flg) mat_mkl_cpardiso->iparm[33] = icntl;
481: PetscOptionsInt("-mat_mkl_cpardiso_60","Intel MKL_CPARDISO mode","None",mat_mkl_cpardiso->iparm[59],&icntl,&flg);
482: if (flg) mat_mkl_cpardiso->iparm[59] = icntl;
483: }
485: PetscOptionsEnd();
486: return(0);
487: }
489: PetscErrorCode PetscInitialize_MKL_CPARDISO(Mat A, Mat_MKL_CPARDISO *mat_mkl_cpardiso)
490: {
491: PetscErrorCode ierr;
492: PetscMPIInt size;
496: MPI_Comm_dup(PetscObjectComm((PetscObject)A),&(mat_mkl_cpardiso->comm_mkl_cpardiso));
497: MPI_Comm_size(mat_mkl_cpardiso->comm_mkl_cpardiso, &size);
499: mat_mkl_cpardiso->CleanUp = PETSC_FALSE;
500: mat_mkl_cpardiso->maxfct = 1;
501: mat_mkl_cpardiso->mnum = 1;
502: mat_mkl_cpardiso->n = A->rmap->N;
503: mat_mkl_cpardiso->msglvl = 0;
504: mat_mkl_cpardiso->nrhs = 1;
505: mat_mkl_cpardiso->err = 0;
506: mat_mkl_cpardiso->phase = -1;
507: #if defined(PETSC_USE_COMPLEX)
508: mat_mkl_cpardiso->mtype = 13;
509: #else
510: mat_mkl_cpardiso->mtype = 11;
511: #endif
513: #if defined(PETSC_USE_REAL_SINGLE)
514: mat_mkl_cpardiso->iparm[27] = 1;
515: #else
516: mat_mkl_cpardiso->iparm[27] = 0;
517: #endif
519: mat_mkl_cpardiso->iparm[34] = 1; /* C style */
521: mat_mkl_cpardiso->iparm[ 0] = 1; /* Solver default parameters overriden with provided by iparm */
522: mat_mkl_cpardiso->iparm[ 1] = 2; /* Use METIS for fill-in reordering */
523: mat_mkl_cpardiso->iparm[ 5] = 0; /* Write solution into x */
524: mat_mkl_cpardiso->iparm[ 7] = 2; /* Max number of iterative refinement steps */
525: mat_mkl_cpardiso->iparm[ 9] = 13; /* Perturb the pivot elements with 1E-13 */
526: mat_mkl_cpardiso->iparm[10] = 1; /* Use nonsymmetric permutation and scaling MPS */
527: mat_mkl_cpardiso->iparm[12] = 1; /* Switch on Maximum Weighted Matching algorithm (default for non-symmetric) */
528: mat_mkl_cpardiso->iparm[17] = -1; /* Output: Number of nonzeros in the factor LU */
529: mat_mkl_cpardiso->iparm[18] = -1; /* Output: Mflops for LU factorization */
530: mat_mkl_cpardiso->iparm[26] = 1; /* Check input data for correctness */
532: mat_mkl_cpardiso->iparm[39] = 0;
533: if (size > 1) {
534: mat_mkl_cpardiso->iparm[39] = 2;
535: mat_mkl_cpardiso->iparm[40] = A->rmap->rstart;
536: mat_mkl_cpardiso->iparm[41] = A->rmap->rend-1;
537: }
538: mat_mkl_cpardiso->perm = 0;
539: return(0);
540: }
542: /*
543: * Symbolic decomposition. Mkl_Pardiso analysis phase.
544: */
545: PetscErrorCode MatLUFactorSymbolic_AIJMKL_CPARDISO(Mat F,Mat A,IS r,IS c,const MatFactorInfo *info)
546: {
547: Mat_MKL_CPARDISO *mat_mkl_cpardiso = (Mat_MKL_CPARDISO*)F->data;
548: PetscErrorCode ierr;
551: mat_mkl_cpardiso->matstruc = DIFFERENT_NONZERO_PATTERN;
553: /* Set MKL_CPARDISO options from the options database */
554: PetscSetMKL_CPARDISOFromOptions(F,A);
555: (*mat_mkl_cpardiso->ConvertToTriples)(A,MAT_INITIAL_MATRIX,&mat_mkl_cpardiso->nz,&mat_mkl_cpardiso->ia,&mat_mkl_cpardiso->ja,&mat_mkl_cpardiso->a);
557: mat_mkl_cpardiso->n = A->rmap->N;
559: /* analysis phase */
560: /*----------------*/
561: mat_mkl_cpardiso->phase = JOB_ANALYSIS;
563: cluster_sparse_solver (
564: mat_mkl_cpardiso->pt,
565: &mat_mkl_cpardiso->maxfct,
566: &mat_mkl_cpardiso->mnum,
567: &mat_mkl_cpardiso->mtype,
568: &mat_mkl_cpardiso->phase,
569: &mat_mkl_cpardiso->n,
570: mat_mkl_cpardiso->a,
571: mat_mkl_cpardiso->ia,
572: mat_mkl_cpardiso->ja,
573: mat_mkl_cpardiso->perm,
574: &mat_mkl_cpardiso->nrhs,
575: mat_mkl_cpardiso->iparm,
576: &mat_mkl_cpardiso->msglvl,
577: NULL,
578: NULL,
579: &mat_mkl_cpardiso->comm_mkl_cpardiso,
580: (int*)&mat_mkl_cpardiso->err);
582: 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));
584: mat_mkl_cpardiso->CleanUp = PETSC_TRUE;
585: F->ops->lufactornumeric = MatFactorNumeric_MKL_CPARDISO;
586: F->ops->solve = MatSolve_MKL_CPARDISO;
587: F->ops->solvetranspose = MatSolveTranspose_MKL_CPARDISO;
588: F->ops->matsolve = MatMatSolve_MKL_CPARDISO;
589: return(0);
590: }
592: PetscErrorCode MatView_MKL_CPARDISO(Mat A, PetscViewer viewer)
593: {
594: PetscErrorCode ierr;
595: PetscBool iascii;
596: PetscViewerFormat format;
597: Mat_MKL_CPARDISO *mat_mkl_cpardiso=(Mat_MKL_CPARDISO*)A->data;
598: PetscInt i;
601: /* check if matrix is mkl_cpardiso type */
602: if (A->ops->solve != MatSolve_MKL_CPARDISO) return(0);
604: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
605: if (iascii) {
606: PetscViewerGetFormat(viewer,&format);
607: if (format == PETSC_VIEWER_ASCII_INFO) {
608: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO run parameters:\n");
609: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO phase: %d \n",mat_mkl_cpardiso->phase);
610: for(i = 1; i <= 64; i++){
611: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO iparm[%d]: %d \n",i, mat_mkl_cpardiso->iparm[i - 1]);
612: }
613: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO maxfct: %d \n", mat_mkl_cpardiso->maxfct);
614: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO mnum: %d \n", mat_mkl_cpardiso->mnum);
615: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO mtype: %d \n", mat_mkl_cpardiso->mtype);
616: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO n: %d \n", mat_mkl_cpardiso->n);
617: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO nrhs: %d \n", mat_mkl_cpardiso->nrhs);
618: PetscViewerASCIIPrintf(viewer,"MKL_CPARDISO msglvl: %d \n", mat_mkl_cpardiso->msglvl);
619: }
620: }
621: return(0);
622: }
624: PetscErrorCode MatGetInfo_MKL_CPARDISO(Mat A, MatInfoType flag, MatInfo *info)
625: {
626: Mat_MKL_CPARDISO *mat_mkl_cpardiso =(Mat_MKL_CPARDISO*)A->data;
629: info->block_size = 1.0;
630: info->nz_allocated = mat_mkl_cpardiso->nz + 0.0;
631: info->nz_unneeded = 0.0;
632: info->assemblies = 0.0;
633: info->mallocs = 0.0;
634: info->memory = 0.0;
635: info->fill_ratio_given = 0;
636: info->fill_ratio_needed = 0;
637: info->factor_mallocs = 0;
638: return(0);
639: }
641: PetscErrorCode MatMkl_CPardisoSetCntl_MKL_CPARDISO(Mat F,PetscInt icntl,PetscInt ival)
642: {
643: Mat_MKL_CPARDISO *mat_mkl_cpardiso =(Mat_MKL_CPARDISO*)F->data;
646: if(icntl <= 64){
647: mat_mkl_cpardiso->iparm[icntl - 1] = ival;
648: } else {
649: if(icntl == 65) mkl_set_num_threads((int)ival);
650: else if(icntl == 66) mat_mkl_cpardiso->maxfct = ival;
651: else if(icntl == 67) mat_mkl_cpardiso->mnum = ival;
652: else if(icntl == 68) mat_mkl_cpardiso->msglvl = ival;
653: else if(icntl == 69){
654: mat_mkl_cpardiso->mtype = ival;
655: #if defined(PETSC_USE_REAL_SINGLE)
656: mat_mkl_cpardiso->iparm[27] = 1;
657: #else
658: mat_mkl_cpardiso->iparm[27] = 0;
659: #endif
660: mat_mkl_cpardiso->iparm[34] = 1;
661: }
662: }
663: return(0);
664: }
666: /*@
667: MatMkl_CPardisoSetCntl - Set Mkl_Pardiso parameters
669: Logically Collective on Mat
671: Input Parameters:
672: + F - the factored matrix obtained by calling MatGetFactor()
673: . icntl - index of Mkl_Pardiso parameter
674: - ival - value of Mkl_Pardiso parameter
676: Options Database:
677: . -mat_mkl_cpardiso_<icntl> <ival>
679: Level: Intermediate
681: Notes: 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
682: database approach when working with TS, SNES, or KSP.
684: References:
685: . Mkl_Pardiso Users' Guide
687: .seealso: MatGetFactor()
688: @*/
689: PetscErrorCode MatMkl_CPardisoSetCntl(Mat F,PetscInt icntl,PetscInt ival)
690: {
694: PetscTryMethod(F,"MatMkl_CPardisoSetCntl_C",(Mat,PetscInt,PetscInt),(F,icntl,ival));
695: return(0);
696: }
698: static PetscErrorCode MatFactorGetSolverType_mkl_cpardiso(Mat A, MatSolverType *type)
699: {
701: *type = MATSOLVERMKL_CPARDISO;
702: return(0);
703: }
705: /* MatGetFactor for MPI AIJ matrices */
706: static PetscErrorCode MatGetFactor_mpiaij_mkl_cpardiso(Mat A,MatFactorType ftype,Mat *F)
707: {
708: Mat B;
709: PetscErrorCode ierr;
710: Mat_MKL_CPARDISO *mat_mkl_cpardiso;
711: PetscBool isSeqAIJ;
714: /* Create the factorization matrix */
716: PetscObjectTypeCompare((PetscObject)A,MATSEQAIJ,&isSeqAIJ);
717: MatCreate(PetscObjectComm((PetscObject)A),&B);
718: MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
719: PetscStrallocpy("mkl_cpardiso",&((PetscObject)B)->type_name);
720: MatSetUp(B);
722: PetscNewLog(B,&mat_mkl_cpardiso);
724: if (isSeqAIJ) mat_mkl_cpardiso->ConvertToTriples = MatCopy_seqaij_seqaij_MKL_CPARDISO;
725: else mat_mkl_cpardiso->ConvertToTriples = MatConvertToTriples_mpiaij_mpiaij_MKL_CPARDISO;
727: B->ops->lufactorsymbolic = MatLUFactorSymbolic_AIJMKL_CPARDISO;
728: B->ops->destroy = MatDestroy_MKL_CPARDISO;
730: B->ops->view = MatView_MKL_CPARDISO;
731: B->ops->getinfo = MatGetInfo_MKL_CPARDISO;
733: B->factortype = ftype;
734: B->assembled = PETSC_TRUE; /* required by -ksp_view */
736: B->data = mat_mkl_cpardiso;
738: /* set solvertype */
739: PetscFree(B->solvertype);
740: PetscStrallocpy(MATSOLVERMKL_CPARDISO,&B->solvertype);
742: PetscObjectComposeFunction((PetscObject)B,"MatFactorGetSolverType_C",MatFactorGetSolverType_mkl_cpardiso);
743: PetscObjectComposeFunction((PetscObject)B,"MatMkl_CPardisoSetCntl_C",MatMkl_CPardisoSetCntl_MKL_CPARDISO);
744: PetscInitialize_MKL_CPARDISO(A, mat_mkl_cpardiso);
746: *F = B;
747: return(0);
748: }
750: PETSC_EXTERN PetscErrorCode MatSolverTypeRegister_MKL_CPardiso(void)
751: {
753:
755: MatSolverTypeRegister(MATSOLVERMKL_CPARDISO,MATMPIAIJ,MAT_FACTOR_LU,MatGetFactor_mpiaij_mkl_cpardiso);
756: MatSolverTypeRegister(MATSOLVERMKL_CPARDISO,MATSEQAIJ,MAT_FACTOR_LU,MatGetFactor_mpiaij_mkl_cpardiso);
757: return(0);
758: }