Actual source code: chwirut2.c

  1: /*
  2:    Include "petsctao.h" so that we can use TAO solvers.  Note that this
  3:    file automatically includes libraries such as:
  4:      petsc.h       - base PETSc routines   petscvec.h - vectors
  5:      petscsys.h    - system routines        petscmat.h - matrices
  6:      petscis.h     - index sets            petscksp.h - Krylov subspace methods
  7:      petscviewer.h - viewers               petscpc.h  - preconditioners

  9: */

 11: #include <petsctao.h>

 13: /*
 14: Description:   These data are the result of a NIST study involving
 15:                ultrasonic calibration.  The response variable is
 16:                ultrasonic response, and the predictor variable is
 17:                metal distance.

 19: Reference:     Chwirut, D., NIST (197?).
 20:                Ultrasonic Reference Block Study.
 21: */

 23: static char help[] = "Finds the nonlinear least-squares solution to the model \n\
 24:             y = exp[-b1*x]/(b2+b3*x)  +  e \n";

 26: #define NOBSERVATIONS 214
 27: #define NPARAMETERS   3

 29: #define DIE_TAG  2000
 30: #define IDLE_TAG 1000

 32: /* User-defined application context */
 33: typedef struct {
 34:   /* Working space */
 35:   PetscReal   t[NOBSERVATIONS]; /* array of independent variables of observation */
 36:   PetscReal   y[NOBSERVATIONS]; /* array of dependent variables */
 37:   PetscMPIInt size, rank;
 38: } AppCtx;

 40: /* User provided Routines */
 41: PetscErrorCode InitializeData(AppCtx *user);
 42: PetscErrorCode FormStartingPoint(Vec);
 43: PetscErrorCode EvaluateFunction(Tao, Vec, Vec, void *);
 44: PetscErrorCode TaskWorker(AppCtx *user);
 45: PetscErrorCode StopWorkers(AppCtx *user);
 46: PetscErrorCode RunSimulation(PetscReal *x, PetscInt i, PetscReal *f, AppCtx *user);

 48: /*--------------------------------------------------------------------*/
 49: int main(int argc, char **argv)
 50: {
 51:   Vec    x, f; /* solution, function */
 52:   Tao    tao;  /* Tao solver context */
 53:   AppCtx user; /* user-defined work context */

 55:   /* Initialize TAO and PETSc */
 57:   PetscInitialize(&argc, &argv, (char *)0, help);
 58:   MPI_Comm_size(MPI_COMM_WORLD, &user.size);
 59:   MPI_Comm_rank(MPI_COMM_WORLD, &user.rank);
 60:   InitializeData(&user);

 62:   /* Run optimization on rank 0 */
 63:   if (user.rank == 0) {
 64:     /* Allocate vectors */
 65:     VecCreateSeq(PETSC_COMM_SELF, NPARAMETERS, &x);
 66:     VecCreateSeq(PETSC_COMM_SELF, NOBSERVATIONS, &f);

 68:     /* TAO code begins here */

 70:     /* Create TAO solver and set desired solution method */
 71:     TaoCreate(PETSC_COMM_SELF, &tao);
 72:     TaoSetType(tao, TAOPOUNDERS);

 74:     /* Set the function and Jacobian routines. */
 75:     FormStartingPoint(x);
 76:     TaoSetSolution(tao, x);
 77:     TaoSetResidualRoutine(tao, f, EvaluateFunction, (void *)&user);

 79:     /* Check for any TAO command line arguments */
 80:     TaoSetFromOptions(tao);

 82:     /* Perform the Solve */
 83:     TaoSolve(tao);

 85:     /* Free TAO data structures */
 86:     TaoDestroy(&tao);

 88:     /* Free PETSc data structures */
 89:     VecDestroy(&x);
 90:     VecDestroy(&f);
 91:     StopWorkers(&user);
 92:   } else {
 93:     TaskWorker(&user);
 94:   }
 95:   PetscFinalize();
 96:   return 0;
 97: }

 99: /*--------------------------------------------------------------------*/
100: PetscErrorCode EvaluateFunction(Tao tao, Vec X, Vec F, void *ptr)
101: {
102:   AppCtx    *user = (AppCtx *)ptr;
103:   PetscInt   i;
104:   PetscReal *x, *f;

106:   VecGetArray(X, &x);
107:   VecGetArray(F, &f);
108:   if (user->size == 1) {
109:     /* Single processor */
110:     for (i = 0; i < NOBSERVATIONS; i++) RunSimulation(x, i, &f[i], user);
111:   } else {
112:     /* Multiprocessor main */
113:     PetscMPIInt tag;
114:     PetscInt    finishedtasks, next_task, checkedin;
115:     PetscReal   f_i = 0.0;
116:     MPI_Status  status;

118:     next_task     = 0;
119:     finishedtasks = 0;
120:     checkedin     = 0;

122:     while (finishedtasks < NOBSERVATIONS || checkedin < user->size - 1) {
123:       MPI_Recv(&f_i, 1, MPIU_REAL, MPI_ANY_SOURCE, MPI_ANY_TAG, PETSC_COMM_WORLD, &status);
124:       if (status.MPI_TAG == IDLE_TAG) {
125:         checkedin++;
126:       } else {
127:         tag    = status.MPI_TAG;
128:         f[tag] = (PetscReal)f_i;
129:         finishedtasks++;
130:       }

132:       if (next_task < NOBSERVATIONS) {
133:         MPI_Send(x, NPARAMETERS, MPIU_REAL, status.MPI_SOURCE, next_task, PETSC_COMM_WORLD);
134:         next_task++;

136:       } else {
137:         /* Send idle message */
138:         MPI_Send(x, NPARAMETERS, MPIU_REAL, status.MPI_SOURCE, IDLE_TAG, PETSC_COMM_WORLD);
139:       }
140:     }
141:   }
142:   VecRestoreArray(X, &x);
143:   VecRestoreArray(F, &f);
144:   PetscLogFlops(6 * NOBSERVATIONS);
145:   return 0;
146: }

148: /* ------------------------------------------------------------ */
149: PetscErrorCode FormStartingPoint(Vec X)
150: {
151:   PetscReal *x;

153:   VecGetArray(X, &x);
154:   x[0] = 0.15;
155:   x[1] = 0.008;
156:   x[2] = 0.010;
157:   VecRestoreArray(X, &x);
158:   return 0;
159: }

161: /* ---------------------------------------------------------------------- */
162: PetscErrorCode InitializeData(AppCtx *user)
163: {
164:   PetscReal *t = user->t, *y = user->y;
165:   PetscInt   i = 0;

167:   y[i]   = 92.9000;
168:   t[i++] = 0.5000;
169:   y[i]   = 78.7000;
170:   t[i++] = 0.6250;
171:   y[i]   = 64.2000;
172:   t[i++] = 0.7500;
173:   y[i]   = 64.9000;
174:   t[i++] = 0.8750;
175:   y[i]   = 57.1000;
176:   t[i++] = 1.0000;
177:   y[i]   = 43.3000;
178:   t[i++] = 1.2500;
179:   y[i]   = 31.1000;
180:   t[i++] = 1.7500;
181:   y[i]   = 23.6000;
182:   t[i++] = 2.2500;
183:   y[i]   = 31.0500;
184:   t[i++] = 1.7500;
185:   y[i]   = 23.7750;
186:   t[i++] = 2.2500;
187:   y[i]   = 17.7375;
188:   t[i++] = 2.7500;
189:   y[i]   = 13.8000;
190:   t[i++] = 3.2500;
191:   y[i]   = 11.5875;
192:   t[i++] = 3.7500;
193:   y[i]   = 9.4125;
194:   t[i++] = 4.2500;
195:   y[i]   = 7.7250;
196:   t[i++] = 4.7500;
197:   y[i]   = 7.3500;
198:   t[i++] = 5.2500;
199:   y[i]   = 8.0250;
200:   t[i++] = 5.7500;
201:   y[i]   = 90.6000;
202:   t[i++] = 0.5000;
203:   y[i]   = 76.9000;
204:   t[i++] = 0.6250;
205:   y[i]   = 71.6000;
206:   t[i++] = 0.7500;
207:   y[i]   = 63.6000;
208:   t[i++] = 0.8750;
209:   y[i]   = 54.0000;
210:   t[i++] = 1.0000;
211:   y[i]   = 39.2000;
212:   t[i++] = 1.2500;
213:   y[i]   = 29.3000;
214:   t[i++] = 1.7500;
215:   y[i]   = 21.4000;
216:   t[i++] = 2.2500;
217:   y[i]   = 29.1750;
218:   t[i++] = 1.7500;
219:   y[i]   = 22.1250;
220:   t[i++] = 2.2500;
221:   y[i]   = 17.5125;
222:   t[i++] = 2.7500;
223:   y[i]   = 14.2500;
224:   t[i++] = 3.2500;
225:   y[i]   = 9.4500;
226:   t[i++] = 3.7500;
227:   y[i]   = 9.1500;
228:   t[i++] = 4.2500;
229:   y[i]   = 7.9125;
230:   t[i++] = 4.7500;
231:   y[i]   = 8.4750;
232:   t[i++] = 5.2500;
233:   y[i]   = 6.1125;
234:   t[i++] = 5.7500;
235:   y[i]   = 80.0000;
236:   t[i++] = 0.5000;
237:   y[i]   = 79.0000;
238:   t[i++] = 0.6250;
239:   y[i]   = 63.8000;
240:   t[i++] = 0.7500;
241:   y[i]   = 57.2000;
242:   t[i++] = 0.8750;
243:   y[i]   = 53.2000;
244:   t[i++] = 1.0000;
245:   y[i]   = 42.5000;
246:   t[i++] = 1.2500;
247:   y[i]   = 26.8000;
248:   t[i++] = 1.7500;
249:   y[i]   = 20.4000;
250:   t[i++] = 2.2500;
251:   y[i]   = 26.8500;
252:   t[i++] = 1.7500;
253:   y[i]   = 21.0000;
254:   t[i++] = 2.2500;
255:   y[i]   = 16.4625;
256:   t[i++] = 2.7500;
257:   y[i]   = 12.5250;
258:   t[i++] = 3.2500;
259:   y[i]   = 10.5375;
260:   t[i++] = 3.7500;
261:   y[i]   = 8.5875;
262:   t[i++] = 4.2500;
263:   y[i]   = 7.1250;
264:   t[i++] = 4.7500;
265:   y[i]   = 6.1125;
266:   t[i++] = 5.2500;
267:   y[i]   = 5.9625;
268:   t[i++] = 5.7500;
269:   y[i]   = 74.1000;
270:   t[i++] = 0.5000;
271:   y[i]   = 67.3000;
272:   t[i++] = 0.6250;
273:   y[i]   = 60.8000;
274:   t[i++] = 0.7500;
275:   y[i]   = 55.5000;
276:   t[i++] = 0.8750;
277:   y[i]   = 50.3000;
278:   t[i++] = 1.0000;
279:   y[i]   = 41.0000;
280:   t[i++] = 1.2500;
281:   y[i]   = 29.4000;
282:   t[i++] = 1.7500;
283:   y[i]   = 20.4000;
284:   t[i++] = 2.2500;
285:   y[i]   = 29.3625;
286:   t[i++] = 1.7500;
287:   y[i]   = 21.1500;
288:   t[i++] = 2.2500;
289:   y[i]   = 16.7625;
290:   t[i++] = 2.7500;
291:   y[i]   = 13.2000;
292:   t[i++] = 3.2500;
293:   y[i]   = 10.8750;
294:   t[i++] = 3.7500;
295:   y[i]   = 8.1750;
296:   t[i++] = 4.2500;
297:   y[i]   = 7.3500;
298:   t[i++] = 4.7500;
299:   y[i]   = 5.9625;
300:   t[i++] = 5.2500;
301:   y[i]   = 5.6250;
302:   t[i++] = 5.7500;
303:   y[i]   = 81.5000;
304:   t[i++] = .5000;
305:   y[i]   = 62.4000;
306:   t[i++] = .7500;
307:   y[i]   = 32.5000;
308:   t[i++] = 1.5000;
309:   y[i]   = 12.4100;
310:   t[i++] = 3.0000;
311:   y[i]   = 13.1200;
312:   t[i++] = 3.0000;
313:   y[i]   = 15.5600;
314:   t[i++] = 3.0000;
315:   y[i]   = 5.6300;
316:   t[i++] = 6.0000;
317:   y[i]   = 78.0000;
318:   t[i++] = .5000;
319:   y[i]   = 59.9000;
320:   t[i++] = .7500;
321:   y[i]   = 33.2000;
322:   t[i++] = 1.5000;
323:   y[i]   = 13.8400;
324:   t[i++] = 3.0000;
325:   y[i]   = 12.7500;
326:   t[i++] = 3.0000;
327:   y[i]   = 14.6200;
328:   t[i++] = 3.0000;
329:   y[i]   = 3.9400;
330:   t[i++] = 6.0000;
331:   y[i]   = 76.8000;
332:   t[i++] = .5000;
333:   y[i]   = 61.0000;
334:   t[i++] = .7500;
335:   y[i]   = 32.9000;
336:   t[i++] = 1.5000;
337:   y[i]   = 13.8700;
338:   t[i++] = 3.0000;
339:   y[i]   = 11.8100;
340:   t[i++] = 3.0000;
341:   y[i]   = 13.3100;
342:   t[i++] = 3.0000;
343:   y[i]   = 5.4400;
344:   t[i++] = 6.0000;
345:   y[i]   = 78.0000;
346:   t[i++] = .5000;
347:   y[i]   = 63.5000;
348:   t[i++] = .7500;
349:   y[i]   = 33.8000;
350:   t[i++] = 1.5000;
351:   y[i]   = 12.5600;
352:   t[i++] = 3.0000;
353:   y[i]   = 5.6300;
354:   t[i++] = 6.0000;
355:   y[i]   = 12.7500;
356:   t[i++] = 3.0000;
357:   y[i]   = 13.1200;
358:   t[i++] = 3.0000;
359:   y[i]   = 5.4400;
360:   t[i++] = 6.0000;
361:   y[i]   = 76.8000;
362:   t[i++] = .5000;
363:   y[i]   = 60.0000;
364:   t[i++] = .7500;
365:   y[i]   = 47.8000;
366:   t[i++] = 1.0000;
367:   y[i]   = 32.0000;
368:   t[i++] = 1.5000;
369:   y[i]   = 22.2000;
370:   t[i++] = 2.0000;
371:   y[i]   = 22.5700;
372:   t[i++] = 2.0000;
373:   y[i]   = 18.8200;
374:   t[i++] = 2.5000;
375:   y[i]   = 13.9500;
376:   t[i++] = 3.0000;
377:   y[i]   = 11.2500;
378:   t[i++] = 4.0000;
379:   y[i]   = 9.0000;
380:   t[i++] = 5.0000;
381:   y[i]   = 6.6700;
382:   t[i++] = 6.0000;
383:   y[i]   = 75.8000;
384:   t[i++] = .5000;
385:   y[i]   = 62.0000;
386:   t[i++] = .7500;
387:   y[i]   = 48.8000;
388:   t[i++] = 1.0000;
389:   y[i]   = 35.2000;
390:   t[i++] = 1.5000;
391:   y[i]   = 20.0000;
392:   t[i++] = 2.0000;
393:   y[i]   = 20.3200;
394:   t[i++] = 2.0000;
395:   y[i]   = 19.3100;
396:   t[i++] = 2.5000;
397:   y[i]   = 12.7500;
398:   t[i++] = 3.0000;
399:   y[i]   = 10.4200;
400:   t[i++] = 4.0000;
401:   y[i]   = 7.3100;
402:   t[i++] = 5.0000;
403:   y[i]   = 7.4200;
404:   t[i++] = 6.0000;
405:   y[i]   = 70.5000;
406:   t[i++] = .5000;
407:   y[i]   = 59.5000;
408:   t[i++] = .7500;
409:   y[i]   = 48.5000;
410:   t[i++] = 1.0000;
411:   y[i]   = 35.8000;
412:   t[i++] = 1.5000;
413:   y[i]   = 21.0000;
414:   t[i++] = 2.0000;
415:   y[i]   = 21.6700;
416:   t[i++] = 2.0000;
417:   y[i]   = 21.0000;
418:   t[i++] = 2.5000;
419:   y[i]   = 15.6400;
420:   t[i++] = 3.0000;
421:   y[i]   = 8.1700;
422:   t[i++] = 4.0000;
423:   y[i]   = 8.5500;
424:   t[i++] = 5.0000;
425:   y[i]   = 10.1200;
426:   t[i++] = 6.0000;
427:   y[i]   = 78.0000;
428:   t[i++] = .5000;
429:   y[i]   = 66.0000;
430:   t[i++] = .6250;
431:   y[i]   = 62.0000;
432:   t[i++] = .7500;
433:   y[i]   = 58.0000;
434:   t[i++] = .8750;
435:   y[i]   = 47.7000;
436:   t[i++] = 1.0000;
437:   y[i]   = 37.8000;
438:   t[i++] = 1.2500;
439:   y[i]   = 20.2000;
440:   t[i++] = 2.2500;
441:   y[i]   = 21.0700;
442:   t[i++] = 2.2500;
443:   y[i]   = 13.8700;
444:   t[i++] = 2.7500;
445:   y[i]   = 9.6700;
446:   t[i++] = 3.2500;
447:   y[i]   = 7.7600;
448:   t[i++] = 3.7500;
449:   y[i]   = 5.4400;
450:   t[i++] = 4.2500;
451:   y[i]   = 4.8700;
452:   t[i++] = 4.7500;
453:   y[i]   = 4.0100;
454:   t[i++] = 5.2500;
455:   y[i]   = 3.7500;
456:   t[i++] = 5.7500;
457:   y[i]   = 24.1900;
458:   t[i++] = 3.0000;
459:   y[i]   = 25.7600;
460:   t[i++] = 3.0000;
461:   y[i]   = 18.0700;
462:   t[i++] = 3.0000;
463:   y[i]   = 11.8100;
464:   t[i++] = 3.0000;
465:   y[i]   = 12.0700;
466:   t[i++] = 3.0000;
467:   y[i]   = 16.1200;
468:   t[i++] = 3.0000;
469:   y[i]   = 70.8000;
470:   t[i++] = .5000;
471:   y[i]   = 54.7000;
472:   t[i++] = .7500;
473:   y[i]   = 48.0000;
474:   t[i++] = 1.0000;
475:   y[i]   = 39.8000;
476:   t[i++] = 1.5000;
477:   y[i]   = 29.8000;
478:   t[i++] = 2.0000;
479:   y[i]   = 23.7000;
480:   t[i++] = 2.5000;
481:   y[i]   = 29.6200;
482:   t[i++] = 2.0000;
483:   y[i]   = 23.8100;
484:   t[i++] = 2.5000;
485:   y[i]   = 17.7000;
486:   t[i++] = 3.0000;
487:   y[i]   = 11.5500;
488:   t[i++] = 4.0000;
489:   y[i]   = 12.0700;
490:   t[i++] = 5.0000;
491:   y[i]   = 8.7400;
492:   t[i++] = 6.0000;
493:   y[i]   = 80.7000;
494:   t[i++] = .5000;
495:   y[i]   = 61.3000;
496:   t[i++] = .7500;
497:   y[i]   = 47.5000;
498:   t[i++] = 1.0000;
499:   y[i]   = 29.0000;
500:   t[i++] = 1.5000;
501:   y[i]   = 24.0000;
502:   t[i++] = 2.0000;
503:   y[i]   = 17.7000;
504:   t[i++] = 2.5000;
505:   y[i]   = 24.5600;
506:   t[i++] = 2.0000;
507:   y[i]   = 18.6700;
508:   t[i++] = 2.5000;
509:   y[i]   = 16.2400;
510:   t[i++] = 3.0000;
511:   y[i]   = 8.7400;
512:   t[i++] = 4.0000;
513:   y[i]   = 7.8700;
514:   t[i++] = 5.0000;
515:   y[i]   = 8.5100;
516:   t[i++] = 6.0000;
517:   y[i]   = 66.7000;
518:   t[i++] = .5000;
519:   y[i]   = 59.2000;
520:   t[i++] = .7500;
521:   y[i]   = 40.8000;
522:   t[i++] = 1.0000;
523:   y[i]   = 30.7000;
524:   t[i++] = 1.5000;
525:   y[i]   = 25.7000;
526:   t[i++] = 2.0000;
527:   y[i]   = 16.3000;
528:   t[i++] = 2.5000;
529:   y[i]   = 25.9900;
530:   t[i++] = 2.0000;
531:   y[i]   = 16.9500;
532:   t[i++] = 2.5000;
533:   y[i]   = 13.3500;
534:   t[i++] = 3.0000;
535:   y[i]   = 8.6200;
536:   t[i++] = 4.0000;
537:   y[i]   = 7.2000;
538:   t[i++] = 5.0000;
539:   y[i]   = 6.6400;
540:   t[i++] = 6.0000;
541:   y[i]   = 13.6900;
542:   t[i++] = 3.0000;
543:   y[i]   = 81.0000;
544:   t[i++] = .5000;
545:   y[i]   = 64.5000;
546:   t[i++] = .7500;
547:   y[i]   = 35.5000;
548:   t[i++] = 1.5000;
549:   y[i]   = 13.3100;
550:   t[i++] = 3.0000;
551:   y[i]   = 4.8700;
552:   t[i++] = 6.0000;
553:   y[i]   = 12.9400;
554:   t[i++] = 3.0000;
555:   y[i]   = 5.0600;
556:   t[i++] = 6.0000;
557:   y[i]   = 15.1900;
558:   t[i++] = 3.0000;
559:   y[i]   = 14.6200;
560:   t[i++] = 3.0000;
561:   y[i]   = 15.6400;
562:   t[i++] = 3.0000;
563:   y[i]   = 25.5000;
564:   t[i++] = 1.7500;
565:   y[i]   = 25.9500;
566:   t[i++] = 1.7500;
567:   y[i]   = 81.7000;
568:   t[i++] = .5000;
569:   y[i]   = 61.6000;
570:   t[i++] = .7500;
571:   y[i]   = 29.8000;
572:   t[i++] = 1.7500;
573:   y[i]   = 29.8100;
574:   t[i++] = 1.7500;
575:   y[i]   = 17.1700;
576:   t[i++] = 2.7500;
577:   y[i]   = 10.3900;
578:   t[i++] = 3.7500;
579:   y[i]   = 28.4000;
580:   t[i++] = 1.7500;
581:   y[i]   = 28.6900;
582:   t[i++] = 1.7500;
583:   y[i]   = 81.3000;
584:   t[i++] = .5000;
585:   y[i]   = 60.9000;
586:   t[i++] = .7500;
587:   y[i]   = 16.6500;
588:   t[i++] = 2.7500;
589:   y[i]   = 10.0500;
590:   t[i++] = 3.7500;
591:   y[i]   = 28.9000;
592:   t[i++] = 1.7500;
593:   y[i]   = 28.9500;
594:   t[i++] = 1.7500;
595:   return 0;
596: }

598: PetscErrorCode TaskWorker(AppCtx *user)
599: {
600:   PetscReal   x[NPARAMETERS], f = 0.0;
601:   PetscMPIInt tag = IDLE_TAG;
602:   PetscInt    index;
603:   MPI_Status  status;

605:   /* Send check-in message to rank-0 */

607:   MPI_Send(&f, 1, MPIU_REAL, 0, IDLE_TAG, PETSC_COMM_WORLD);
608:   while (tag != DIE_TAG) {
609:     MPI_Recv(x, NPARAMETERS, MPIU_REAL, 0, MPI_ANY_TAG, PETSC_COMM_WORLD, &status);
610:     tag = status.MPI_TAG;
611:     if (tag == IDLE_TAG) {
612:       MPI_Send(&f, 1, MPIU_REAL, 0, IDLE_TAG, PETSC_COMM_WORLD);
613:     } else if (tag != DIE_TAG) {
614:       index = (PetscInt)tag;
615:       RunSimulation(x, index, &f, user);
616:       MPI_Send(&f, 1, MPIU_REAL, 0, tag, PETSC_COMM_WORLD);
617:     }
618:   }
619:   return 0;
620: }

622: PetscErrorCode RunSimulation(PetscReal *x, PetscInt i, PetscReal *f, AppCtx *user)
623: {
624:   PetscReal *t = user->t;
625:   PetscReal *y = user->y;
626: #if defined(PETSC_USE_REAL_SINGLE)
627:   *f = y[i] - exp(-x[0] * t[i]) / (x[1] + x[2] * t[i]); /* expf() for single-precision breaks this example on Freebsd, Valgrind errors on Linux */
628: #else
629:   *f = y[i] - PetscExpScalar(-x[0] * t[i]) / (x[1] + x[2] * t[i]);
630: #endif
631:   return (0);
632: }

634: PetscErrorCode StopWorkers(AppCtx *user)
635: {
636:   PetscInt   checkedin;
637:   MPI_Status status;
638:   PetscReal  f, x[NPARAMETERS];

640:   checkedin = 0;
641:   while (checkedin < user->size - 1) {
642:     MPI_Recv(&f, 1, MPIU_REAL, MPI_ANY_SOURCE, MPI_ANY_TAG, PETSC_COMM_WORLD, &status);
643:     checkedin++;
644:     PetscArrayzero(x, NPARAMETERS);
645:     MPI_Send(x, NPARAMETERS, MPIU_REAL, status.MPI_SOURCE, DIE_TAG, PETSC_COMM_WORLD);
646:   }
647:   return 0;
648: }

650: /*TEST

652:    build:
653:       requires: !complex

655:    test:
656:       nsize: 3
657:       requires: !single
658:       args: -tao_smonitor -tao_max_it 100 -tao_type pounders -tao_gatol 1.e-5

660: TEST*/