Date: 2025-03-29T05:58:30+00:00

Actual source code: nn.c

      1: #include <../src/ksp/pc/impls/is/nn/nn.h>

      3: /*
      4:    PCSetUp_NN - Prepares for the use of the NN preconditioner
      5:                     by setting data structures and options.

      7:    Input Parameter:
      8: .  pc - the preconditioner context

     10:    Application Interface Routine: PCSetUp()

     12:    Note:
     13:    The interface routine PCSetUp() is not usually called directly by
     14:    the user, but instead is called by PCApply() if necessary.
     15: */
     16: static PetscErrorCode PCSetUp_NN(PC pc)
     17: {
     18:   PetscFunctionBegin;
     19:   if (!pc->setupcalled) {
     20:     /* Set up all the "iterative substructuring" common block */
     21:     PetscCall(PCISSetUp(pc, PETSC_TRUE, PETSC_TRUE));
     22:     /* Create the coarse matrix. */
     23:     PetscCall(PCNNCreateCoarseMatrix(pc));
     24:   }
     25:   PetscFunctionReturn(PETSC_SUCCESS);
     26: }

     28: /*
     29:    PCApply_NN - Applies the NN preconditioner to a vector.

     31:    Input Parameters:
     32: +  pc - the preconditioner context
     33: -  r - input vector (global)

     35:    Output Parameter:
     36: .  z - output vector (global)

     38:    Application Interface Routine: PCApply()
     39:  */
     40: static PetscErrorCode PCApply_NN(PC pc, Vec r, Vec z)
     41: {
     42:   PC_IS      *pcis  = (PC_IS *)pc->data;
     43:   PetscScalar m_one = -1.0;
     44:   Vec         w     = pcis->vec1_global;

     46:   PetscFunctionBegin;
     47:   /*
     48:     Dirichlet solvers.
     49:     Solving $ B_I^{(i)}r_I^{(i)} $ at each processor.
     50:     Storing the local results at vec2_D
     51:   */
     52:   PetscCall(VecScatterBegin(pcis->global_to_D, r, pcis->vec1_D, INSERT_VALUES, SCATTER_FORWARD));
     53:   PetscCall(VecScatterEnd(pcis->global_to_D, r, pcis->vec1_D, INSERT_VALUES, SCATTER_FORWARD));
     54:   PetscCall(KSPSolve(pcis->ksp_D, pcis->vec1_D, pcis->vec2_D));

     56:   /*
     57:     Computing $ r_B - \sum_j \tilde R_j^T A_{BI}^{(j)} (B_I^{(j)}r_I^{(j)}) $ .
     58:     Storing the result in the interface portion of the global vector w.
     59:   */
     60:   PetscCall(MatMult(pcis->A_BI, pcis->vec2_D, pcis->vec1_B));
     61:   PetscCall(VecScale(pcis->vec1_B, m_one));
     62:   PetscCall(VecCopy(r, w));
     63:   PetscCall(VecScatterBegin(pcis->global_to_B, pcis->vec1_B, w, ADD_VALUES, SCATTER_REVERSE));
     64:   PetscCall(VecScatterEnd(pcis->global_to_B, pcis->vec1_B, w, ADD_VALUES, SCATTER_REVERSE));

     66:   /*
     67:     Apply the interface preconditioner
     68:   */
     69:   PetscCall(PCNNApplyInterfacePreconditioner(pc, w, z, pcis->work_N, pcis->vec1_B, pcis->vec2_B, pcis->vec3_B, pcis->vec1_D, pcis->vec3_D, pcis->vec1_N, pcis->vec2_N));

     71:   /*
     72:     Computing $ t_I^{(i)} = A_{IB}^{(i)} \tilde R_i z_B $
     73:     The result is stored in vec1_D.
     74:   */
     75:   PetscCall(VecScatterBegin(pcis->global_to_B, z, pcis->vec1_B, INSERT_VALUES, SCATTER_FORWARD));
     76:   PetscCall(VecScatterEnd(pcis->global_to_B, z, pcis->vec1_B, INSERT_VALUES, SCATTER_FORWARD));
     77:   PetscCall(MatMult(pcis->A_IB, pcis->vec1_B, pcis->vec1_D));

     79:   /*
     80:     Dirichlet solvers.
     81:     Computing $ B_I^{(i)}t_I^{(i)} $ and sticking into the global vector the blocks
     82:     $ B_I^{(i)}r_I^{(i)} - B_I^{(i)}t_I^{(i)} $.
     83:   */
     84:   PetscCall(VecScatterBegin(pcis->global_to_D, pcis->vec2_D, z, INSERT_VALUES, SCATTER_REVERSE));
     85:   PetscCall(VecScatterEnd(pcis->global_to_D, pcis->vec2_D, z, INSERT_VALUES, SCATTER_REVERSE));
     86:   PetscCall(KSPSolve(pcis->ksp_D, pcis->vec1_D, pcis->vec2_D));
     87:   PetscCall(VecScale(pcis->vec2_D, m_one));
     88:   PetscCall(VecScatterBegin(pcis->global_to_D, pcis->vec2_D, z, ADD_VALUES, SCATTER_REVERSE));
     89:   PetscCall(VecScatterEnd(pcis->global_to_D, pcis->vec2_D, z, ADD_VALUES, SCATTER_REVERSE));
     90:   PetscFunctionReturn(PETSC_SUCCESS);
     91: }

     93: /*
     94:    PCDestroy_NN - Destroys the private context for the NN preconditioner
     95:    that was created with PCCreate_NN().

     97:    Input Parameter:
     98: .  pc - the preconditioner context

    100:    Application Interface Routine: PCDestroy()
    101: */
    102: static PetscErrorCode PCDestroy_NN(PC pc)
    103: {
    104:   PC_NN *pcnn = (PC_NN *)pc->data;

    106:   PetscFunctionBegin;
    107:   PetscCall(PCISReset(pc));

    109:   PetscCall(MatDestroy(&pcnn->coarse_mat));
    110:   PetscCall(VecDestroy(&pcnn->coarse_x));
    111:   PetscCall(VecDestroy(&pcnn->coarse_b));
    112:   PetscCall(KSPDestroy(&pcnn->ksp_coarse));
    113:   if (pcnn->DZ_IN) {
    114:     PetscCall(PetscFree(pcnn->DZ_IN[0]));
    115:     PetscCall(PetscFree(pcnn->DZ_IN));
    116:   }

    118:   /*
    119:       Free the private data structure that was hanging off the PC
    120:   */
    121:   PetscCall(PetscFree(pc->data));
    122:   PetscFunctionReturn(PETSC_SUCCESS);
    123: }

    125: /*MC
    126:    PCNN - Balancing Neumann-Neumann for scalar elliptic PDEs.

    128:    Options Database Keys:
    129: +    -pc_nn_turn_off_first_balancing - do not balance the residual before solving the local Neumann problems
    130:                                        (this skips the first coarse grid solve in the preconditioner)
    131: .    -pc_nn_turn_off_second_balancing - do not balance the solution solving the local Neumann problems
    132:                                        (this skips the second coarse grid solve in the preconditioner)
    133: .    -pc_is_damp_fixed <fact> -
    134: .    -pc_is_remove_nullspace_fixed -
    135: .    -pc_is_set_damping_factor_floating <fact> -
    136: .    -pc_is_not_damp_floating -
    137: -    -pc_is_not_remove_nullspace_floating -

    139:    Options Database prefixes for the subsolvers this preconditioner uses:
    140: +  -nn_coarse_pc_ - for the coarse grid preconditioner
    141: .  -is_localD_pc_ - for the Dirichlet subproblem preconditioner
    142: -  -is_localN_pc_ - for the Neumann subproblem preconditioner

    144:    Level: intermediate

    146:    Notes:
    147:    The matrix used with this preconditioner must be of type `MATIS`

    149:    Unlike more 'conventional' Neumann-Neumann preconditioners this iterates over ALL the
    150:    degrees of freedom, NOT just those on the interface (this allows the use of approximate solvers
    151:    on the subdomains; though in our experience using approximate solvers is slower.).

    153:    Contributed by Paulo Goldfeld

    155: .seealso: [](ch_ksp), `PCCreate()`, `PCSetType()`, `PCType`, `PC`, `MATIS`, `PCBDDC`
    156: M*/

    158: PETSC_EXTERN PetscErrorCode PCCreate_NN(PC pc)
    159: {
    160:   PC_NN *pcnn;

    162:   PetscFunctionBegin;
    163:   /*
    164:      Creates the private data structure for this preconditioner and
    165:      attach it to the PC object.
    166:   */
    167:   PetscCall(PetscNew(&pcnn));
    168:   pc->data = (void *)pcnn;

    170:   PetscCall(PCISInitialize(pc));
    171:   pcnn->coarse_mat = NULL;
    172:   pcnn->coarse_x   = NULL;
    173:   pcnn->coarse_b   = NULL;
    174:   pcnn->ksp_coarse = NULL;
    175:   pcnn->DZ_IN      = NULL;

    177:   /*
    178:       Set the pointers for the functions that are provided above.
    179:       Now when the user-level routines (such as PCApply(), PCDestroy(), etc.)
    180:       are called, they will automatically call these functions.  Note we
    181:       choose not to provide a couple of these functions since they are
    182:       not needed.
    183:   */
    184:   pc->ops->apply               = PCApply_NN;
    185:   pc->ops->applytranspose      = NULL;
    186:   pc->ops->setup               = PCSetUp_NN;
    187:   pc->ops->destroy             = PCDestroy_NN;
    188:   pc->ops->view                = NULL;
    189:   pc->ops->applyrichardson     = NULL;
    190:   pc->ops->applysymmetricleft  = NULL;
    191:   pc->ops->applysymmetricright = NULL;
    192:   PetscFunctionReturn(PETSC_SUCCESS);
    193: }

    195: /*
    196:    PCNNCreateCoarseMatrix -
    197: */
    198: PetscErrorCode PCNNCreateCoarseMatrix(PC pc)
    199: {
    200:   MPI_Request  *send_request, *recv_request;
    201:   PetscInt      i, j, k;
    202:   PetscScalar  *mat;    /* Sub-matrix with this subdomain's contribution to the coarse matrix             */
    203:   PetscScalar **DZ_OUT; /* proc[k].DZ_OUT[i][] = bit of vector to be sent from processor k to processor i */

    205:   PC_IS        *pcis     = (PC_IS *)pc->data;
    206:   PC_NN        *pcnn     = (PC_NN *)pc->data;
    207:   PetscInt     *neigh    = pcis->neigh;
    208:   PetscInt     *n_shared = pcis->n_shared;
    209:   PetscInt    **shared   = pcis->shared;
    210:   PetscMPIInt   n_neigh;
    211:   PetscScalar **DZ_IN; /* Must be initialized after memory allocation. */

    213:   PetscFunctionBegin;
    214:   PetscCall(PetscMPIIntCast(pcis->n_neigh, &n_neigh));
    215:   /* Allocate memory for mat (the +1 is to handle the case n_neigh equal to zero) */
    216:   PetscCall(PetscMalloc1(n_neigh * n_neigh + 1, &mat));

    218:   /* Allocate memory for DZ */
    219:   /* Notice that DZ_OUT[0] is allocated some space that is never used. */
    220:   /* This is just in order to DZ_OUT and DZ_IN to have exactly the same form. */
    221:   {
    222:     PetscInt size_of_Z = 0;
    223:     PetscCall(PetscMalloc((n_neigh + 1) * sizeof(PetscScalar *), &pcnn->DZ_IN));
    224:     DZ_IN = pcnn->DZ_IN;
    225:     PetscCall(PetscMalloc((n_neigh + 1) * sizeof(PetscScalar *), &DZ_OUT));
    226:     for (i = 0; i < n_neigh; i++) size_of_Z += n_shared[i];
    227:     PetscCall(PetscMalloc((size_of_Z + 1) * sizeof(PetscScalar), &DZ_IN[0]));
    228:     PetscCall(PetscMalloc((size_of_Z + 1) * sizeof(PetscScalar), &DZ_OUT[0]));
    229:   }
    230:   for (i = 1; i < n_neigh; i++) {
    231:     DZ_IN[i]  = DZ_IN[i - 1] + n_shared[i - 1];
    232:     DZ_OUT[i] = DZ_OUT[i - 1] + n_shared[i - 1];
    233:   }

    235:   /* Set the values of DZ_OUT, in order to send this info to the neighbours */
    236:   /* First, set the auxiliary array pcis->work_N. */
    237:   PetscCall(PCISScatterArrayNToVecB(pc, pcis->work_N, pcis->D, INSERT_VALUES, SCATTER_REVERSE));
    238:   for (i = 1; i < n_neigh; i++) {
    239:     for (j = 0; j < n_shared[i]; j++) DZ_OUT[i][j] = pcis->work_N[shared[i][j]];
    240:   }

    242:   /* Non-blocking send/receive the common-interface chunks of scaled nullspaces */
    243:   /* Notice that send_request[] and recv_request[] could have one less element. */
    244:   /* We make them longer to have request[i] corresponding to neigh[i].          */
    245:   {
    246:     PetscMPIInt tag;
    247:     PetscCall(PetscObjectGetNewTag((PetscObject)pc, &tag));
    248:     PetscCall(PetscMalloc2(n_neigh + 1, &send_request, n_neigh + 1, &recv_request));
    249:     for (i = 1; i < n_neigh; i++) {
    250:       PetscMPIInt nn;

    252:       PetscCall(PetscMPIIntCast(neigh[i], &nn));
    253:       PetscCallMPI(MPIU_Isend(DZ_OUT[i], n_shared[i], MPIU_SCALAR, nn, tag, PetscObjectComm((PetscObject)pc), &send_request[i]));
    254:       PetscCallMPI(MPIU_Irecv(DZ_IN[i], n_shared[i], MPIU_SCALAR, nn, tag, PetscObjectComm((PetscObject)pc), &recv_request[i]));
    255:     }
    256:   }

    258:   /* Set DZ_IN[0][] (recall that neigh[0]==rank, always) */
    259:   for (j = 0; j < n_shared[0]; j++) DZ_IN[0][j] = pcis->work_N[shared[0][j]];

    261:   /* Start computing with local D*Z while communication goes on.    */
    262:   /* Apply Schur complement. The result is "stored" in vec (more    */
    263:   /* precisely, vec points to the result, stored in pc_nn->vec1_B)  */
    264:   /* and also scattered to pcnn->work_N.                            */
    265:   PetscCall(PCNNApplySchurToChunk(pc, n_shared[0], shared[0], DZ_IN[0], pcis->work_N, pcis->vec1_B, pcis->vec2_B, pcis->vec1_D, pcis->vec2_D));

    267:   /* Compute the first column, while completing the receiving. */
    268:   for (i = 0; i < n_neigh; i++) {
    269:     MPI_Status  stat;
    270:     PetscMPIInt ind = 0;
    271:     if (i > 0) {
    272:       PetscCallMPI(MPI_Waitany(n_neigh - 1, recv_request + 1, &ind, &stat));
    273:       ind++;
    274:     }
    275:     mat[ind * n_neigh + 0] = 0.0;
    276:     for (k = 0; k < n_shared[ind]; k++) mat[ind * n_neigh + 0] += DZ_IN[ind][k] * pcis->work_N[shared[ind][k]];
    277:   }

    279:   /* Compute the remaining of the columns */
    280:   for (j = 1; j < n_neigh; j++) {
    281:     PetscCall(PCNNApplySchurToChunk(pc, n_shared[j], shared[j], DZ_IN[j], pcis->work_N, pcis->vec1_B, pcis->vec2_B, pcis->vec1_D, pcis->vec2_D));
    282:     for (i = 0; i < n_neigh; i++) {
    283:       mat[i * n_neigh + j] = 0.0;
    284:       for (k = 0; k < n_shared[i]; k++) mat[i * n_neigh + j] += DZ_IN[i][k] * pcis->work_N[shared[i][k]];
    285:     }
    286:   }

    288:   /* Complete the sending. */
    289:   if (n_neigh > 1) {
    290:     MPI_Status *stat;
    291:     PetscCall(PetscMalloc1(n_neigh - 1, &stat));
    292:     if (n_neigh - 1) PetscCallMPI(MPI_Waitall(n_neigh - 1, &send_request[1], stat));
    293:     PetscCall(PetscFree(stat));
    294:   }

    296:   /* Free the memory for the MPI requests */
    297:   PetscCall(PetscFree2(send_request, recv_request));

    299:   /* Free the memory for DZ_OUT */
    300:   if (DZ_OUT) {
    301:     PetscCall(PetscFree(DZ_OUT[0]));
    302:     PetscCall(PetscFree(DZ_OUT));
    303:   }

    305:   {
    306:     PetscMPIInt size;
    307:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)pc), &size));
    308:     /* Create the global coarse vectors (rhs and solution). */
    309:     PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)pc), 1, size, &pcnn->coarse_b));
    310:     PetscCall(VecDuplicate(pcnn->coarse_b, &pcnn->coarse_x));
    311:     /* Create and set the global coarse AIJ matrix. */
    312:     PetscCall(MatCreate(PetscObjectComm((PetscObject)pc), &pcnn->coarse_mat));
    313:     PetscCall(MatSetSizes(pcnn->coarse_mat, 1, 1, size, size));
    314:     PetscCall(MatSetType(pcnn->coarse_mat, MATAIJ));
    315:     PetscCall(MatSeqAIJSetPreallocation(pcnn->coarse_mat, 1, NULL));
    316:     PetscCall(MatMPIAIJSetPreallocation(pcnn->coarse_mat, 1, NULL, n_neigh, NULL));
    317:     PetscCall(MatSetOption(pcnn->coarse_mat, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE));
    318:     PetscCall(MatSetOption(pcnn->coarse_mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_FALSE));
    319:     PetscCall(MatSetValues(pcnn->coarse_mat, n_neigh, neigh, n_neigh, neigh, mat, ADD_VALUES));
    320:     PetscCall(MatAssemblyBegin(pcnn->coarse_mat, MAT_FINAL_ASSEMBLY));
    321:     PetscCall(MatAssemblyEnd(pcnn->coarse_mat, MAT_FINAL_ASSEMBLY));
    322:   }

    324:   {
    325:     PetscMPIInt rank;
    326:     PetscScalar one = 1.0;
    327:     PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)pc), &rank));
    328:     /* "Zero out" rows of not-purely-Neumann subdomains */
    329:     if (pcis->pure_neumann) { /* does NOT zero the row; create an empty index set. The reason is that MatZeroRows() is collective. */
    330:       PetscCall(MatZeroRows(pcnn->coarse_mat, 0, NULL, one, NULL, NULL));
    331:     } else { /* here it DOES zero the row, since it's not a floating subdomain. */
    332:       PetscInt row = rank;
    333:       PetscCall(MatZeroRows(pcnn->coarse_mat, 1, &row, one, NULL, NULL));
    334:     }
    335:   }

    337:   /* Create the coarse linear solver context */
    338:   {
    339:     PC  pc_ctx, inner_pc;
    340:     KSP inner_ksp;

    342:     PetscCall(KSPCreate(PetscObjectComm((PetscObject)pc), &pcnn->ksp_coarse));
    343:     PetscCall(KSPSetNestLevel(pcnn->ksp_coarse, pc->kspnestlevel));
    344:     PetscCall(PetscObjectIncrementTabLevel((PetscObject)pcnn->ksp_coarse, (PetscObject)pc, 2));
    345:     PetscCall(KSPSetOperators(pcnn->ksp_coarse, pcnn->coarse_mat, pcnn->coarse_mat));
    346:     PetscCall(KSPGetPC(pcnn->ksp_coarse, &pc_ctx));
    347:     PetscCall(PCSetType(pc_ctx, PCREDUNDANT));
    348:     PetscCall(KSPSetType(pcnn->ksp_coarse, KSPPREONLY));
    349:     PetscCall(PCRedundantGetKSP(pc_ctx, &inner_ksp));
    350:     PetscCall(KSPGetPC(inner_ksp, &inner_pc));
    351:     PetscCall(PCSetType(inner_pc, PCLU));
    352:     PetscCall(KSPSetOptionsPrefix(pcnn->ksp_coarse, "nn_coarse_"));
    353:     PetscCall(KSPSetFromOptions(pcnn->ksp_coarse));
    354:     /* the vectors in the following line are dummy arguments, just telling the KSP the vector size. Values are not used */
    355:     PetscCall(KSPSetUp(pcnn->ksp_coarse));
    356:   }

    358:   /* Free the memory for mat */
    359:   PetscCall(PetscFree(mat));

    361:   /* for DEBUGGING, save the coarse matrix to a file. */
    362:   {
    363:     PetscBool flg = PETSC_FALSE;
    364:     PetscCall(PetscOptionsGetBool(NULL, NULL, "-pc_nn_save_coarse_matrix", &flg, NULL));
    365:     if (flg) {
    366:       PetscViewer viewer;
    367:       PetscCall(PetscViewerASCIIOpen(PETSC_COMM_WORLD, "coarse.m", &viewer));
    368:       PetscCall(PetscViewerPushFormat(viewer, PETSC_VIEWER_ASCII_MATLAB));
    369:       PetscCall(MatView(pcnn->coarse_mat, viewer));
    370:       PetscCall(PetscViewerPopFormat(viewer));
    371:       PetscCall(PetscViewerDestroy(&viewer));
    372:     }
    373:   }

    375:   /*  Set the variable pcnn->factor_coarse_rhs. */
    376:   pcnn->factor_coarse_rhs = (pcis->pure_neumann) ? 1.0 : 0.0;

    378:   /* See historical note 02, at the bottom of this file. */
    379:   PetscFunctionReturn(PETSC_SUCCESS);
    380: }

    382: /*
    383:    PCNNApplySchurToChunk -

    385:    Input parameters:
    386: .  pcnn
    387: .  n - size of chunk
    388: .  idx - indices of chunk
    389: .  chunk - values

    391:    Output parameters:
    392: .  array_N - result of Schur complement applied to chunk, scattered to big array
    393: .  vec1_B  - result of Schur complement applied to chunk
    394: .  vec2_B  - garbage (used as work space)
    395: .  vec1_D  - garbage (used as work space)
    396: .  vec2_D  - garbage (used as work space)

    398: */
    399: PetscErrorCode PCNNApplySchurToChunk(PC pc, PetscInt n, PetscInt *idx, PetscScalar *chunk, PetscScalar *array_N, Vec vec1_B, Vec vec2_B, Vec vec1_D, Vec vec2_D)
    400: {
    401:   PetscInt i;
    402:   PC_IS   *pcis = (PC_IS *)pc->data;

    404:   PetscFunctionBegin;
    405:   PetscCall(PetscArrayzero(array_N, pcis->n));
    406:   for (i = 0; i < n; i++) array_N[idx[i]] = chunk[i];
    407:   PetscCall(PCISScatterArrayNToVecB(pc, array_N, vec2_B, INSERT_VALUES, SCATTER_FORWARD));
    408:   PetscCall(PCISApplySchur(pc, vec2_B, vec1_B, (Vec)0, vec1_D, vec2_D));
    409:   PetscCall(PCISScatterArrayNToVecB(pc, array_N, vec1_B, INSERT_VALUES, SCATTER_REVERSE));
    410:   PetscFunctionReturn(PETSC_SUCCESS);
    411: }

    413: /*
    414:    PCNNApplyInterfacePreconditioner - Apply the interface preconditioner, i.e.,
    415:                                       the preconditioner for the Schur complement.

    417:    Input parameter:
    418: .  r - global vector of interior and interface nodes. The values on the interior nodes are NOT used.

    420:    Output parameters:
    421: .  z - global vector of interior and interface nodes. The values on the interface are the result of
    422:        the application of the interface preconditioner to the interface part of r. The values on the
    423:        interior nodes are garbage.
    424: .  work_N - array of local nodes (interior and interface, including ghosts); returns garbage (used as work space)
    425: .  vec1_B - vector of local interface nodes (including ghosts); returns garbage (used as work space)
    426: .  vec2_B - vector of local interface nodes (including ghosts); returns garbage (used as work space)
    427: .  vec3_B - vector of local interface nodes (including ghosts); returns garbage (used as work space)
    428: .  vec1_D - vector of local interior nodes; returns garbage (used as work space)
    429: .  vec2_D - vector of local interior nodes; returns garbage (used as work space)
    430: .  vec1_N - vector of local nodes (interior and interface, including ghosts); returns garbage (used as work space)
    431: .  vec2_N - vector of local nodes (interior and interface, including ghosts); returns garbage (used as work space)

    433: */
    434: PetscErrorCode PCNNApplyInterfacePreconditioner(PC pc, Vec r, Vec z, PetscScalar *work_N, Vec vec1_B, Vec vec2_B, Vec vec3_B, Vec vec1_D, Vec vec2_D, Vec vec1_N, Vec vec2_N)
    435: {
    436:   PC_IS *pcis = (PC_IS *)pc->data;

    438:   PetscFunctionBegin;
    439:   /*
    440:     First balancing step.
    441:   */
    442:   {
    443:     PetscBool flg = PETSC_FALSE;
    444:     PetscCall(PetscOptionsGetBool(NULL, NULL, "-pc_nn_turn_off_first_balancing", &flg, NULL));
    445:     if (!flg) {
    446:       PetscCall(PCNNBalancing(pc, r, (Vec)0, z, vec1_B, vec2_B, (Vec)0, vec1_D, vec2_D, work_N));
    447:     } else {
    448:       PetscCall(VecCopy(r, z));
    449:     }
    450:   }

    452:   /*
    453:     Extract the local interface part of z and scale it by D
    454:   */
    455:   PetscCall(VecScatterBegin(pcis->global_to_B, z, vec1_B, INSERT_VALUES, SCATTER_FORWARD));
    456:   PetscCall(VecScatterEnd(pcis->global_to_B, z, vec1_B, INSERT_VALUES, SCATTER_FORWARD));
    457:   PetscCall(VecPointwiseMult(vec2_B, pcis->D, vec1_B));

    459:   /* Neumann Solver */
    460:   PetscCall(PCISApplyInvSchur(pc, vec2_B, vec1_B, vec1_N, vec2_N));

    462:   /*
    463:     Second balancing step.
    464:   */
    465:   {
    466:     PetscBool flg = PETSC_FALSE;
    467:     PetscCall(PetscOptionsGetBool(NULL, NULL, "-pc_turn_off_second_balancing", &flg, NULL));
    468:     if (!flg) {
    469:       PetscCall(PCNNBalancing(pc, r, vec1_B, z, vec2_B, vec3_B, (Vec)0, vec1_D, vec2_D, work_N));
    470:     } else {
    471:       PetscCall(VecPointwiseMult(vec2_B, pcis->D, vec1_B));
    472:       PetscCall(VecSet(z, 0.0));
    473:       PetscCall(VecScatterBegin(pcis->global_to_B, vec2_B, z, ADD_VALUES, SCATTER_REVERSE));
    474:       PetscCall(VecScatterEnd(pcis->global_to_B, vec2_B, z, ADD_VALUES, SCATTER_REVERSE));
    475:     }
    476:   }
    477:   PetscFunctionReturn(PETSC_SUCCESS);
    478: }

    480: /*
    481:    PCNNBalancing - Computes z, as given in equations (15) and (16) (if the
    482:                    input argument u is provided), or s, as given in equations
    483:                    (12) and (13), if the input argument u is a null vector.
    484:                    Notice that the input argument u plays the role of u_i in
    485:                    equation (14). The equation numbers refer to [Man93].

    487:    Input Parameters:
    488: +  pcnn - NN preconditioner context.
    489: .  r - MPI vector of all nodes (interior and interface). It's preserved.
    490: -  u - (Optional) sequential vector of local interface nodes. It's preserved UNLESS vec3_B is null.

    492:    Output Parameters:
    493: +  z - MPI vector of interior and interface nodes. Returns s or z (see description above).
    494: .  vec1_B - Sequential vector of local interface nodes. Workspace.
    495: .  vec2_B - Sequential vector of local interface nodes. Workspace.
    496: .  vec3_B - (Optional) sequential vector of local interface nodes. Workspace.
    497: .  vec1_D - Sequential vector of local interior nodes. Workspace.
    498: .  vec2_D - Sequential vector of local interior nodes. Workspace.
    499: -  work_N - Array of all local nodes (interior and interface). Workspace.

    501: */
    502: PetscErrorCode PCNNBalancing(PC pc, Vec r, Vec u, Vec z, Vec vec1_B, Vec vec2_B, Vec vec3_B, Vec vec1_D, Vec vec2_D, PetscScalar *work_N)
    503: {
    504:   PetscInt     k;
    505:   PetscScalar  value;
    506:   PetscScalar *lambda;
    507:   PC_NN       *pcnn = (PC_NN *)pc->data;
    508:   PC_IS       *pcis = (PC_IS *)pc->data;

    510:   PetscFunctionBegin;
    511:   PetscCall(PetscLogEventBegin(PC_ApplyCoarse, pc, 0, 0, 0));
    512:   if (u) {
    513:     if (!vec3_B) vec3_B = u;
    514:     PetscCall(VecPointwiseMult(vec1_B, pcis->D, u));
    515:     PetscCall(VecSet(z, 0.0));
    516:     PetscCall(VecScatterBegin(pcis->global_to_B, vec1_B, z, ADD_VALUES, SCATTER_REVERSE));
    517:     PetscCall(VecScatterEnd(pcis->global_to_B, vec1_B, z, ADD_VALUES, SCATTER_REVERSE));
    518:     PetscCall(VecScatterBegin(pcis->global_to_B, z, vec2_B, INSERT_VALUES, SCATTER_FORWARD));
    519:     PetscCall(VecScatterEnd(pcis->global_to_B, z, vec2_B, INSERT_VALUES, SCATTER_FORWARD));
    520:     PetscCall(PCISApplySchur(pc, vec2_B, vec3_B, (Vec)0, vec1_D, vec2_D));
    521:     PetscCall(VecScale(vec3_B, -1.0));
    522:     PetscCall(VecCopy(r, z));
    523:     PetscCall(VecScatterBegin(pcis->global_to_B, vec3_B, z, ADD_VALUES, SCATTER_REVERSE));
    524:     PetscCall(VecScatterEnd(pcis->global_to_B, vec3_B, z, ADD_VALUES, SCATTER_REVERSE));
    525:   } else {
    526:     PetscCall(VecCopy(r, z));
    527:   }
    528:   PetscCall(VecScatterBegin(pcis->global_to_B, z, vec2_B, INSERT_VALUES, SCATTER_FORWARD));
    529:   PetscCall(VecScatterEnd(pcis->global_to_B, z, vec2_B, INSERT_VALUES, SCATTER_FORWARD));
    530:   PetscCall(PCISScatterArrayNToVecB(pc, work_N, vec2_B, INSERT_VALUES, SCATTER_REVERSE));
    531:   for (k = 0, value = 0.0; k < pcis->n_shared[0]; k++) value += pcnn->DZ_IN[0][k] * work_N[pcis->shared[0][k]];
    532:   value *= pcnn->factor_coarse_rhs; /* This factor is set in CreateCoarseMatrix(). */
    533:   {
    534:     PetscMPIInt rank;
    535:     PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)pc), &rank));
    536:     PetscCall(VecSetValue(pcnn->coarse_b, rank, value, INSERT_VALUES));
    537:     /*
    538:        Since we are only inserting local values (one value actually) we don't need to do the
    539:        reduction that tells us there is no data that needs to be moved. Hence we comment out these
    540:        PetscCall(VecAssemblyBegin(pcnn->coarse_b));
    541:        PetscCall(VecAssemblyEnd  (pcnn->coarse_b));
    542:     */
    543:   }
    544:   PetscCall(KSPSolve(pcnn->ksp_coarse, pcnn->coarse_b, pcnn->coarse_x));
    545:   if (!u) PetscCall(VecScale(pcnn->coarse_x, -1.0));
    546:   PetscCall(VecGetArray(pcnn->coarse_x, &lambda));
    547:   for (k = 0; k < pcis->n_shared[0]; k++) work_N[pcis->shared[0][k]] = *lambda * pcnn->DZ_IN[0][k];
    548:   PetscCall(VecRestoreArray(pcnn->coarse_x, &lambda));
    549:   PetscCall(PCISScatterArrayNToVecB(pc, work_N, vec2_B, INSERT_VALUES, SCATTER_FORWARD));
    550:   PetscCall(VecSet(z, 0.0));
    551:   PetscCall(VecScatterBegin(pcis->global_to_B, vec2_B, z, ADD_VALUES, SCATTER_REVERSE));
    552:   PetscCall(VecScatterEnd(pcis->global_to_B, vec2_B, z, ADD_VALUES, SCATTER_REVERSE));
    553:   if (!u) {
    554:     PetscCall(VecScatterBegin(pcis->global_to_B, z, vec2_B, INSERT_VALUES, SCATTER_FORWARD));
    555:     PetscCall(VecScatterEnd(pcis->global_to_B, z, vec2_B, INSERT_VALUES, SCATTER_FORWARD));
    556:     PetscCall(PCISApplySchur(pc, vec2_B, vec1_B, (Vec)0, vec1_D, vec2_D));
    557:     PetscCall(VecCopy(r, z));
    558:   }
    559:   PetscCall(VecScatterBegin(pcis->global_to_B, vec1_B, z, ADD_VALUES, SCATTER_REVERSE));
    560:   PetscCall(VecScatterEnd(pcis->global_to_B, vec1_B, z, ADD_VALUES, SCATTER_REVERSE));
    561:   PetscCall(PetscLogEventEnd(PC_ApplyCoarse, pc, 0, 0, 0));
    562:   PetscFunctionReturn(PETSC_SUCCESS);
    563: }

    565: /*  From now on, "footnotes" (or "historical notes").  */
    566: /*
    567:    Historical note 01

    569:    We considered the possibility of an alternative D_i that would still
    570:    provide a partition of unity (i.e., $ \sum_i  N_i D_i N_i^T = I $).
    571:    The basic principle was still the pseudo-inverse of the counting
    572:    function; the difference was that we would not count subdomains
    573:    that do not contribute to the coarse space (i.e., not pure-Neumann
    574:    subdomains).

    576:    This turned out to be a bad idea:  we would solve trivial Neumann
    577:    problems in the not pure-Neumann subdomains, since we would be scaling
    578:    the balanced residual by zero.
    579: */

    581: /*
    582:    Historical note 02

    584:    We tried an alternative coarse problem, that would eliminate exactly a
    585:    constant error. Turned out not to improve the overall convergence.
    586: */