Actual source code: ispai.c


  2: /*
  3:    3/99 Modified by Stephen Barnard to support SPAI version 3.0
  4: */

  6: /*
  7:       Provides an interface to the SPAI Sparse Approximate Inverse Preconditioner
  8:    Code written by Stephen Barnard.

 10:       Note: there is some BAD memory bleeding below!

 12:       This code needs work

 14:    1) get rid of all memory bleeding
 15:    2) fix PETSc/interface so that it gets if the matrix is symmetric from the matrix
 16:       rather than having the sp flag for PC_SPAI
 17:    3) fix to set the block size based on the matrix block size

 19: */
 20: #define PETSC_SKIP_COMPLEX /* since spai uses I which conflicts with some complex implementations */

 22: #include <petsc/private/pcimpl.h>
 23: #include <../src/ksp/pc/impls/spai/petscspai.h>

 25: /*
 26:     These are the SPAI include files
 27: */
 28: EXTERN_C_BEGIN
 29: #define SPAI_USE_MPI /* required for setting SPAI_Comm correctly in basics.h */
 30: #include <spai.h>
 31: #include <matrix.h>
 32: EXTERN_C_END

 34: extern PetscErrorCode ConvertMatToMatrix(MPI_Comm, Mat, Mat, matrix **);
 35: extern PetscErrorCode ConvertMatrixToMat(MPI_Comm, matrix *, Mat *);
 36: extern PetscErrorCode ConvertVectorToVec(MPI_Comm, vector *, Vec *);
 37: extern PetscErrorCode MM_to_PETSC(char *, char *, char *);

 39: typedef struct {
 40:   matrix *B;  /* matrix in SPAI format */
 41:   matrix *BT; /* transpose of matrix in SPAI format */
 42:   matrix *M;  /* the approximate inverse in SPAI format */

 44:   Mat PM; /* the approximate inverse PETSc format */

 46:   double epsilon;    /* tolerance */
 47:   int    nbsteps;    /* max number of "improvement" steps per line */
 48:   int    max;        /* max dimensions of is_I, q, etc. */
 49:   int    maxnew;     /* max number of new entries per step */
 50:   int    block_size; /* constant block size */
 51:   int    cache_size; /* one of (1,2,3,4,5,6) indicting size of cache */
 52:   int    verbose;    /* SPAI prints timing and statistics */

 54:   int      sp;        /* symmetric nonzero pattern */
 55:   MPI_Comm comm_spai; /* communicator to be used with spai */
 56: } PC_SPAI;

 58: static PetscErrorCode PCSetUp_SPAI(PC pc)
 59: {
 60:   PC_SPAI *ispai = (PC_SPAI *)pc->data;
 61:   Mat      AT;

 63:   PetscFunctionBegin;
 64:   init_SPAI();

 66:   if (ispai->sp) {
 67:     PetscCall(ConvertMatToMatrix(ispai->comm_spai, pc->pmat, pc->pmat, &ispai->B));
 68:   } else {
 69:     /* Use the transpose to get the column nonzero structure. */
 70:     PetscCall(MatTranspose(pc->pmat, MAT_INITIAL_MATRIX, &AT));
 71:     PetscCall(ConvertMatToMatrix(ispai->comm_spai, pc->pmat, AT, &ispai->B));
 72:     PetscCall(MatDestroy(&AT));
 73:   }

 75:   /* Destroy the transpose */
 76:   /* Don't know how to do it. PETSc developers? */

 78:   /* construct SPAI preconditioner */
 79:   /* FILE *messages */    /* file for warning messages */
 80:   /* double epsilon */    /* tolerance */
 81:   /* int nbsteps */       /* max number of "improvement" steps per line */
 82:   /* int max */           /* max dimensions of is_I, q, etc. */
 83:   /* int maxnew */        /* max number of new entries per step */
 84:   /* int block_size */    /* block_size == 1 specifies scalar elements
 85:                               block_size == n specifies nxn constant-block elements
 86:                               block_size == 0 specifies variable-block elements */
 87:   /* int cache_size */    /* one of (1,2,3,4,5,6) indicting size of cache. cache_size == 0 indicates no caching */
 88:   /* int    verbose    */ /* verbose == 0 specifies that SPAI is silent
 89:                               verbose == 1 prints timing and matrix statistics */

 91:   PetscCallExternal(bspai, ispai->B, &ispai->M, stdout, ispai->epsilon, ispai->nbsteps, ispai->max, ispai->maxnew, ispai->block_size, ispai->cache_size, ispai->verbose);

 93:   PetscCall(ConvertMatrixToMat(PetscObjectComm((PetscObject)pc), ispai->M, &ispai->PM));

 95:   /* free the SPAI matrices */
 96:   sp_free_matrix(ispai->B);
 97:   sp_free_matrix(ispai->M);
 98:   PetscFunctionReturn(PETSC_SUCCESS);
 99: }

101: static PetscErrorCode PCApply_SPAI(PC pc, Vec xx, Vec y)
102: {
103:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

105:   PetscFunctionBegin;
106:   /* Now using PETSc's multiply */
107:   PetscCall(MatMult(ispai->PM, xx, y));
108:   PetscFunctionReturn(PETSC_SUCCESS);
109: }

111: static PetscErrorCode PCMatApply_SPAI(PC pc, Mat X, Mat Y)
112: {
113:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

115:   PetscFunctionBegin;
116:   /* Now using PETSc's multiply */
117:   PetscCall(MatMatMult(ispai->PM, X, MAT_REUSE_MATRIX, PETSC_DEFAULT, &Y));
118:   PetscFunctionReturn(PETSC_SUCCESS);
119: }

121: static PetscErrorCode PCDestroy_SPAI(PC pc)
122: {
123:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

125:   PetscFunctionBegin;
126:   PetscCall(MatDestroy(&ispai->PM));
127:   PetscCallMPI(MPI_Comm_free(&(ispai->comm_spai)));
128:   PetscCall(PetscFree(pc->data));
129:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetEpsilon_C", NULL));
130:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetNBSteps_C", NULL));
131:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetMax_C", NULL));
132:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetMaxNew_C", NULL));
133:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetBlockSize_C", NULL));
134:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetCacheSize_C", NULL));
135:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetVerbose_C", NULL));
136:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetSp_C", NULL));
137:   PetscFunctionReturn(PETSC_SUCCESS);
138: }

140: static PetscErrorCode PCView_SPAI(PC pc, PetscViewer viewer)
141: {
142:   PC_SPAI  *ispai = (PC_SPAI *)pc->data;
143:   PetscBool iascii;

145:   PetscFunctionBegin;
146:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
147:   if (iascii) {
148:     PetscCall(PetscViewerASCIIPrintf(viewer, "    epsilon %g\n", ispai->epsilon));
149:     PetscCall(PetscViewerASCIIPrintf(viewer, "    nbsteps %d\n", ispai->nbsteps));
150:     PetscCall(PetscViewerASCIIPrintf(viewer, "    max %d\n", ispai->max));
151:     PetscCall(PetscViewerASCIIPrintf(viewer, "    maxnew %d\n", ispai->maxnew));
152:     PetscCall(PetscViewerASCIIPrintf(viewer, "    block_size %d\n", ispai->block_size));
153:     PetscCall(PetscViewerASCIIPrintf(viewer, "    cache_size %d\n", ispai->cache_size));
154:     PetscCall(PetscViewerASCIIPrintf(viewer, "    verbose %d\n", ispai->verbose));
155:     PetscCall(PetscViewerASCIIPrintf(viewer, "    sp %d\n", ispai->sp));
156:   }
157:   PetscFunctionReturn(PETSC_SUCCESS);
158: }

160: static PetscErrorCode PCSPAISetEpsilon_SPAI(PC pc, PetscReal epsilon1)
161: {
162:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

164:   PetscFunctionBegin;
165:   ispai->epsilon = (double)epsilon1;
166:   PetscFunctionReturn(PETSC_SUCCESS);
167: }

169: static PetscErrorCode PCSPAISetNBSteps_SPAI(PC pc, PetscInt nbsteps1)
170: {
171:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

173:   PetscFunctionBegin;
174:   ispai->nbsteps = (int)nbsteps1;
175:   PetscFunctionReturn(PETSC_SUCCESS);
176: }

178: /* added 1/7/99 g.h. */
179: static PetscErrorCode PCSPAISetMax_SPAI(PC pc, PetscInt max1)
180: {
181:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

183:   PetscFunctionBegin;
184:   ispai->max = (int)max1;
185:   PetscFunctionReturn(PETSC_SUCCESS);
186: }

188: static PetscErrorCode PCSPAISetMaxNew_SPAI(PC pc, PetscInt maxnew1)
189: {
190:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

192:   PetscFunctionBegin;
193:   ispai->maxnew = (int)maxnew1;
194:   PetscFunctionReturn(PETSC_SUCCESS);
195: }

197: static PetscErrorCode PCSPAISetBlockSize_SPAI(PC pc, PetscInt block_size1)
198: {
199:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

201:   PetscFunctionBegin;
202:   ispai->block_size = (int)block_size1;
203:   PetscFunctionReturn(PETSC_SUCCESS);
204: }

206: static PetscErrorCode PCSPAISetCacheSize_SPAI(PC pc, PetscInt cache_size)
207: {
208:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

210:   PetscFunctionBegin;
211:   ispai->cache_size = (int)cache_size;
212:   PetscFunctionReturn(PETSC_SUCCESS);
213: }

215: static PetscErrorCode PCSPAISetVerbose_SPAI(PC pc, PetscInt verbose)
216: {
217:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

219:   PetscFunctionBegin;
220:   ispai->verbose = (int)verbose;
221:   PetscFunctionReturn(PETSC_SUCCESS);
222: }

224: static PetscErrorCode PCSPAISetSp_SPAI(PC pc, PetscInt sp)
225: {
226:   PC_SPAI *ispai = (PC_SPAI *)pc->data;

228:   PetscFunctionBegin;
229:   ispai->sp = (int)sp;
230:   PetscFunctionReturn(PETSC_SUCCESS);
231: }

233: /*@
234:   PCSPAISetEpsilon -- Set the tolerance for the `PCSPAI` preconditioner

236:   Input Parameters:
237: + pc - the preconditioner
238: - eps - epsilon (default .4)

240:   Note:
241:     Espilon must be between 0 and 1. It controls the
242:                  quality of the approximation of M to the inverse of
243:                  A. Higher values of epsilon lead to more work, more
244:                  fill, and usually better preconditioners. In many
245:                  cases the best choice of epsilon is the one that
246:                  divides the total solution time equally between the
247:                  preconditioner and the solver.

249:   Level: intermediate

251: .seealso: `PCSPAI`, `PCSetType()`
252:   @*/
253: PetscErrorCode PCSPAISetEpsilon(PC pc, PetscReal epsilon1)
254: {
255:   PetscFunctionBegin;
256:   PetscTryMethod(pc, "PCSPAISetEpsilon_C", (PC, PetscReal), (pc, epsilon1));
257:   PetscFunctionReturn(PETSC_SUCCESS);
258: }

260: /*@
261:   PCSPAISetNBSteps - set maximum number of improvement steps per row in
262:         the `PCSPAI` preconditioner

264:   Input Parameters:
265: + pc - the preconditioner
266: - n - number of steps (default 5)

268:   Note:
269:     `PCSPAI` constructs to approximation to every column of
270:                  the exact inverse of A in a series of improvement
271:                  steps. The quality of the approximation is determined
272:                  by epsilon. If an approximation achieving an accuracy
273:                  of epsilon is not obtained after ns steps, SPAI simply
274:                  uses the best approximation constructed so far.

276:   Level: intermediate

278: .seealso: `PCSPAI`, `PCSetType()`, `PCSPAISetMaxNew()`
279: @*/
280: PetscErrorCode PCSPAISetNBSteps(PC pc, PetscInt nbsteps1)
281: {
282:   PetscFunctionBegin;
283:   PetscTryMethod(pc, "PCSPAISetNBSteps_C", (PC, PetscInt), (pc, nbsteps1));
284:   PetscFunctionReturn(PETSC_SUCCESS);
285: }

287: /* added 1/7/99 g.h. */
288: /*@
289:   PCSPAISetMax - set the size of various working buffers in
290:         the `PCSPAI` preconditioner

292:   Input Parameters:
293: + pc - the preconditioner
294: - n - size (default is 5000)

296:   Level: intermediate

298: .seealso: `PCSPAI`, `PCSetType()`
299: @*/
300: PetscErrorCode PCSPAISetMax(PC pc, PetscInt max1)
301: {
302:   PetscFunctionBegin;
303:   PetscTryMethod(pc, "PCSPAISetMax_C", (PC, PetscInt), (pc, max1));
304:   PetscFunctionReturn(PETSC_SUCCESS);
305: }

307: /*@
308:   PCSPAISetMaxNew - set maximum number of new nonzero candidates per step
309:    in `PCSPAI` preconditioner

311:   Input Parameters:
312: + pc - the preconditioner
313: - n - maximum number (default 5)

315:   Level: intermediate

317: .seealso: `PCSPAI`, `PCSetType()`, `PCSPAISetNBSteps()`
318: @*/
319: PetscErrorCode PCSPAISetMaxNew(PC pc, PetscInt maxnew1)
320: {
321:   PetscFunctionBegin;
322:   PetscTryMethod(pc, "PCSPAISetMaxNew_C", (PC, PetscInt), (pc, maxnew1));
323:   PetscFunctionReturn(PETSC_SUCCESS);
324: }

326: /*@
327:   PCSPAISetBlockSize - set the block size for the `PCSPAI` preconditioner

329:   Input Parameters:
330: + pc - the preconditioner
331: - n - block size (default 1)

333:   Notes:
334:     A block
335:                  size of 1 treats A as a matrix of scalar elements. A
336:                  block size of s > 1 treats A as a matrix of sxs
337:                  blocks. A block size of 0 treats A as a matrix with
338:                  variable sized blocks, which are determined by
339:                  searching for dense square diagonal blocks in A.
340:                  This can be very effective for finite-element
341:                  matrices.

343:                  SPAI will convert A to block form, use a block
344:                  version of the preconditioner algorithm, and then
345:                  convert the result back to scalar form.

347:                  In many cases the a block-size parameter other than 1
348:                  can lead to very significant improvement in
349:                  performance.

351:   Level: intermediate

353: .seealso: `PCSPAI`, `PCSetType()`
354: @*/
355: PetscErrorCode PCSPAISetBlockSize(PC pc, PetscInt block_size1)
356: {
357:   PetscFunctionBegin;
358:   PetscTryMethod(pc, "PCSPAISetBlockSize_C", (PC, PetscInt), (pc, block_size1));
359:   PetscFunctionReturn(PETSC_SUCCESS);
360: }

362: /*@
363:   PCSPAISetCacheSize - specify cache size in the `PCSPAI` preconditioner

365:   Input Parameters:
366: + pc - the preconditioner
367: - n -  cache size {0,1,2,3,4,5} (default 5)

369:   Note:
370:     `PCSPAI` uses a hash table to cache messages and avoid
371:                  redundant communication. If suggest always using
372:                  5. This parameter is irrelevant in the serial
373:                  version.

375:   Level: intermediate

377: .seealso: `PCSPAI`, `PCSetType()`
378: @*/
379: PetscErrorCode PCSPAISetCacheSize(PC pc, PetscInt cache_size)
380: {
381:   PetscFunctionBegin;
382:   PetscTryMethod(pc, "PCSPAISetCacheSize_C", (PC, PetscInt), (pc, cache_size));
383:   PetscFunctionReturn(PETSC_SUCCESS);
384: }

386: /*@
387:   PCSPAISetVerbose - verbosity level for the `PCSPAI` preconditioner

389:   Input Parameters:
390: + pc - the preconditioner
391: - n - level (default 1)

393:   Note:
394:     print parameters, timings and matrix statistics

396:   Level: intermediate

398: .seealso: `PCSPAI`, `PCSetType()`
399: @*/
400: PetscErrorCode PCSPAISetVerbose(PC pc, PetscInt verbose)
401: {
402:   PetscFunctionBegin;
403:   PetscTryMethod(pc, "PCSPAISetVerbose_C", (PC, PetscInt), (pc, verbose));
404:   PetscFunctionReturn(PETSC_SUCCESS);
405: }

407: /*@
408:   PCSPAISetSp - specify a symmetric matrix sparsity pattern in the `PCSPAI` preconditioner

410:   Input Parameters:
411: + pc - the preconditioner
412: - n - 0 or 1

414:   Note:
415:     If A has a symmetric nonzero pattern use -sp 1 to
416:                  improve performance by eliminating some communication
417:                  in the parallel version. Even if A does not have a
418:                  symmetric nonzero pattern -sp 1 may well lead to good
419:                  results, but the code will not follow the published
420:                  SPAI algorithm exactly.

422:   Level: intermediate

424: .seealso: `PCSPAI`, `PCSetType()`
425: @*/
426: PetscErrorCode PCSPAISetSp(PC pc, PetscInt sp)
427: {
428:   PetscFunctionBegin;
429:   PetscTryMethod(pc, "PCSPAISetSp_C", (PC, PetscInt), (pc, sp));
430:   PetscFunctionReturn(PETSC_SUCCESS);
431: }

433: static PetscErrorCode PCSetFromOptions_SPAI(PC pc, PetscOptionItems *PetscOptionsObject)
434: {
435:   PC_SPAI  *ispai = (PC_SPAI *)pc->data;
436:   int       nbsteps1, max1, maxnew1, block_size1, cache_size, verbose, sp;
437:   double    epsilon1;
438:   PetscBool flg;

440:   PetscFunctionBegin;
441:   PetscOptionsHeadBegin(PetscOptionsObject, "SPAI options");
442:   PetscCall(PetscOptionsReal("-pc_spai_epsilon", "", "PCSPAISetEpsilon", ispai->epsilon, &epsilon1, &flg));
443:   if (flg) PetscCall(PCSPAISetEpsilon(pc, epsilon1));
444:   PetscCall(PetscOptionsInt("-pc_spai_nbsteps", "", "PCSPAISetNBSteps", ispai->nbsteps, &nbsteps1, &flg));
445:   if (flg) PetscCall(PCSPAISetNBSteps(pc, nbsteps1));
446:   /* added 1/7/99 g.h. */
447:   PetscCall(PetscOptionsInt("-pc_spai_max", "", "PCSPAISetMax", ispai->max, &max1, &flg));
448:   if (flg) PetscCall(PCSPAISetMax(pc, max1));
449:   PetscCall(PetscOptionsInt("-pc_spai_maxnew", "", "PCSPAISetMaxNew", ispai->maxnew, &maxnew1, &flg));
450:   if (flg) PetscCall(PCSPAISetMaxNew(pc, maxnew1));
451:   PetscCall(PetscOptionsInt("-pc_spai_block_size", "", "PCSPAISetBlockSize", ispai->block_size, &block_size1, &flg));
452:   if (flg) PetscCall(PCSPAISetBlockSize(pc, block_size1));
453:   PetscCall(PetscOptionsInt("-pc_spai_cache_size", "", "PCSPAISetCacheSize", ispai->cache_size, &cache_size, &flg));
454:   if (flg) PetscCall(PCSPAISetCacheSize(pc, cache_size));
455:   PetscCall(PetscOptionsInt("-pc_spai_verbose", "", "PCSPAISetVerbose", ispai->verbose, &verbose, &flg));
456:   if (flg) PetscCall(PCSPAISetVerbose(pc, verbose));
457:   PetscCall(PetscOptionsInt("-pc_spai_sp", "", "PCSPAISetSp", ispai->sp, &sp, &flg));
458:   if (flg) PetscCall(PCSPAISetSp(pc, sp));
459:   PetscOptionsHeadEnd();
460:   PetscFunctionReturn(PETSC_SUCCESS);
461: }

463: /*MC
464:    PCSPAI - Use the Sparse Approximate Inverse method

466:    Options Database Keys:
467: +  -pc_spai_epsilon <eps> - set tolerance
468: .  -pc_spai_nbstep <n> - set nbsteps
469: .  -pc_spai_max <m> - set max
470: .  -pc_spai_max_new <m> - set maxnew
471: .  -pc_spai_block_size <n> - set block size
472: .  -pc_spai_cache_size <n> - set cache size
473: .  -pc_spai_sp <m> - set sp
474: -  -pc_spai_set_verbose <true,false> - verbose output

476:    Level: beginner

478:    Note:
479:     This only works with `MATAIJ` matrices.

481:    References:
482:  . * -  Grote and Barnard (SIAM J. Sci. Comput.; vol 18, nr 3)

484: .seealso: `PCCreate()`, `PCSetType()`, `PCType`, `PC`,
485:           `PCSPAISetEpsilon()`, `PCSPAISetMax()`, `PCSPAISetMaxNew()`, `PCSPAISetBlockSize()`,
486:           `PCSPAISetVerbose()`, `PCSPAISetSp()`, `PCSPAISetNBSteps()`, `PCSPAISetCacheSize()`
487: M*/

489: PETSC_EXTERN PetscErrorCode PCCreate_SPAI(PC pc)
490: {
491:   PC_SPAI *ispai;

493:   PetscFunctionBegin;
494:   PetscCall(PetscNew(&ispai));
495:   pc->data = ispai;

497:   pc->ops->destroy         = PCDestroy_SPAI;
498:   pc->ops->apply           = PCApply_SPAI;
499:   pc->ops->matapply        = PCMatApply_SPAI;
500:   pc->ops->applyrichardson = 0;
501:   pc->ops->setup           = PCSetUp_SPAI;
502:   pc->ops->view            = PCView_SPAI;
503:   pc->ops->setfromoptions  = PCSetFromOptions_SPAI;

505:   ispai->epsilon    = .4;
506:   ispai->nbsteps    = 5;
507:   ispai->max        = 5000;
508:   ispai->maxnew     = 5;
509:   ispai->block_size = 1;
510:   ispai->cache_size = 5;
511:   ispai->verbose    = 0;

513:   ispai->sp = 1;
514:   PetscCallMPI(MPI_Comm_dup(PetscObjectComm((PetscObject)pc), &(ispai->comm_spai)));

516:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetEpsilon_C", PCSPAISetEpsilon_SPAI));
517:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetNBSteps_C", PCSPAISetNBSteps_SPAI));
518:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetMax_C", PCSPAISetMax_SPAI));
519:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetMaxNew_C", PCSPAISetMaxNew_SPAI));
520:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetBlockSize_C", PCSPAISetBlockSize_SPAI));
521:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetCacheSize_C", PCSPAISetCacheSize_SPAI));
522:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetVerbose_C", PCSPAISetVerbose_SPAI));
523:   PetscCall(PetscObjectComposeFunction((PetscObject)pc, "PCSPAISetSp_C", PCSPAISetSp_SPAI));
524:   PetscFunctionReturn(PETSC_SUCCESS);
525: }

527: /*
528:    Converts from a PETSc matrix to an SPAI matrix
529: */
530: PetscErrorCode ConvertMatToMatrix(MPI_Comm comm, Mat A, Mat AT, matrix **B)
531: {
532:   matrix                  *M;
533:   int                      i, j, col;
534:   int                      row_indx;
535:   int                      len, pe, local_indx, start_indx;
536:   int                     *mapping;
537:   const int               *cols;
538:   const double            *vals;
539:   int                      n, mnl, nnl, nz, rstart, rend;
540:   PetscMPIInt              size, rank;
541:   struct compressed_lines *rows;

543:   PetscFunctionBegin;
544:   PetscCallMPI(MPI_Comm_size(comm, &size));
545:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
546:   PetscCall(MatGetSize(A, &n, &n));
547:   PetscCall(MatGetLocalSize(A, &mnl, &nnl));

549:   /*
550:     not sure why a barrier is required. commenting out
551:   PetscCallMPI(MPI_Barrier(comm));
552:   */

554:   M = new_matrix((SPAI_Comm)comm);

556:   M->n              = n;
557:   M->bs             = 1;
558:   M->max_block_size = 1;

560:   M->mnls          = (int *)malloc(sizeof(int) * size);
561:   M->start_indices = (int *)malloc(sizeof(int) * size);
562:   M->pe            = (int *)malloc(sizeof(int) * n);
563:   M->block_sizes   = (int *)malloc(sizeof(int) * n);
564:   for (i = 0; i < n; i++) M->block_sizes[i] = 1;

566:   PetscCallMPI(MPI_Allgather(&mnl, 1, MPI_INT, M->mnls, 1, MPI_INT, comm));

568:   M->start_indices[0] = 0;
569:   for (i = 1; i < size; i++) M->start_indices[i] = M->start_indices[i - 1] + M->mnls[i - 1];

571:   M->mnl            = M->mnls[M->myid];
572:   M->my_start_index = M->start_indices[M->myid];

574:   for (i = 0; i < size; i++) {
575:     start_indx = M->start_indices[i];
576:     for (j = 0; j < M->mnls[i]; j++) M->pe[start_indx + j] = i;
577:   }

579:   if (AT) {
580:     M->lines = new_compressed_lines(M->mnls[rank], 1);
581:   } else {
582:     M->lines = new_compressed_lines(M->mnls[rank], 0);
583:   }

585:   rows = M->lines;

587:   /* Determine the mapping from global indices to pointers */
588:   PetscCall(PetscMalloc1(M->n, &mapping));
589:   pe         = 0;
590:   local_indx = 0;
591:   for (i = 0; i < M->n; i++) {
592:     if (local_indx >= M->mnls[pe]) {
593:       pe++;
594:       local_indx = 0;
595:     }
596:     mapping[i] = local_indx + M->start_indices[pe];
597:     local_indx++;
598:   }

600:   /************** Set up the row structure *****************/

602:   PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
603:   for (i = rstart; i < rend; i++) {
604:     row_indx = i - rstart;
605:     PetscCall(MatGetRow(A, i, &nz, &cols, &vals));
606:     /* allocate buffers */
607:     rows->ptrs[row_indx] = (int *)malloc(nz * sizeof(int));
608:     rows->A[row_indx]    = (double *)malloc(nz * sizeof(double));
609:     /* copy the matrix */
610:     for (j = 0; j < nz; j++) {
611:       col = cols[j];
612:       len = rows->len[row_indx]++;

614:       rows->ptrs[row_indx][len] = mapping[col];
615:       rows->A[row_indx][len]    = vals[j];
616:     }
617:     rows->slen[row_indx] = rows->len[row_indx];

619:     PetscCall(MatRestoreRow(A, i, &nz, &cols, &vals));
620:   }

622:   /************** Set up the column structure *****************/

624:   if (AT) {
625:     for (i = rstart; i < rend; i++) {
626:       row_indx = i - rstart;
627:       PetscCall(MatGetRow(AT, i, &nz, &cols, &vals));
628:       /* allocate buffers */
629:       rows->rptrs[row_indx] = (int *)malloc(nz * sizeof(int));
630:       /* copy the matrix (i.e., the structure) */
631:       for (j = 0; j < nz; j++) {
632:         col = cols[j];
633:         len = rows->rlen[row_indx]++;

635:         rows->rptrs[row_indx][len] = mapping[col];
636:       }
637:       PetscCall(MatRestoreRow(AT, i, &nz, &cols, &vals));
638:     }
639:   }

641:   PetscCall(PetscFree(mapping));

643:   order_pointers(M);
644:   M->maxnz = calc_maxnz(M);
645:   *B       = M;
646:   PetscFunctionReturn(PETSC_SUCCESS);
647: }

649: /*
650:    Converts from an SPAI matrix B  to a PETSc matrix PB.
651:    This assumes that the SPAI matrix B is stored in
652:    COMPRESSED-ROW format.
653: */
654: PetscErrorCode ConvertMatrixToMat(MPI_Comm comm, matrix *B, Mat *PB)
655: {
656:   PetscMPIInt size, rank;
657:   int         m, n, M, N;
658:   int         d_nz, o_nz;
659:   int        *d_nnz, *o_nnz;
660:   int         i, k, global_row, global_col, first_diag_col, last_diag_col;
661:   PetscScalar val;

663:   PetscFunctionBegin;
664:   PetscCallMPI(MPI_Comm_size(comm, &size));
665:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

667:   m = n = B->mnls[rank];
668:   d_nz = o_nz = 0;

670:   /* Determine preallocation for MatCreateAIJ */
671:   PetscCall(PetscMalloc1(m, &d_nnz));
672:   PetscCall(PetscMalloc1(m, &o_nnz));
673:   for (i = 0; i < m; i++) d_nnz[i] = o_nnz[i] = 0;
674:   first_diag_col = B->start_indices[rank];
675:   last_diag_col  = first_diag_col + B->mnls[rank];
676:   for (i = 0; i < B->mnls[rank]; i++) {
677:     for (k = 0; k < B->lines->len[i]; k++) {
678:       global_col = B->lines->ptrs[i][k];
679:       if ((global_col >= first_diag_col) && (global_col < last_diag_col)) d_nnz[i]++;
680:       else o_nnz[i]++;
681:     }
682:   }

684:   M = N = B->n;
685:   /* Here we only know how to create AIJ format */
686:   PetscCall(MatCreate(comm, PB));
687:   PetscCall(MatSetSizes(*PB, m, n, M, N));
688:   PetscCall(MatSetType(*PB, MATAIJ));
689:   PetscCall(MatSeqAIJSetPreallocation(*PB, d_nz, d_nnz));
690:   PetscCall(MatMPIAIJSetPreallocation(*PB, d_nz, d_nnz, o_nz, o_nnz));

692:   for (i = 0; i < B->mnls[rank]; i++) {
693:     global_row = B->start_indices[rank] + i;
694:     for (k = 0; k < B->lines->len[i]; k++) {
695:       global_col = B->lines->ptrs[i][k];

697:       val = B->lines->A[i][k];
698:       PetscCall(MatSetValues(*PB, 1, &global_row, 1, &global_col, &val, ADD_VALUES));
699:     }
700:   }

702:   PetscCall(PetscFree(d_nnz));
703:   PetscCall(PetscFree(o_nnz));

705:   PetscCall(MatAssemblyBegin(*PB, MAT_FINAL_ASSEMBLY));
706:   PetscCall(MatAssemblyEnd(*PB, MAT_FINAL_ASSEMBLY));
707:   PetscFunctionReturn(PETSC_SUCCESS);
708: }

710: /*
711:    Converts from an SPAI vector v  to a PETSc vec Pv.
712: */
713: PetscErrorCode ConvertVectorToVec(MPI_Comm comm, vector *v, Vec *Pv)
714: {
715:   PetscMPIInt size, rank;
716:   int         m, M, i, *mnls, *start_indices, *global_indices;

718:   PetscFunctionBegin;
719:   PetscCallMPI(MPI_Comm_size(comm, &size));
720:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

722:   m = v->mnl;
723:   M = v->n;

725:   PetscCall(VecCreateMPI(comm, m, M, Pv));

727:   PetscCall(PetscMalloc1(size, &mnls));
728:   PetscCallMPI(MPI_Allgather(&v->mnl, 1, MPI_INT, mnls, 1, MPI_INT, comm));

730:   PetscCall(PetscMalloc1(size, &start_indices));

732:   start_indices[0] = 0;
733:   for (i = 1; i < size; i++) start_indices[i] = start_indices[i - 1] + mnls[i - 1];

735:   PetscCall(PetscMalloc1(v->mnl, &global_indices));
736:   for (i = 0; i < v->mnl; i++) global_indices[i] = start_indices[rank] + i;

738:   PetscCall(PetscFree(mnls));
739:   PetscCall(PetscFree(start_indices));

741:   PetscCall(VecSetValues(*Pv, v->mnl, global_indices, v->v, INSERT_VALUES));
742:   PetscCall(VecAssemblyBegin(*Pv));
743:   PetscCall(VecAssemblyEnd(*Pv));

745:   PetscCall(PetscFree(global_indices));
746:   PetscFunctionReturn(PETSC_SUCCESS);
747: }