Actual source code: pipelcg.c

petsc-3.11.3 2019-06-26
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  1:  #include <petsc/private/kspimpl.h>
  2:  #include <petsc/private/vecimpl.h>

  4: #define offset(j)      PetscMax(((j) - (2*l)), 0)
  5: #define shift(i,j)     ((i) - offset((j)))
  6: #define G(i,j)         (plcg->G[((j)*(2*l+1))+(shift((i),(j))) ])
  7: #define G_noshift(i,j) (plcg->G[((j)*(2*l+1))+(i)])
  8: #define alpha(i)       (plcg->alpha[(i)])
  9: #define gamma(i)       (plcg->gamma[(i)])
 10: #define delta(i)       (plcg->delta[(i)])
 11: #define sigma(i)       (plcg->sigma[(i)])
 12: #define req(i)         (plcg->req[(i)])

 14: typedef struct KSP_CG_PIPE_L_s KSP_CG_PIPE_L;
 15: struct KSP_CG_PIPE_L_s {
 16:   PetscInt    l;      /* pipeline depth */
 17:   Vec         *Z;     /* Z vector (shifted base) */
 18:   Vec         *V;     /* V vector (original base) */
 19:   Vec         z_2;    /* additional vector needed when l == 1 */
 20:   Vec         p,u,up,upp; /* some work vectors */
 21:   PetscScalar *G;     /* such that Z = VG (band matrix)*/
 22:   PetscScalar *gamma,*delta,*alpha;
 23:   PetscReal   lmin,lmax; /* min and max eigen values estimates to compute base shifts */
 24:   PetscReal   *sigma; /* base shifts */
 25:   MPI_Request *req;   /* request array for asynchronous global collective */
 26: };

 28: /**
 29:  * KSPSetUp_PIPELCG - Sets up the workspace needed by the PIPELCG method.
 30:  *
 31:  * This is called once, usually automatically by KSPSolve() or KSPSetUp()
 32:  * but can be called directly by KSPSetUp()
 33:  */
 34: static PetscErrorCode KSPSetUp_PIPELCG(KSP ksp)
 35: {
 37:   KSP_CG_PIPE_L  *plcg = (KSP_CG_PIPE_L*)ksp->data;
 38:   PetscInt       l=plcg->l,max_it=ksp->max_it;
 39:   MPI_Comm       comm;

 42:   comm = PetscObjectComm((PetscObject)ksp);
 43:   if (max_it < 1) SETERRQ1(comm,PETSC_ERR_ARG_OUTOFRANGE,"%s: max_it argument must be positive.",((PetscObject)ksp)->type_name);
 44:   if (l < 1) SETERRQ1(comm,PETSC_ERR_ARG_OUTOFRANGE,"%s: pipel argument must be positive.",((PetscObject)ksp)->type_name);
 45:   if (l > max_it) SETERRQ1(comm,PETSC_ERR_ARG_OUTOFRANGE,"%s: pipel argument must be less than max_it.",((PetscObject)ksp)->type_name);

 47:   KSPSetWorkVecs(ksp,4); /* get work vectors needed by PIPELCG */
 48:   plcg->p   = ksp->work[0];
 49:   plcg->u   = ksp->work[1];
 50:   plcg->up  = ksp->work[2];
 51:   plcg->upp = ksp->work[3];

 53:   VecDuplicateVecs(plcg->p,l+1,&plcg->Z);
 54:   VecDuplicateVecs(plcg->p,2*l+1,&plcg->V);
 55:   PetscCalloc1(2*l,&plcg->alpha);
 56:   PetscCalloc1(l,&plcg->sigma);
 57:   if (l == 1) {
 58:     VecDuplicate(plcg->p,&plcg->z_2);
 59:   }

 61:   return(0);
 62: }

 64: static PetscErrorCode KSPReset_PIPELCG(KSP ksp)
 65: {
 66:   KSP_CG_PIPE_L *plcg = (KSP_CG_PIPE_L*)ksp->data;
 67:   PetscInt      ierr=0,l=plcg->l;

 70:   PetscFree(plcg->sigma);
 71:   PetscFree(plcg->alpha);
 72:   VecDestroyVecs(l+1,&plcg->Z);
 73:   VecDestroyVecs(2*l+1,&plcg->V);
 74:   VecDestroy(&plcg->z_2);
 75:   return(0);
 76: }

 78: static PetscErrorCode KSPDestroy_PIPELCG(KSP ksp)
 79: {
 80:   PetscInt ierr=0;

 83:   KSPReset_PIPELCG(ksp);
 84:   KSPDestroyDefault(ksp);
 85:   return(0);
 86: }

 88: static PetscErrorCode KSPSetFromOptions_PIPELCG(PetscOptionItems *PetscOptionsObject,KSP ksp)
 89: {
 90:   PetscInt      ierr=0;
 91:   KSP_CG_PIPE_L *plcg = (KSP_CG_PIPE_L*)ksp->data;
 92:   PetscBool     flag=PETSC_FALSE;

 95:   PetscOptionsHead(PetscOptionsObject,"KSP PIPELCG options");
 96:   PetscOptionsInt("-ksp_pipelcg_pipel","Pipeline length","",plcg->l,&plcg->l,&flag);
 97:   if (!flag) plcg->l = 1;
 98:   PetscOptionsReal("-ksp_pipelcg_lmin","Estimate for smallest eigenvalue","",plcg->lmin,&plcg->lmin,&flag);
 99:   if (!flag) plcg->lmin = 0.0;
100:   PetscOptionsReal("-ksp_pipelcg_lmax","Estimate for largest eigenvalue","",plcg->lmax,&plcg->lmax,&flag);
101:   if (!flag) plcg->lmax = 0.0;
102:   PetscOptionsTail();
103:   return(0);
104: }

106: static PetscErrorCode MPIPetsc_Iallreduce(void *sendbuf,void *recvbuf,PetscMPIInt count,MPI_Datatype datatype,MPI_Op op,MPI_Comm comm,MPI_Request *request)
107: {
108:   PetscErrorCode ierr=0;

111: #if defined(PETSC_HAVE_MPI_IALLREDUCE)
112:   MPI_Iallreduce(sendbuf,recvbuf,count,datatype,op,comm,request);
113: #else
114:   MPIU_Allreduce(sendbuf,recvbuf,count,datatype,op,comm);
115:   *request = MPI_REQUEST_NULL;
116: #endif
117:   return(0);
118: }

120: static PetscErrorCode KSPView_PIPELCG(KSP ksp,PetscViewer viewer)
121: {
122:   KSP_CG_PIPE_L  *plcg = (KSP_CG_PIPE_L*)ksp->data;
123:   PetscErrorCode ierr=0;
124:   PetscBool      iascii=PETSC_FALSE,isstring=PETSC_FALSE;

127:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
128:   PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERSTRING,&isstring);
129:   if (iascii) {
130:     PetscViewerASCIIPrintf(viewer,"  Pipeline depth: %D\n", plcg->l);
131:     PetscViewerASCIIPrintf(viewer,"  Minimal eigen value estimate %g\n",plcg->lmin);
132:     PetscViewerASCIIPrintf(viewer,"  Maximal eigen value estimate %g\n",plcg->lmax);
133:   } else if (isstring) {
134:     PetscViewerStringSPrintf(viewer,"  Pipeline depth: %D\n", plcg->l);
135:     PetscViewerStringSPrintf(viewer,"  Minimal eigen value estimate %g\n",plcg->lmin);
136:     PetscViewerStringSPrintf(viewer,"  Maximal eigen value estimate %g\n",plcg->lmax);
137:   }
138:   return(0);
139: }

141: static PetscErrorCode KSPSolve_InnerLoop_PIPELCG(KSP ksp)
142: {
143:   KSP_CG_PIPE_L *plcg = (KSP_CG_PIPE_L*)ksp->data;
144:   Mat           A=NULL,Pmat=NULL;
145:   PetscInt      it=0,max_it=ksp->max_it,ierr=0,l=plcg->l,i=0,j=0,k=0;
146:   PetscInt      start=0,middle=0,end=0;
147:   Vec           *Z = plcg->Z,*V=plcg->V;
148:   Vec           x=NULL,p=NULL,u=NULL,up=NULL,upp=NULL,temp=NULL,temp2[2],z_2=NULL;
149:   PetscScalar   sum_dummy=0.0,eta=0.0,zeta=0.0,lambda=0.0;
150:   PetscReal     dp=0.0,tmp=0.0,beta=0.0,invbeta2=0.0;
151:   MPI_Comm      comm;

154:   x   = ksp->vec_sol;
155:   p   = plcg->p;
156:   u   = plcg->u;
157:   up  = plcg->up;
158:   upp = plcg->upp;
159:   z_2 = plcg->z_2;

161:   comm = PetscObjectComm((PetscObject)ksp);
162:   PCGetOperators(ksp->pc,&A,&Pmat);

164:   for (it = 0; it < max_it+l; ++it) {
165:     /* ----------------------------------- */
166:     /* Multiplication  z_{it+1} =  Az_{it} */
167:     /* ----------------------------------- */
168:     VecCopy(up,upp);
169:     VecCopy(u,up);
170:     if (it < l) {
171:       /* SpMV and Prec */
172:       MatMult(A,Z[l-it],u);
173:       VecAXPY(u,-sigma(it),up);
174:       KSP_PCApply(ksp,u,Z[l-it-1]);
175:       /* Apply shift */
176:     } else {
177:       /* Shift the Z vector pointers */
178:       if (l == 1) {
179:         VecCopy(Z[l],z_2);
180:       }
181:       temp = Z[l];
182:       for (i = l; i > 0; --i) {
183:         Z[i] = Z[i-1];
184:       }
185:       Z[0] = temp;
186:       MatMult(A,Z[1],u);
187:       KSP_PCApply(ksp,u,Z[0]);
188:     }

190:     /* ----------------------------------- */
191:     /* Adjust the G matrix */
192:     /* ----------------------------------- */
193:     if (it >= l) {
194:       if (it == l) {
195:         /* MPI_Wait for G(0,0),scale V0 and Z and u vectors with 1/beta */
196:         MPI_Wait(&req(0),MPI_STATUS_IGNORE);
197:         beta = PetscSqrtReal(PetscRealPart(G(0,0)));
198:         G(0,0) = 1.0;
199:         VecAXPY(V[2*l],1.0/beta,p);
200:         for (j = 0; j <= l; ++j) {
201:           VecScale(Z[j],1.0/beta);
202:         }
203:         VecScale(u,1.0/beta);
204:         VecScale(up,1.0/beta);
205:         VecScale(upp,1.0/beta);
206:       }

208:       /* MPI_Wait until the dot products,started l iterations ago,are completed */
209:       MPI_Wait(&req(it-l+1),MPI_STATUS_IGNORE);
210:       if (it <= 2*l-1) {
211:         invbeta2 = 1.0 / (beta * beta);
212:         /* scale column 1 up to column l of G with 1/beta^2 */
213:         for (j = PetscMax(it-3*l+1,0); j <= it-l+1; ++j) {
214:           G(j,it-l+1) *= invbeta2;
215:         }
216:       }

218:       for (j = PetscMax(it-2*l+2,0); j <= it-l; ++j) {
219:         sum_dummy = 0.0;
220:         for (k = PetscMax(it-3*l+1,0); k <= j-1; ++k) {
221:           sum_dummy = sum_dummy + G(k,j) * G(k,it-l+1);
222:         }
223:         G(j,it-l+1) = (G(j,it-l+1) - sum_dummy) / G(j,j);
224:       }

226:       sum_dummy = 0.0;
227:       for (k = PetscMax(it-3*l+1,0); k <= it-l; ++k) {
228:         sum_dummy = sum_dummy + G(k,it-l+1) * G(k,it-l+1);
229:       }

231:       /* Breakdown check */
232:       tmp = PetscRealPart(G(it-l+1,it-l+1) - sum_dummy);
233:       if (tmp < 0) {
234:         PetscPrintf(comm,"sqrt breakdown in iteration %d: value is %e. Iteration was restarted.\n",ksp->its+1,tmp);
235:         /* End hanging dot-products in the pipeline before exiting for-loop */
236:         start = it-l+2;
237:         end = PetscMin(it+1,max_it+1);  /* !warning! 'it' can actually be greater than 'max_it' */
238:         for (i = start; i < end; ++i) {
239:           MPI_Wait(&req(i),MPI_STATUS_IGNORE);
240:         }
241:         break;
242:       }
243:       G(it-l+1,it-l+1) = PetscSqrtReal(tmp);

245:       if (it < 2*l) {
246:         if (it == l) {
247:           gamma(it-l) = (G(it-l,it-l+1) + sigma(it-l) * G(it-l,it-l)) / G(it-l,it-l);
248:         } else {
249:           gamma(it-l) = (G(it-l,it-l+1) + sigma(it-l) * G(it-l,it-l)
250:                          - delta(it-l-1) * G(it-l-1,it-l)) / G(it-l,it-l);
251:         }
252:         delta(it-l) = G(it-l+1,it-l+1) / G(it-l,it-l);
253:       } else {
254:         gamma(it-l) = (G(it-l,it-l) * gamma(it-2*l)
255:                        + G(it-l,it-l+1) * delta(it-2*l)
256:                        - G(it-l-1,it-l) * delta(it-l-1)) / G(it-l,it-l);
257:         delta(it-l) = (G(it-l+1,it-l+1) * delta(it-2*l)) / G(it-l,it-l);
258:       }

260:       /* -------------------------------------------- */
261:       /* Recursively compute the next V and Z vectors */
262:       /* -------------------------------------------- */
263:       /* Recurrence V vectors */
264:       if (it < 3*l) {
265:         VecAXPY(V[3*l-it-1],1.0/G(it-l+1,it-l+1),Z[l]);
266:         for (j = 0; j <= it-l; ++j) {
267:           alpha(it-l-j) = -G(j,it-l+1)/G(it-l+1,it-l+1);
268:         }
269:         VecMAXPY(V[3*l-it-1],it-l+1,&alpha(0),&V[3*l-it]);
270:       } else {
271:         /* Shift the V vector pointers */
272:         temp = V[2*l];
273:         for (i = 2*l; i>0; i--) {
274:           V[i] = V[i-1];
275:         }
276:         V[0] = temp;

278:         VecSet(V[0],0.0);
279:         VecAXPY(V[0],1.0/G(it-l+1,it-l+1),Z[l]);
280:         for (j = 0; j < 2*l; ++j) {
281:           k = (it-3*l+1)+j;
282:           alpha(2*l-1-j) = -G(k,it-l+1)/G(it-l+1,it-l+1);
283:         }
284:         VecMAXPY(V[0],2*l,&alpha(0),&V[1]);
285:       }
286:       /* Recurrence Z and U vectors */
287:       if (it <= l) {
288:         VecAXPY(Z[0],-gamma(it-l),Z[1]);
289:         VecAXPY(u,-gamma(it-l),up);
290:       } else {
291:         alpha(0) = -delta(it-l-1);
292:         alpha(1) = -gamma(it-l);

294:         temp2[0] = (l==1) ? z_2 : Z[2];
295:         temp2[1] = Z[1];
296:         VecMAXPY(Z[0],2,&alpha(0),temp2);

298:         temp2[0] = upp;
299:         temp2[1] = up;
300:         VecMAXPY(u,2,&alpha(0),temp2);
301:       }
302:       VecScale(Z[0],1.0/delta(it-l));
303:       VecScale(u,1.0/delta(it-l));
304:     }

306:     /* ---------------------------------------- */
307:     /* Compute and communicate the dot products */
308:     /* ---------------------------------------- */
309:     if (it < l) {
310:       /* dot-product (Z_{it+1},z_j) */
311:       for (j = 0; j < it+2; ++j) {
312:         (*u->ops->dot_local)(u,Z[l-j],&G(j,it+1));
313:       }
314:       MPIPetsc_Iallreduce(MPI_IN_PLACE,&G(0,it+1),it+2,MPIU_SCALAR,MPIU_SUM,comm,&req(it+1));
315:     } else if ((it >= l) && (it < max_it)) {
316:       start = PetscMax(0,it-2*l+1);
317:       middle = it-l+2;
318:       end = it+2;
319:       for (j = start; j < middle; ++j) { /* dot-product (Z_{it+1},v_j) */
320:         temp = (it < 3*l) ? plcg->V[2*l-j] : plcg->V[it-l+1-j];
321:         (*u->ops->dot_local)(u,temp,&G(j,it+1));
322:       }
323:       for (j = middle; j < end; ++j) { /* dot-product (Z_{it+1},z_j) */
324:         (*u->ops->dot_local)(u,plcg->Z[it+1-j],&G(j,it+1));
325:       }
326:       MPIPetsc_Iallreduce(MPI_IN_PLACE,&G(start,it+1),end-start,MPIU_SCALAR,MPIU_SUM,comm,&req(it+1));
327:     }

329:     /* ----------------------------------------- */
330:     /* Compute solution vector and residual norm */
331:     /* ----------------------------------------- */
332:     if (it >= l) {
333:       if (it == l) {
334:         if (ksp->its != 0) {
335:           ++ ksp->its;
336:         }
337:         eta  = gamma(0);
338:         zeta = beta;
339:         VecCopy(V[2*l],p);
340:         VecScale(p,1.0/eta);
341:         VecAXPY(x,zeta,p);

343:         dp         = beta;
344:         ksp->rnorm = dp;
345:         KSPLogResidualHistory(ksp,dp);
346:         KSPMonitor(ksp,ksp->its,dp);
347:         (*ksp->converged)(ksp,ksp->its,dp,&ksp->reason,ksp->cnvP);
348:       } else if (it > l) {
349:         k = it-l;
350:         ++ ksp->its;
351:         lambda  = delta(k-1) / eta;
352:         eta     = gamma(k) - lambda * delta(k-1);
353:         zeta    = -lambda * zeta;
354:         VecScale(p,-delta(k-1)/eta);
355:         VecAXPY(p,1.0/eta,(it < 3*l) ? V[3*l-it] : V[1]);
356:         VecAXPY(x,zeta,p);

358:         dp         = PetscAbsScalar(zeta);
359:         ksp->rnorm = dp;
360:         KSPLogResidualHistory(ksp,dp);
361:         KSPMonitor(ksp,ksp->its,dp);
362:         (*ksp->converged)(ksp,ksp->its,dp,&ksp->reason,ksp->cnvP);
363:       }
364:       if (ksp->its >= max_it-1) {
365:         ksp->reason = KSP_DIVERGED_ITS;
366:       }
367:       if (ksp->reason) {
368:         /* End hanging dot-products in the pipeline before exiting for-loop */
369:         start = it-l+2;
370:         end = PetscMin(it+2,max_it+1); /* !warning! 'it' can actually be greater than 'max_it' */
371:         for (i = start; i < end; ++i) {
372:           MPI_Wait(&req(i),MPI_STATUS_IGNORE);
373:         }
374:         break;
375:       }
376:     }
377:   } /* End inner for loop */
378:   return(0);
379: }

381: static PetscErrorCode KSPSolve_ReInitData_PIPELCG(KSP ksp)
382: {
383:   KSP_CG_PIPE_L *plcg = (KSP_CG_PIPE_L*)ksp->data;
384:   PetscInt      ierr=0,i=0,j=0,l=plcg->l,max_it=ksp->max_it;

387:   VecSet(plcg->up,0.0);
388:   VecSet(plcg->upp,0.0);
389:   for (i = 0; i < l+1; ++i) {
390:     VecSet(plcg->Z[i],0.0);
391:   }
392:   for (i = 0; i < (2*l+1); ++i) {
393:     VecSet(plcg->V[i],0.0);
394:   }
395:   for (j = 0; j < (max_it+1); ++j) {
396:     gamma(j) = 0.0;
397:     delta(j) = 0.0;
398:     for (i = 0; i < (2*l+1); ++i) {
399:       G_noshift(i,j) = 0.0;
400:     }
401:   }
402:   return(0);
403: }

405: /**
406:  * KSPSolve_PIPELCG - This routine actually applies the pipelined(l) conjugate gradient method
407:  *
408:  * Input Parameter:
409:  *     ksp - the Krylov space object that was set to use conjugate gradient,by,for
410:  *     example,KSPCreate(MPI_Comm,KSP *ksp); KSPSetType(ksp,KSPCG);
411:  */
412: static PetscErrorCode KSPSolve_PIPELCG(KSP ksp)
413: {
414:   PetscErrorCode ierr=0;
415:   KSP_CG_PIPE_L  *plcg = (KSP_CG_PIPE_L*)ksp->data;
416:   Mat            A=NULL,Pmat=NULL;
417:   Vec            b=NULL,x=NULL,p=NULL,u=NULL;
418:   PetscInt       max_it=ksp->max_it,l=plcg->l;
419:   PetscInt       i=0,outer_it=0,curr_guess_zero=0;
420:   PetscReal      lmin=plcg->lmin,lmax=plcg->lmax;
421:   PetscBool      diagonalscale=PETSC_FALSE;
422:   MPI_Comm       comm;

425:   comm = PetscObjectComm((PetscObject)ksp);
426:   PCGetDiagonalScale(ksp->pc,&diagonalscale);
427:   if (diagonalscale) {
428:     SETERRQ1(comm,PETSC_ERR_SUP,"Krylov method %s does not support diagonal scaling",((PetscObject)ksp)->type_name);
429:   }

431:   x = ksp->vec_sol;
432:   b = ksp->vec_rhs;
433:   p = plcg->p;
434:   u = plcg->u;

436:   PetscCalloc1((max_it+1)*(2*l+1),&plcg->G);
437:   PetscCalloc1(max_it+1,&plcg->gamma);
438:   PetscCalloc1(max_it+1,&plcg->delta);
439:   PetscCalloc1(max_it+1,&plcg->req);

441:   PCGetOperators(ksp->pc,&A,&Pmat);

443:   for (i = 0; i < l; ++i) {
444:     sigma(i) = (0.5*(lmin+lmax) + (0.5*(lmax-lmin) * PetscCosReal(PETSC_PI*(2.0*i+1.0)/(2.0*l))));
445:   }

447:   ksp->its = 0;
448:   outer_it = 0;
449:   curr_guess_zero = !! ksp->guess_zero;

451:   while (ksp->its < max_it) { /* OUTER LOOP (gmres-like restart to handle breakdowns) */
452:     /* * */
453:     /* RESTART LOOP */
454:     /* * */
455:     if (!curr_guess_zero) {
456:       KSP_MatMult(ksp,A,x,u);  /* u <- b - Ax */
457:       VecAYPX(u,-1.0,b);
458:     } else {
459:       VecCopy(b,u);            /* u <- b (x is 0) */
460:     }
461:     KSP_PCApply(ksp,u,p);      /* p <- Bu */

463:     if (outer_it > 0) {
464:       /* Re-initialize Z,V,gamma,delta,G,u,up,upp after restart occurred */
465:       KSPSolve_ReInitData_PIPELCG(ksp);
466:     }

468:     (*u->ops->dot_local)(u,p,&G(0,0));
469:     MPIPetsc_Iallreduce(MPI_IN_PLACE,&G(0,0),1,MPIU_SCALAR,MPIU_SUM,comm,&req(0));
470:     VecCopy(p,plcg->Z[l]);

472:     KSPSolve_InnerLoop_PIPELCG(ksp);

474:     if (ksp->reason) break; /* convergence or divergence */
475:     ++ outer_it;
476:     curr_guess_zero = 0;
477:   }

479:   if (ksp->its >= max_it-1) {
480:     ksp->reason = KSP_DIVERGED_ITS;
481:   }
482:   PetscFree(plcg->G);
483:   PetscFree(plcg->gamma);
484:   PetscFree(plcg->delta);
485:   PetscFree(plcg->req);
486:   return(0);
487: }

489: /*MC
490:     KSPPIPELCG - Deep pipelined (length l) Conjugate Gradient method. This method has only a single non-blocking global
491:     reduction per iteration, compared to 2 blocking reductions for standard CG. The reduction is overlapped by the
492:     matrix-vector product and preconditioner application of the next l iterations. The pipeline length l is a parameter
493:     of the method.

495:     Options Database Keys:
496: .   see KSPSolve()

498:     Level: intermediate

500:     Notes:
501:     MPI configuration may be necessary for reductions to make asynchronous progress, which is important for
502:     performance of pipelined methods. See the FAQ on the PETSc website for details.

504:     Contributed by:
505:     Siegfried Cools, University of Antwerp, Dept. Mathematics and Computer Science,
506:     funded by Flemish Research Foundation (FWO) grant number 12H4617N.

508:     Reference:
509:     J. Cornelis, S. Cools and W. Vanroose,
510:     "The Communication-Hiding Conjugate Gradient Method with Deep Pipelines", Submitted to SIAM Journal on Scientific
511:     Computing (SISC), 2018.

513: .seealso:  KSPCreate(), KSPSetType(), KSPType (for list of available types), KSPCG, KSPPIPECG, KSPPIPECGRR, KSPPGMRES,
514:     KSPPIPEBCGS, KSPSetPCSide()
515: M*/
516: PETSC_EXTERN
517: PetscErrorCode KSPCreate_PIPELCG(KSP ksp)
518: {
520:   KSP_CG_PIPE_L  *plcg = NULL;

523:   PetscNewLog(ksp,&plcg);
524:   ksp->data = (void*)plcg;

526:   KSPSetSupportedNorm(ksp,KSP_NORM_UNPRECONDITIONED,PC_LEFT,2);
527:   KSPSetSupportedNorm(ksp,KSP_NORM_NATURAL,PC_LEFT,2);

529:   ksp->ops->setup          = KSPSetUp_PIPELCG;
530:   ksp->ops->solve          = KSPSolve_PIPELCG;
531:   ksp->ops->reset          = KSPReset_PIPELCG;
532:   ksp->ops->destroy        = KSPDestroy_PIPELCG;
533:   ksp->ops->view           = KSPView_PIPELCG;
534:   ksp->ops->setfromoptions = KSPSetFromOptions_PIPELCG;
535:   ksp->ops->buildsolution  = KSPBuildSolutionDefault;
536:   ksp->ops->buildresidual  = KSPBuildResidualDefault;
537:   return(0);
538: }