Actual source code: mpiaijkok.kokkos.cxx
1: #include <petscvec_kokkos.hpp>
2: #include <petscpkg_version.h>
3: #include <petsc/private/sfimpl.h>
4: #include <../src/mat/impls/aij/seq/kokkos/aijkok.hpp>
5: #include <../src/mat/impls/aij/mpi/mpiaij.h>
6: #include <KokkosSparse_spadd.hpp>
7: #include <KokkosSparse_spgemm.hpp>
9: static PetscErrorCode MatAssemblyEnd_MPIAIJKokkos(Mat A, MatAssemblyType mode)
10: {
11: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)A->data;
13: PetscFunctionBegin;
14: PetscCall(MatAssemblyEnd_MPIAIJ(A, mode));
15: /* E.g., MatCreateSubMatrix() calls MatCreateMPIAIJWithSeqAIJ(comm,A,B,..), which creates Bnew of SEQAIJ and destroys B of SEQAIJKOKKOS.
16: Thus we finalize A/B/lvec's type in MatAssemblyEnd() to handle various cases.
17: */
18: if (mode == MAT_FINAL_ASSEMBLY) {
19: PetscCall(MatSetType(mpiaij->A, MATSEQAIJKOKKOS));
20: PetscCall(MatSetType(mpiaij->B, MATSEQAIJKOKKOS));
21: PetscCall(VecSetType(mpiaij->lvec, VECSEQKOKKOS));
22: }
24: PetscFunctionReturn(PETSC_SUCCESS);
25: }
27: static PetscErrorCode MatMPIAIJSetPreallocation_MPIAIJKokkos(Mat mat, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[])
28: {
29: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
31: PetscFunctionBegin;
32: PetscCall(PetscLayoutSetUp(mat->rmap));
33: PetscCall(PetscLayoutSetUp(mat->cmap));
34: #if defined(PETSC_USE_DEBUG)
35: if (d_nnz) {
36: PetscInt i;
37: for (i = 0; i < mat->rmap->n; i++) PetscCheck(d_nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "d_nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, d_nnz[i]);
38: }
39: if (o_nnz) {
40: PetscInt i;
41: for (i = 0; i < mat->rmap->n; i++) PetscCheck(o_nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "o_nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, o_nnz[i]);
42: }
43: #endif
44: #if defined(PETSC_USE_CTABLE)
45: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
46: #else
47: PetscCall(PetscFree(mpiaij->colmap));
48: #endif
49: PetscCall(PetscFree(mpiaij->garray));
50: PetscCall(VecDestroy(&mpiaij->lvec));
51: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
52: /* Because the B will have been resized we simply destroy it and create a new one each time */
53: PetscCall(MatDestroy(&mpiaij->B));
55: if (!mpiaij->A) {
56: PetscCall(MatCreate(PETSC_COMM_SELF, &mpiaij->A));
57: PetscCall(MatSetSizes(mpiaij->A, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
58: }
59: if (!mpiaij->B) {
60: PetscMPIInt size;
61: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
62: PetscCall(MatCreate(PETSC_COMM_SELF, &mpiaij->B));
63: PetscCall(MatSetSizes(mpiaij->B, mat->rmap->n, size > 1 ? mat->cmap->N : 0, mat->rmap->n, size > 1 ? mat->cmap->N : 0));
64: }
65: PetscCall(MatSetType(mpiaij->A, MATSEQAIJKOKKOS));
66: PetscCall(MatSetType(mpiaij->B, MATSEQAIJKOKKOS));
67: PetscCall(MatSeqAIJSetPreallocation(mpiaij->A, d_nz, d_nnz));
68: PetscCall(MatSeqAIJSetPreallocation(mpiaij->B, o_nz, o_nnz));
69: mat->preallocated = PETSC_TRUE;
70: PetscFunctionReturn(PETSC_SUCCESS);
71: }
73: static PetscErrorCode MatMult_MPIAIJKokkos(Mat mat, Vec xx, Vec yy)
74: {
75: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
76: PetscInt nt;
78: PetscFunctionBegin;
79: PetscCall(VecGetLocalSize(xx, &nt));
80: PetscCheck(nt == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of mat (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", mat->cmap->n, nt);
81: PetscCall(VecScatterBegin(mpiaij->Mvctx, xx, mpiaij->lvec, INSERT_VALUES, SCATTER_FORWARD));
82: PetscCall((*mpiaij->A->ops->mult)(mpiaij->A, xx, yy));
83: PetscCall(VecScatterEnd(mpiaij->Mvctx, xx, mpiaij->lvec, INSERT_VALUES, SCATTER_FORWARD));
84: PetscCall((*mpiaij->B->ops->multadd)(mpiaij->B, mpiaij->lvec, yy, yy));
85: PetscFunctionReturn(PETSC_SUCCESS);
86: }
88: static PetscErrorCode MatMultAdd_MPIAIJKokkos(Mat mat, Vec xx, Vec yy, Vec zz)
89: {
90: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
91: PetscInt nt;
93: PetscFunctionBegin;
94: PetscCall(VecGetLocalSize(xx, &nt));
95: PetscCheck(nt == mat->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of mat (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", mat->cmap->n, nt);
96: PetscCall(VecScatterBegin(mpiaij->Mvctx, xx, mpiaij->lvec, INSERT_VALUES, SCATTER_FORWARD));
97: PetscCall((*mpiaij->A->ops->multadd)(mpiaij->A, xx, yy, zz));
98: PetscCall(VecScatterEnd(mpiaij->Mvctx, xx, mpiaij->lvec, INSERT_VALUES, SCATTER_FORWARD));
99: PetscCall((*mpiaij->B->ops->multadd)(mpiaij->B, mpiaij->lvec, zz, zz));
100: PetscFunctionReturn(PETSC_SUCCESS);
101: }
103: static PetscErrorCode MatMultTranspose_MPIAIJKokkos(Mat mat, Vec xx, Vec yy)
104: {
105: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
106: PetscInt nt;
108: PetscFunctionBegin;
109: PetscCall(VecGetLocalSize(xx, &nt));
110: PetscCheck(nt == mat->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Incompatible partition of mat (%" PetscInt_FMT ") and xx (%" PetscInt_FMT ")", mat->rmap->n, nt);
111: PetscCall((*mpiaij->B->ops->multtranspose)(mpiaij->B, xx, mpiaij->lvec));
112: PetscCall((*mpiaij->A->ops->multtranspose)(mpiaij->A, xx, yy));
113: PetscCall(VecScatterBegin(mpiaij->Mvctx, mpiaij->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
114: PetscCall(VecScatterEnd(mpiaij->Mvctx, mpiaij->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
115: PetscFunctionReturn(PETSC_SUCCESS);
116: }
118: /* Merge the "A, B" matrices of mat into a matrix C. mat's type is MPIAIJKOKKOS. C's type is MATSEQAIJKOKKOS.
119: A is put before B. C's size would be A->rmap->n by (A->cmap->n + B->cmap->n).
120: C still uses local column ids. Their corresponding global column ids are returned in glob.
121: */
122: static PetscErrorCode MatMPIAIJGetLocalMatMerge_MPIAIJKokkos(Mat mat, MatReuse reuse, IS *glob, Mat *C)
123: {
124: Mat Ad, Ao;
125: const PetscInt *cmap;
127: PetscFunctionBegin;
128: PetscCall(MatMPIAIJGetSeqAIJ(mat, &Ad, &Ao, &cmap));
129: PetscCall(MatSeqAIJKokkosMergeMats(Ad, Ao, reuse, C));
130: if (glob) {
131: PetscInt cst, i, dn, on, *gidx;
132: PetscCall(MatGetLocalSize(Ad, NULL, &dn));
133: PetscCall(MatGetLocalSize(Ao, NULL, &on));
134: PetscCall(MatGetOwnershipRangeColumn(mat, &cst, NULL));
135: PetscCall(PetscMalloc1(dn + on, &gidx));
136: for (i = 0; i < dn; i++) gidx[i] = cst + i;
137: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
138: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
139: }
140: PetscFunctionReturn(PETSC_SUCCESS);
141: }
143: /* Structs used in matrix products of type C=AB, C=A^tB and C=B^tAB */
144: struct MatMatStruct {
145: PetscInt n, *garray; // C's garray and its size.
146: KokkosCsrMatrix Cd, Co; // C is in split form matrices (all in local column indcies)
147: KokkosCsrMatrix C1, C2, C3, C4; // intermediate mat products
148: KokkosCsrMatrix C2_mid, C4_mid; // alias of C2, C4; share their a[], i[], but with different j[] (hence column size)
149: PetscIntKokkosView E_NzLeft;
150: PetscSF sf = nullptr; // SF to bcast or reduce matrices E to F
151: MatScalarKokkosView rootBuf, leafBuf;
152: KokkosCsrMatrix Fd, Fo; // F in split form
154: KernelHandle kh1; // compute C1, add C1+C3 or C1+Fd
155: KernelHandle kh2; // compute C2, add C2+C4 or C2+Fo
156: KernelHandle kh3; // compute C3
157: KernelHandle kh4; // compute C4
159: PetscInt E_TeamSize; // kernel launching parameters in merging E or splitting F
160: PetscInt E_VectorLength;
161: PetscInt E_RowsPerTeam;
162: PetscInt F_TeamSize;
163: PetscInt F_VectorLength;
164: PetscInt F_RowsPerTeam;
166: ~MatMatStruct()
167: {
168: PetscFunctionBegin;
169: PetscCallAbort(PETSC_COMM_SELF, PetscSFDestroy(&sf));
170: PetscFunctionReturnVoid();
171: }
172: };
174: struct MatMatStruct_AB : public MatMatStruct {
175: PetscIntKokkosView F_NzLeft; // plans to split F (in leafbuf) into Fd, Fo
176: PetscIntKokkosView irootloc; // plans to put E (i.e., Bd, Bo) into rootBuf
177: PetscIntKokkosView rowoffset;
178: };
180: struct MatMatStruct_AtB : public MatMatStruct {
181: MatColIdxKokkosView Fdjmap; // plans to reduce data in rootBuf to Fd, Fo
182: MatColIdxKokkosView Fdjperm;
183: MatColIdxKokkosView Fojmap;
184: MatColIdxKokkosView Fojperm;
185: };
187: struct MatProductData_MPIAIJKokkos {
188: MatMatStruct_AB *mmAB = nullptr;
189: MatMatStruct_AtB *mmAtB = nullptr;
190: PetscBool reusesym = PETSC_FALSE;
191: Mat Z = nullptr; // store Z=AB in computing BtAB
193: ~MatProductData_MPIAIJKokkos()
194: {
195: delete mmAB;
196: delete mmAtB;
197: PetscCallAbort(PETSC_COMM_SELF, MatDestroy(&Z));
198: }
199: };
201: static PetscErrorCode MatProductDataDestroy_MPIAIJKokkos(void *data)
202: {
203: PetscFunctionBegin;
204: PetscCallCXX(delete static_cast<MatProductData_MPIAIJKokkos *>(data));
205: PetscFunctionReturn(PETSC_SUCCESS);
206: }
208: /* MatSetMPIAIJKokkosWithSplitSeqAIJKokkosMatrices - Set the diag and offdiag matrices of a MATMPIAIJKOKKOS matrix.
209: It is similar to MatCreateMPIAIJWithSplitArrays.
211: Input Parameters:
212: + mat - the MATMPIAIJKOKKOS matrix, which should have its type and layout set, but should not have its diag, offdiag matrices set
213: . A - the diag matrix using local col ids
214: - B - the offdiag matrix using global col ids
216: Output Parameter:
217: . mat - the updated MATMPIAIJKOKKOS matrix
218: */
219: static PetscErrorCode MatSetMPIAIJKokkosWithSplitSeqAIJKokkosMatrices(Mat mat, Mat A, Mat B, PetscInt *garray)
220: {
221: Mat_MPIAIJ *mpiaij = static_cast<Mat_MPIAIJ *>(mat->data);
222: PetscInt m, n, M, N, Am, An, Bm, Bn;
224: PetscFunctionBegin;
225: PetscCall(MatGetSize(mat, &M, &N));
226: PetscCall(MatGetLocalSize(mat, &m, &n));
227: PetscCall(MatGetLocalSize(A, &Am, &An));
228: PetscCall(MatGetLocalSize(B, &Bm, &Bn));
230: PetscCheck(m == Am && m == Bm, PETSC_COMM_SELF, PETSC_ERR_PLIB, "local number of rows do not match");
231: PetscCheck(n == An, PETSC_COMM_SELF, PETSC_ERR_PLIB, "local number of columns do not match");
232: // PetscCheck(N == Bn, PETSC_COMM_SELF, PETSC_ERR_PLIB, "global number of columns do not match");
233: PetscCheck(!mpiaij->A && !mpiaij->B, PETSC_COMM_SELF, PETSC_ERR_PLIB, "A, B of the MPIAIJ matrix are not empty");
234: mpiaij->A = A;
235: mpiaij->B = B;
236: mpiaij->garray = garray;
238: mat->preallocated = PETSC_TRUE;
239: mat->nooffprocentries = PETSC_TRUE; /* See MatAssemblyBegin_MPIAIJ. In effect, making MatAssemblyBegin a nop */
241: PetscCall(MatSetOption(mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
242: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
243: /* MatAssemblyEnd is critical here. It sets mat->offloadmask according to A and B's, and
244: also gets mpiaij->B compacted, with its col ids and size reduced
245: */
246: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
247: PetscCall(MatSetOption(mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
248: PetscCall(MatSetOption(mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
249: PetscFunctionReturn(PETSC_SUCCESS);
250: }
252: // Adapted from Kokkos-Kernels spmv_launch_parameters(), to get parameters in Kokkos nested loops which we used to merge or
253: // split csr matrices. The rule is to have "vector_length * team_size" be around 256 on GPUs (e.g., for a CUDA thread block)
254: template <class ExecutionSpace>
255: static PetscErrorCode MatMergeGetLaunchParameters(PetscInt numRows, PetscInt nnz, PetscInt rows_per_thread, PetscInt &team_size, PetscInt &vector_length, PetscInt &rows_per_team)
256: {
257: Kokkos::TeamPolicy<ExecutionSpace> teamPolicy(128, Kokkos::AUTO);
259: PetscFunctionBegin;
260: PetscInt nnz_per_row = numRows ? (nnz / numRows) : 0; // we might meet empty matrices
262: if (nnz_per_row < 1) nnz_per_row = 1;
264: int max_vector_length = teamPolicy.vector_length_max();
266: if (vector_length < 1) {
267: vector_length = 1;
268: while (vector_length < max_vector_length && vector_length * 6 < nnz_per_row) vector_length *= 2;
269: }
271: // Determine rows per thread
272: if (rows_per_thread < 1) {
273: if (KokkosKernels::Impl::kk_is_gpu_exec_space<ExecutionSpace>()) rows_per_thread = 1;
274: else {
275: if (nnz_per_row < 20 && nnz > 5000000) {
276: rows_per_thread = 256;
277: } else rows_per_thread = 64;
278: }
279: }
281: if (team_size < 1) {
282: if (KokkosKernels::Impl::kk_is_gpu_exec_space<ExecutionSpace>()) {
283: team_size = 256 / vector_length;
284: } else {
285: team_size = 1;
286: }
287: }
289: rows_per_team = rows_per_thread * team_size;
291: if (rows_per_team < 0) {
292: PetscInt nnz_per_team = 4096;
293: PetscInt conc = ExecutionSpace().concurrency();
294: while ((conc * nnz_per_team * 4 > nnz) && (nnz_per_team > 256)) nnz_per_team /= 2;
295: rows_per_team = (nnz_per_team + nnz_per_row - 1) / nnz_per_row;
296: }
297: PetscFunctionReturn(PETSC_SUCCESS);
298: }
300: /*
301: Reduce two sets of global indices into local ones
303: Input Parameters:
304: + n1 - size of garray1[], the first set
305: . garray1[n1] - a sorted global index array (without duplicates)
306: . m - size of indices[], the second set
307: - indices[m] - a unsorted global index array (might have duplicates), which will be updated on output into local ones
309: Output Parameters:
310: + n2 - size of garray2[], the merged set, which combines garray1[] and indices[]
311: . garray2[n2] - allocated by callee using PetscMalloc1(). Contains sorted unique global indices (without duplicates). Caller needs to free it.
312: . map[n1] - allocated by caller. It gives garray1[i] = garray2[map[i]]
313: - indices[m] - on output, global indices in this array are rewritten with local ones, i.e, indices_input[i] = garray2[indices_output[i]]
315: Example, say
316: n1 = 5
317: garray1[5] = {1, 4, 7, 8, 10}
318: m = 4
319: indices[4] = {2, 4, 8, 9}
321: Combining them together, we have 7 global indices in garray2[]
322: n2 = 7
323: garray2[7] = {1, 2, 4, 7, 8, 9, 10}
325: And we have map[] to connect "garray1[i] = garray2[map[i]], i=[0,n1)"
326: map[5] = {0, 2, 3, 4, 6}
328: On output, indices[] is updated with local indices
329: indices[4] = {1, 2, 4, 5}
330: */
331: static PetscErrorCode ReduceTwoSetsOfGlobalIndices(PetscInt n1, const PetscInt *garray1, PetscInt m, PetscInt *indices, PetscInt *n2_, PetscInt **garray2_, PetscInt *map)
332: {
333: PetscHMapI g2l = nullptr;
334: PetscHashIter iter;
335: PetscInt tot, key, val; // total unique global indices. key is global id; val is local id
336: PetscInt n2, *garray2;
338: PetscFunctionBegin;
339: tot = 0;
340: PetscCall(PetscHMapICreateWithSize(n1, &g2l));
341: for (PetscInt i = 0; i < m; i++) { // insert those in indices[]
342: PetscCall(PetscHMapIGetWithDefault(g2l, indices[i], -1, &val)); // if not exist, val is set with -1
343: if (val < 0) PetscCall(PetscHMapISet(g2l, indices[i], tot++)); // val < 0 means gid is not in the hash table yet
344: }
346: for (PetscInt i = 0; i < n1; i++) { // insert those in garray1[]
347: PetscCall(PetscHMapIGetWithDefault(g2l, garray1[i], -1, &val));
348: if (val < 0) PetscCall(PetscHMapISet(g2l, garray1[i], tot++));
349: }
351: // Pull out (unique) globals in the hash table and put them in garray2[]
352: n2 = tot;
353: PetscCall(PetscMalloc1(n2, &garray2));
354: tot = 0;
355: PetscHashIterBegin(g2l, iter);
356: while (!PetscHashIterAtEnd(g2l, iter)) {
357: PetscHashIterGetKey(g2l, iter, key);
358: PetscHashIterNext(g2l, iter);
359: garray2[tot++] = key;
360: }
362: // Sort garray2[] and then map them to local indices starting from 0
363: PetscCall(PetscSortInt(n2, garray2));
364: PetscCall(PetscHMapIClear(g2l));
365: for (PetscInt i = 0; i < tot; i++) PetscCall(PetscHMapISet(g2l, garray2[i], i)); // i is the local id
367: // Rewrite indices[] with local indices
368: for (PetscInt i = 0; i < m; i++) {
369: PetscCall(PetscHMapIGetWithDefault(g2l, indices[i], -1, &val));
370: PetscAssert(val >= 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Met a negative local column index");
371: indices[i] = val;
372: }
373: // Record the map that maps garray1[i] to garray2[map[i]]
374: for (PetscInt i = 0; i < n1; i++) PetscCall(PetscHMapIGetWithDefault(g2l, garray1[i], -1, &map[i]));
375: PetscCall(PetscHMapIDestroy(&g2l));
376: *n2_ = n2;
377: *garray2_ = garray2;
378: PetscFunctionReturn(PETSC_SUCCESS);
379: }
381: /*
382: MatMPIAIJKokkosReduce - Reduce rows of a MPIAIJKOKKOS matrix (E, in split form) to produce another matrix (F, also in split form, stored in mm)
384: It is the reverse of MatMPIAIJKokkosBcast() in some sense, but with a different signature since we do not really need a fully populated MPIAIJKOKKOS E.
386: Think each row of E as a leaf, then the given ownerSF specifies roots for the leaves. Roots may connect to multiple leaves.
387: In this routine, we sparse-merge leaves (rows) at their roots to form potentially longer rows in F. F's number of rows will be nroots of ownerSF.
389: Input Parameters:
390: + comm - MPI communicator of E
391: . A - diag block of E, using local column indices
392: . B - off-diag block of E, using local column indices
393: . cstart - (global) start column of Ed
394: . cend - (global) end column + 1 of Ed. In other words, E's column ownership is in range of [cstart, cend)
395: . garray1[n1] - global column indices of Eo. Here n1 is Eo's column size.
396: . ownerSF - the SF specifies ownership (root) of rows in E
397: . reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
398: - mm - to stash intermediate data structures for reuse
400: Output Parameters:
401: + map[n1] - allocated by caller. It maps garray1[] to garray2[]. See more at ReduceTwoSetsOfGlobalIndices().
402: - mm - contains various info, such as garray2[], F (Fd, Fo) etc.
404: Notes:
405: When reuse = MAT_REUSE_MATRIX, cstart, cend, garray1, ownerSF, map are not significant.
407: */
408: static PetscErrorCode MatMPIAIJKokkosReduceBegin(MPI_Comm comm, KokkosCsrMatrix A, KokkosCsrMatrix B, PetscInt cstart, PetscInt cend, const PetscInt *garray1, PetscSF ownerSF, MatReuse reuse, PetscInt *map, MatMatStruct_AtB *mm)
409: {
410: PetscFunctionBegin;
411: if (reuse == MAT_INITIAL_MATRIX) {
412: PetscInt Em = A.numRows(), Fm;
413: PetscInt n1 = B.numCols();
415: PetscCall(PetscSFGetGraph(ownerSF, &Fm, NULL, NULL, NULL)); // Fm = #rows of F = nroots of ownerSF
417: // Do the analysis on host
418: auto Ai_h = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), A.graph.row_map);
419: auto Aj_h = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), A.graph.entries);
420: auto Bi_h = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), B.graph.row_map);
421: auto Bj_h = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), B.graph.entries);
422: const MatRowMapType *Ai = Ai_h.data(), *Bi = Bi_h.data();
423: const MatColIdxType *Aj = Aj_h.data(), *Bj = Bj_h.data();
425: // Count how many nonzeros of each row in E are in the left of the diag block [cstart,cend)
426: PetscIntKokkosViewHost E_NzLeft_h(NoInit("E_NzLeft_h"), Em), E_RowLen_h(NoInit("E_RowLen_h"), Em);
427: PetscInt *E_NzLeft = E_NzLeft_h.data(), *E_RowLen = E_RowLen_h.data();
428: for (PetscInt i = 0; i < Em; i++) {
429: const PetscInt *first, *last, *it;
430: PetscInt count, step;
431: // std::lower_bound(first,last,cstart), but need to use global column indices
432: first = Bj + Bi[i];
433: last = Bj + Bi[i + 1];
434: count = last - first;
435: while (count > 0) {
436: it = first;
437: step = count / 2;
438: it += step;
439: if (garray1[*it] < cstart) { // map local to global
440: first = ++it;
441: count -= step + 1;
442: } else count = step;
443: }
444: E_NzLeft[i] = first - (Bj + Bi[i]);
445: E_RowLen[i] = (Ai[i + 1] - Ai[i]) + (Bi[i + 1] - Bi[i]);
446: }
448: // Get length of rows (i.e., sizes of leaves) that contribute to my roots
449: const PetscMPIInt *iranks, *ranks;
450: const PetscInt *ioffset, *irootloc, *roffset, *rmine;
451: PetscInt niranks, nranks;
452: MPI_Request *reqs;
453: PetscMPIInt tag;
454: PetscSF reduceSF;
455: PetscInt *sdisp, *rdisp;
457: PetscCall(PetscCommGetNewTag(comm, &tag));
458: PetscCall(PetscSFGetLeafRanks(ownerSF, &niranks, &iranks, &ioffset, &irootloc)); // get leaf ranks connecting to roots on this process (I'll recv from them)
459: PetscCall(PetscSFGetRootRanks(ownerSF, &nranks, &ranks, &roffset, &rmine, NULL)); // get root ranks this process connects (I'll send to them)
461: // Find out length of each row I will receive. Even for the same row index, when they are from
462: // different senders, they might have different lengths (and sparsity patterns)
463: PetscInt sendRowCnt = roffset[nranks], recvRowCnt = ioffset[niranks];
464: PetscInt *sendRowLen, *recvRowLen; // lengths of rows of I need to send/recv per process
466: PetscCall(PetscMalloc5(sendRowCnt, &sendRowLen, recvRowCnt + 1, &recvRowLen, nranks, &sdisp, niranks + 1, &rdisp, nranks + niranks, &reqs));
468: for (PetscInt i = 0; i < sendRowCnt; i++) sendRowLen[i] = E_RowLen[rmine[i]];
469: recvRowLen[0] = 0; // since we will make it in CSR format later
470: recvRowLen++; // advance the pointer now
471: for (PetscInt i = 0; i < niranks; i++) { MPI_Irecv(&recvRowLen[ioffset[i]], ioffset[i + 1] - ioffset[i], MPIU_INT, iranks[i], tag, comm, &reqs[nranks + i]); }
472: for (PetscInt i = 0; i < nranks; i++) { MPI_Isend(&sendRowLen[roffset[i]], roffset[i + 1] - roffset[i], MPIU_INT, ranks[i], tag, comm, &reqs[i]); }
473: PetscCallMPI(MPI_Waitall(nranks + niranks, reqs, MPI_STATUSES_IGNORE));
475: // Build the real PetscSF for reducing E rows (buffer to buffer)
476: rdisp[0] = 0;
477: for (PetscInt i = 0; i < niranks; i++) {
478: rdisp[i + 1] = rdisp[i];
479: for (PetscInt j = ioffset[i]; j < ioffset[i + 1]; j++) { rdisp[i + 1] += recvRowLen[j]; }
480: }
481: recvRowLen--; // put it back into csr format
482: for (PetscInt i = 0; i < recvRowCnt; i++) recvRowLen[i + 1] += recvRowLen[i];
484: for (PetscInt i = 0; i < nranks; i++) { MPI_Irecv(&sdisp[i], 1, MPIU_INT, ranks[i], tag, comm, &reqs[i]); }
485: for (PetscInt i = 0; i < niranks; i++) { MPI_Isend(&rdisp[i], 1, MPIU_INT, iranks[i], tag, comm, &reqs[nranks + i]); }
486: PetscCallMPI(MPI_Waitall(nranks + niranks, reqs, MPI_STATUSES_IGNORE));
488: PetscInt nleaves = 0, Enz = 0; // leaves are nonzeros I will send
489: PetscInt nroots = rdisp[niranks]; // roots are nonzeros I will recv
490: PetscSFNode *iremote;
492: for (PetscInt i = 0; i < Em; i++) Enz += E_RowLen[i];
493: PetscAssert(A.nnz() + B.nnz() == Enz, comm, PETSC_ERR_PLIB, "Enz should be equal to sum of nnz of A and B");
494: PetscCallMPI(PetscMalloc1(Enz, &iremote)); // no free, since we give ownership to reduceSF
496: for (PetscInt i = 0; i < nranks; i++) {
497: PetscInt count = 0;
498: for (PetscInt j = roffset[i]; j < roffset[i + 1]; j++) count += E_RowLen[rmine[j]];
499: for (PetscInt j = 0; j < count; j++) {
500: iremote[nleaves + j].rank = ranks[i];
501: iremote[nleaves + j].index = sdisp[i] + j;
502: }
503: nleaves += count;
504: }
505: PetscCheck(nleaves == Enz, comm, PETSC_ERR_PLIB, "nleaves should be equal to Enz");
507: PetscCall(PetscSFCreate(comm, &reduceSF));
508: PetscCall(PetscSFSetGraph(reduceSF, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
510: // Copy (global) column indices of the needed rows in E to sendCol[], and then PetscSFReduce to recvCol[]
511: PetscInt *sendCol, *recvCol;
512: PetscCall(PetscMalloc2(nleaves, &sendCol, nroots, &recvCol));
513: for (PetscInt k = 0; k < roffset[nranks]; k++) {
514: PetscInt i = rmine[k]; // row to be copied
515: PetscInt *buf = &sendCol[Ai[i] + Bi[i]];
516: PetscInt nzLeft = E_NzLeft[i];
517: PetscInt alen = Ai[i + 1] - Ai[i], blen = Bi[i + 1] - Bi[i];
518: for (PetscInt j = 0; j < alen + blen; j++) {
519: if (j < nzLeft) {
520: buf[j] = garray1[Bj[Bi[i] + j]]; // left B, in global
521: } else if (j < nzLeft + alen) {
522: buf[j] = Aj[Ai[i] + j - nzLeft] + cstart; // diag A, also in global
523: } else {
524: buf[j] = garray1[Bj[Bi[i] + j - alen]]; // right B, in global
525: }
526: }
527: }
528: PetscCall(PetscSFReduceWithMemTypeBegin(reduceSF, MPIU_INT, PETSC_MEMTYPE_HOST, sendCol, PETSC_MEMTYPE_HOST, recvCol, MPI_REPLACE));
529: PetscCall(PetscSFReduceEnd(reduceSF, MPIU_INT, sendCol, recvCol, MPI_REPLACE));
531: // With recvCol[], we do a series of analysis to get i, j of Fd, Fo, and build plans to reduce nonzeros in recv buffers to Fd and Fo
532: PetscInt *recvRowPerm, *recvColSorted;
533: PetscInt *recvNzPerm, *recvNzPermSorted;
534: PetscCall(PetscMalloc4(recvRowCnt, &recvRowPerm, nroots, &recvColSorted, nroots, &recvNzPerm, nroots, &recvNzPermSorted));
536: for (PetscInt i = 0; i < nroots; i++) recvNzPerm[i] = i; // numbering all received nonzeros
537: for (PetscInt i = 0; i < recvRowCnt; i++) recvRowPerm[i] = i; // put up a permutation array, so that after sorting we know where to get a row in recvCol[]
538: PetscCall(PetscSortIntWithPermutation(recvRowCnt, irootloc, recvRowPerm)); // irootloc[] (owned by ownerSF) won't be changed
540: // i[] array, nz are always easiest to compute
541: MatRowMapKokkosViewHost Fdi_h(NoInit("Fdi_h"), Fm + 1), Foi_h(NoInit("Foi_h"), Fm + 1);
542: MatRowMapType *Fdi, *Foi;
543: PetscInt FnzDups = 0, Fdnz = 0, FdnzDups = 0, Fonz = 0, FonzDups = 0; // nz (with or without dups) in F, Fd, Fo
544: PetscInt iter;
546: Kokkos::deep_copy(Fdi_h, 0); // zero, as we will do 'val++' on them
547: Kokkos::deep_copy(Foi_h, 0);
548: Fdi = Fdi_h.data() + 1; // +1 for easy indexing in code below
549: Foi = Foi_h.data() + 1;
550: iter = 0;
551: while (iter < recvRowCnt) { // iter over received rows
552: PetscInt curRowIdx = irootloc[recvRowPerm[iter]];
553: PetscInt dupRows = 1; // current row has this many contributing rows (of various sparsity patterns)
555: while (iter + dupRows < recvRowCnt && irootloc[recvRowPerm[iter + dupRows]] == curRowIdx) dupRows++;
557: // Copy column indices (and their permutation) of these rows into recvColSorted & recvNzPermSorted
558: PetscInt nz = 0; // nz (with dups) in the current row
559: PetscInt *jbuf = recvColSorted + FnzDups;
560: PetscInt *pbuf = recvNzPermSorted + FnzDups;
561: PetscInt *jbuf2 = jbuf; // temp pointers
562: PetscInt *pbuf2 = pbuf;
563: for (PetscInt d = 0; d < dupRows; d++) {
564: PetscInt i = recvRowPerm[iter + d];
565: PetscInt len = recvRowLen[i + 1] - recvRowLen[i];
566: PetscCall(PetscArraycpy(jbuf2, &recvCol[recvRowLen[i]], len));
567: PetscCall(PetscArraycpy(pbuf2, &recvNzPerm[recvRowLen[i]], len));
568: jbuf2 += len;
569: pbuf2 += len;
570: nz += len;
571: }
572: PetscCall(PetscIntSortSemiOrderedWithArray(nz, jbuf, pbuf)); // It could be improved with k-way merge sort, since the rows are already sorted
574: // Scan column indices (in jbuf[0,nz), might have dups) of this row, and see how many go to Fd and how many go to Fo
575: PetscInt cur = 0;
576: while (cur < nz) {
577: PetscInt curColIdx = jbuf[cur];
578: PetscInt dups = 1;
580: while (cur + dups < nz && jbuf[cur + dups] == curColIdx) dups++;
581: if (curColIdx >= cstart && curColIdx < cend) {
582: Fdi[curRowIdx]++;
583: FdnzDups += dups;
584: } else {
585: Foi[curRowIdx]++;
586: FonzDups += dups;
587: }
588: cur += dups;
589: }
591: FnzDups += nz;
592: iter += dupRows; // Move to next unique row
593: }
595: Fdi = Fdi_h.data(); // restore Fdi, Foi and make them CSR
596: Foi = Foi_h.data();
597: for (PetscInt i = 0; i < Fm; i++) {
598: Fdi[i + 1] += Fdi[i];
599: Foi[i + 1] += Foi[i];
600: }
601: Fdnz = Fdi[Fm];
602: Fonz = Foi[Fm];
603: PetscCall(PetscFree2(sendCol, recvCol));
605: // Allocate j, jmap, jperm for Fd and Fo
606: MatColIdxKokkosViewHost Fdj_h(NoInit("Fdj_h"), Fdnz), Foj_h(NoInit("Foj_h"), Fonz);
607: MatRowMapKokkosViewHost Fdjmap_h(NoInit("Fdjmap_h"), Fdnz + 1), Fojmap_h(NoInit("Fojmap_h"), Fonz + 1); // +1 to make csr
608: MatRowMapKokkosViewHost Fdjperm_h(NoInit("Fdjperm_h"), FdnzDups), Fojperm_h(NoInit("Fojperm_h"), FonzDups);
609: MatColIdxType *Fdj = Fdj_h.data(), *Foj = Foj_h.data();
610: MatRowMapType *Fdjmap = Fdjmap_h.data(), *Fojmap = Fojmap_h.data();
611: MatRowMapType *Fdjperm = Fdjperm_h.data(), *Fojperm = Fojperm_h.data();
613: // Scan recvColSorted[] again, and fill j, jmap, jperm for Fd and Fo
614: Fdjmap[0] = 0;
615: Fojmap[0] = 0;
616: FnzDups = 0;
617: Fdnz = 0;
618: Fonz = 0;
619: iter = 0; // iter over received rows
620: while (iter < recvRowCnt) {
621: PetscInt curRowIdx = irootloc[recvRowPerm[iter]]; // current row idx
622: PetscInt dupRows = 1; // It has this many contributing rows (of various lengths)
623: PetscInt nz = 0; // nz (with dups) in the current row
625: while (iter + dupRows < recvRowCnt && irootloc[recvRowPerm[iter + dupRows]] == curRowIdx) dupRows++;
626: for (PetscInt d = 0; d < dupRows; d++) {
627: PetscInt i = recvRowPerm[iter + d];
628: nz += recvRowLen[i + 1] - recvRowLen[i];
629: }
631: PetscInt *jbuf = recvColSorted + FnzDups;
632: // Scan columns (in jbuf[0,nz) of this row, copy them and their permutation to j[] and jperm[] of Fd and Fo
633: PetscInt cur = 0;
634: while (cur < nz) {
635: PetscInt curColIdx = jbuf[cur];
636: PetscInt dups = 1;
638: while (cur + dups < nz && jbuf[cur + dups] == curColIdx) dups++;
639: if (curColIdx >= cstart && curColIdx < cend) {
640: Fdj[Fdnz] = curColIdx - cstart; // easily convert to local
641: Fdjmap[Fdnz + 1] = Fdjmap[Fdnz] + dups;
642: for (PetscInt j = 0; j < dups; j++) Fdjperm[Fdjmap[Fdnz] + j] = recvNzPermSorted[FnzDups + j];
643: FdnzDups += dups;
644: Fdnz++;
645: } else {
646: Foj[Fonz] = curColIdx; // in global
647: Fojmap[Fonz + 1] = Fojmap[Fonz] + dups;
648: for (PetscInt j = 0; j < dups; j++) Fojperm[Fojmap[Fonz] + j] = recvNzPermSorted[FnzDups + j];
649: FonzDups += dups;
650: Fonz++;
651: }
652: cur += dups;
653: FnzDups += dups;
654: }
655: iter += dupRows; // Move to next unique row
656: }
657: PetscCall(PetscFree4(recvRowPerm, recvColSorted, recvNzPerm, recvNzPermSorted));
658: PetscCall(PetscFree5(sendRowLen, recvRowLen, sdisp, rdisp, reqs));
660: // Combine global column indices in garray1[] and Foj[]
661: PetscInt n2, *garray2;
663: PetscCall(ReduceTwoSetsOfGlobalIndices(n1, garray1, Fonz, Foj, &n2, &garray2, map));
664: mm->sf = reduceSF;
665: mm->leafBuf = MatScalarKokkosView(NoInit("leafBuf"), nleaves);
666: mm->rootBuf = MatScalarKokkosView(NoInit("rootBuf"), nroots);
667: mm->garray = garray2; // give ownership, so no free
668: mm->n = n2;
669: mm->E_NzLeft = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), E_NzLeft_h);
670: mm->Fdjmap = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdjmap_h);
671: mm->Fdjperm = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdjperm_h);
672: mm->Fojmap = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fojmap_h);
673: mm->Fojperm = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fojperm_h);
675: // Output Fd and Fo in KokkosCsrMatrix format
676: MatScalarKokkosView Fda_d(NoInit("Fda_d"), Fdnz);
677: MatRowMapKokkosView Fdi_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdi_h);
678: MatColIdxKokkosView Fdj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdj_h);
679: MatScalarKokkosView Foa_d(NoInit("Foa_d"), Fonz);
680: MatRowMapKokkosView Foi_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Foi_h);
681: MatColIdxKokkosView Foj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Foj_h);
683: PetscCallCXX(mm->Fd = KokkosCsrMatrix("Fd", Fm, cend - cstart, Fdnz, Fda_d, Fdi_d, Fdj_d));
684: PetscCallCXX(mm->Fo = KokkosCsrMatrix("Fo", Fm, n2, Fonz, Foa_d, Foi_d, Foj_d)); // Fo's column size is n2, length of garray2[]
686: // Compute kernel launch parameters in merging E
687: PetscInt teamSize, vectorLength, rowsPerTeam;
689: teamSize = vectorLength = rowsPerTeam = -1;
690: PetscCall(MatMergeGetLaunchParameters<DefaultExecutionSpace>(Em, Enz, -1, teamSize, vectorLength, rowsPerTeam));
691: mm->E_TeamSize = teamSize;
692: mm->E_VectorLength = vectorLength;
693: mm->E_RowsPerTeam = rowsPerTeam;
694: } else PetscCheck(reuse == MAT_REUSE_MATRIX, comm, PETSC_ERR_PLIB, "Unsupported MatReuse enum %d", reuse);
696: // Handy aliases
697: auto &Aa = A.values;
698: auto &Ba = B.values;
699: const auto &Ai = A.graph.row_map;
700: const auto &Bi = B.graph.row_map;
701: const auto &E_NzLeft = mm->E_NzLeft;
702: auto &leafBuf = mm->leafBuf;
703: auto &rootBuf = mm->rootBuf;
704: PetscSF reduceSF = mm->sf;
705: PetscInt Em = A.numRows();
706: PetscInt teamSize = mm->E_TeamSize;
707: PetscInt vectorLength = mm->E_VectorLength;
708: PetscInt rowsPerTeam = mm->E_RowsPerTeam;
709: PetscInt workSets = (Em + rowsPerTeam - 1) / rowsPerTeam;
711: // Copy rows in A/B of E to leafBuf, then pass it to rootBuf
712: PetscCallCXX(Kokkos::parallel_for(
713: Kokkos::TeamPolicy<>(workSets, teamSize, vectorLength), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
714: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, 0, rowsPerTeam), [&](PetscInt k) {
715: PetscInt i = t.league_rank() * rowsPerTeam + k; // i-th row in F
716: if (i < Em) {
717: PetscInt disp = Ai(i) + Bi(i);
718: PetscInt alen = Ai(i + 1) - Ai(i);
719: PetscInt blen = Bi(i + 1) - Bi(i);
720: PetscInt nzleft = E_NzLeft(i);
722: Kokkos::parallel_for(Kokkos::ThreadVectorRange(t, alen + blen), [&](PetscInt j) {
723: MatScalar &val = leafBuf(disp + j);
724: if (j < nzleft) { // B left
725: val = Ba(Bi(i) + j);
726: } else if (j < nzleft + alen) { // diag A
727: val = Aa(Ai(i) + j - nzleft);
728: } else { // B right
729: val = Ba(Bi(i) + j - alen);
730: }
731: });
732: }
733: });
734: }));
735: PetscCall(PetscSFReduceWithMemTypeBegin(reduceSF, MPIU_SCALAR, PETSC_MEMTYPE_KOKKOS, leafBuf.data(), PETSC_MEMTYPE_KOKKOS, rootBuf.data(), MPI_REPLACE));
736: PetscFunctionReturn(PETSC_SUCCESS);
737: }
739: // To finish MatMPIAIJKokkosReduce.
740: static PetscErrorCode MatMPIAIJKokkosReduceEnd(MPI_Comm comm, KokkosCsrMatrix A, KokkosCsrMatrix B, PetscInt cstart, PetscInt cend, const PetscInt *garray1, PetscSF ownerSF, MatReuse reuse, PetscInt *map, MatMatStruct_AtB *mm)
741: {
742: PetscFunctionBegin;
743: auto &leafBuf = mm->leafBuf;
744: auto &rootBuf = mm->rootBuf;
745: auto &Fda = mm->Fd.values;
746: const auto &Fdjmap = mm->Fdjmap;
747: const auto &Fdjperm = mm->Fdjperm;
748: auto Fdnz = mm->Fd.nnz();
749: auto &Foa = mm->Fo.values;
750: const auto &Fojmap = mm->Fojmap;
751: const auto &Fojperm = mm->Fojperm;
752: auto Fonz = mm->Fo.nnz();
753: PetscSF reduceSF = mm->sf;
755: PetscCall(PetscSFReduceEnd(reduceSF, MPIU_SCALAR, leafBuf.data(), rootBuf.data(), MPI_REPLACE));
757: // Reduce data in rootBuf to Fd and Fo
758: PetscCallCXX(Kokkos::parallel_for(
759: Fdnz, KOKKOS_LAMBDA(const MatRowMapType i) {
760: PetscScalar sum = 0.0;
761: for (MatRowMapType k = Fdjmap(i); k < Fdjmap(i + 1); k++) sum += rootBuf(Fdjperm(k));
762: Fda(i) = sum;
763: }));
765: PetscCallCXX(Kokkos::parallel_for(
766: Fonz, KOKKOS_LAMBDA(const MatRowMapType i) {
767: PetscScalar sum = 0.0;
768: for (MatRowMapType k = Fojmap(i); k < Fojmap(i + 1); k++) sum += rootBuf(Fojperm(k));
769: Foa(i) = sum;
770: }));
771: PetscFunctionReturn(PETSC_SUCCESS);
772: }
774: /*
775: MatMPIAIJKokkosBcast - Bcast local rows of a MPIAIJKOKKOS matrix (E) to produce a local matrix (F, stored in mm) in split form
777: This is a complex routine. It is essentially the MPIAIJKOKKOS counterpart of MatGetBrowsOfAoCols_MPIAIJ, but supports
778: device and involves various index mapping.
780: In the given ownerSF, leaves correspond to rows in F, and roots correspond to rows in E. Roots may connect to multiple leaves.
781: Suppose F's j-th row is connected to a root identified by PetscSFNode (k,i), it means we need to bcast the i-th row of E on rank k
782: to j-th row of F. ownerSF is not an arbitrary SF, instead it is the Mvctx of another MPIAIJ matrix A that is able to perform A*E.
783: F has the same column layout as E.
785: Conceptually F has global column indices. In this routine, we spit F into diagonal Fd and off-diagonal Fo.
786: Fd uses local column indices, which are easy to compute. We just need to subtract the "local column range start" from the global indices.
787: Fo had global column indices at first. We will reduce them into local ones. In doing that, we also take into account the global
788: column indices that E's off-diag block has. Let's say there are n1 such indices stored in garray1[]. We will reduce them along with
789: column indices in Fo and update Fo with local indices.
791: Input Parameters:
792: + E - the MPIAIJKOKKOS matrix
793: . ownerSF - the ownership SF (insignificant in MAT_REUSE_MATRIX)
794: . reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
795: - mm - to stash matproduct intermediate data structures
797: Output Parameters:
798: + map[n1] - allocated by caller. It maps garray1[] to garray2[]. See more at ReduceTwoSetsOfGlobalIndices.
799: - mm - contains various info, such as garray2[], Fd, Fo, etc.
801: Notes:
802: When reuse = MAT_REUSE_MATRIX, ownerSF, map are not significant.
803: The routine is provide in split-phase form MatMPIAIJKokkosBcastBegin/End() to provide computation/communication opportunities.
804: */
805: static PetscErrorCode MatMPIAIJKokkosBcastBegin(Mat E, PetscSF ownerSF, MatReuse reuse, PetscInt *map, MatMatStruct_AB *mm)
806: {
807: Mat_MPIAIJ *empi = static_cast<Mat_MPIAIJ *>(E->data);
808: Mat A = empi->A, B = empi->B; // diag and off-diag
809: Mat_SeqAIJKokkos *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr), *bkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
810: PetscInt Em = E->rmap->n; // #local rows
811: MPI_Comm comm;
813: PetscFunctionBegin;
814: PetscCallMPI(PetscObjectGetComm((PetscObject)E, &comm));
815: if (reuse == MAT_INITIAL_MATRIX) {
816: Mat_SeqAIJ *aseq = static_cast<Mat_SeqAIJ *>(A->data), *bseq = static_cast<Mat_SeqAIJ *>(B->data);
817: PetscInt n1 = B->cmap->n, *Ai = aseq->i, *Aj = aseq->j, *Bi = bseq->i, *Bj = bseq->j;
818: const PetscInt *garray1 = empi->garray; // its size is n1
819: PetscInt cstart, cend;
820: PetscSF bcastSF;
822: PetscCall(MatGetOwnershipRangeColumn(E, &cstart, &cend));
824: // Count how many nonzeros of each row in E are in the left of the diag block [cstart,cend)
825: PetscIntKokkosViewHost E_NzLeft_h(NoInit("E_NzLeft_h"), Em), E_RowLen_h(NoInit("E_RowLen_h"), Em);
826: PetscInt *E_NzLeft = E_NzLeft_h.data(), *E_RowLen = E_RowLen_h.data();
827: for (PetscInt i = 0; i < Em; i++) {
828: const PetscInt *first, *last, *it;
829: PetscInt count, step;
830: // std::lower_bound(first,last,cstart), but need to use global column indices
831: first = Bj + Bi[i];
832: last = Bj + Bi[i + 1];
833: count = last - first;
834: while (count > 0) {
835: it = first;
836: step = count / 2;
837: it += step;
838: if (empi->garray[*it] < cstart) { // map local to global
839: first = ++it;
840: count -= step + 1;
841: } else count = step;
842: }
843: E_NzLeft[i] = first - (Bj + Bi[i]);
844: E_RowLen[i] = (Ai[i + 1] - Ai[i]) + (Bi[i + 1] - Bi[i]);
845: }
847: // Compute row pointer Fi of F
848: PetscInt *Fi, Fm, Fnz;
849: PetscCall(PetscSFGetGraph(ownerSF, NULL, &Fm, NULL, NULL)); // Fm = #rows of F = nleaves of ownerSF
850: PetscCall(PetscMalloc1(Fm + 1, &Fi));
851: Fi[0] = 0;
852: PetscCall(PetscSFBcastWithMemTypeBegin(ownerSF, MPIU_INT, PETSC_MEMTYPE_HOST, E_RowLen, PETSC_MEMTYPE_HOST, &Fi[1], MPI_REPLACE));
853: PetscCall(PetscSFBcastEnd(ownerSF, MPIU_INT, E_RowLen, &Fi[1], MPI_REPLACE));
854: for (PetscInt i = 0; i < Fm; i++) Fi[i + 1] += Fi[i];
855: Fnz = Fi[Fm];
857: // Build the real PetscSF for bcasting E rows (buffer to buffer)
858: const PetscMPIInt *iranks, *ranks;
859: const PetscInt *ioffset, *irootloc, *roffset;
860: PetscInt niranks, nranks, *sdisp, *rdisp;
861: MPI_Request *reqs;
862: PetscMPIInt tag;
864: PetscCall(PetscSFGetLeafRanks(ownerSF, &niranks, &iranks, &ioffset, &irootloc)); // get leaf ranks referencing roots on this process
865: PetscCall(PetscSFGetRootRanks(ownerSF, &nranks, &ranks, &roffset, NULL, NULL)); // recv info
866: PetscCall(PetscMalloc3(niranks + 1, &sdisp, nranks, &rdisp, niranks + nranks, &reqs));
868: sdisp[0] = 0; // send displacement
869: for (PetscInt i = 0; i < niranks; i++) {
870: sdisp[i + 1] = sdisp[i];
871: for (PetscInt j = ioffset[i]; j < ioffset[i + 1]; j++) {
872: PetscInt r = irootloc[j]; // row to be sent
873: sdisp[i + 1] += E_RowLen[r];
874: }
875: }
877: PetscCallMPI(PetscCommGetNewTag(comm, &tag));
878: for (PetscInt i = 0; i < nranks; i++) PetscCallMPI(MPI_Irecv(&rdisp[i], 1, MPIU_INT, ranks[i], tag, comm, &reqs[i]));
879: for (PetscInt i = 0; i < niranks; i++) PetscCallMPI(MPI_Isend(&sdisp[i], 1, MPIU_INT, iranks[i], tag, comm, &reqs[nranks + i]));
880: PetscCallMPI(MPI_Waitall(niranks + nranks, reqs, MPI_STATUSES_IGNORE));
882: PetscInt nleaves = Fnz; // leaves are nonzeros I will receive
883: PetscInt nroots = sdisp[niranks]; // roots are nonzeros I will send
884: PetscSFNode *iremote; // give ownership to bcastSF
885: PetscCall(PetscMalloc1(nleaves, &iremote));
886: for (PetscInt i = 0; i < nranks; i++) { // for each sender rank
887: PetscInt k = 0;
888: for (PetscInt j = Fi[roffset[i]]; j < Fi[roffset[i + 1]]; j++) { // I will receive rows [roffset[i], roffset[i+1]) of F from ranks[i]
889: iremote[j].rank = ranks[i];
890: iremote[j].index = rdisp[i] + k; // their root location
891: k++;
892: }
893: }
894: PetscCall(PetscSFCreate(comm, &bcastSF));
895: PetscCall(PetscSFSetGraph(bcastSF, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
896: PetscCall(PetscFree3(sdisp, rdisp, reqs));
898: // Build a plan (rowoffset, irootloc, E_NzLeft) to copy rows in E to rootdata of bcastSF in parallel
899: PetscIntKokkosViewHost rowoffset_h(NoInit("rowoffset_h"), ioffset[niranks] + 1);
900: PetscInt *rowoffset = rowoffset_h.data(); // for each entry (row) indicated in irootloc[], we calculate its destinate offset in copying
901: rowoffset[0] = 0;
902: for (PetscInt i = 0; i < ioffset[niranks]; i++) { rowoffset[i + 1] = rowoffset[i] + E_RowLen[irootloc[i]]; }
904: // Copy (global) column indices of the needed rows in E to a buffer, and then bcast to Fj[]
905: PetscInt *jbuf, *Fj;
906: PetscCall(PetscMalloc2(nroots, &jbuf, Fnz, &Fj));
907: for (PetscInt k = 0; k < ioffset[niranks]; k++) {
908: PetscInt i = irootloc[k]; // row to be copied
909: PetscInt *buf = &jbuf[rowoffset[k]];
910: PetscInt nzLeft = E_NzLeft[i];
911: PetscInt alen = Ai[i + 1] - Ai[i], blen = Bi[i + 1] - Bi[i];
912: for (PetscInt j = 0; j < alen + blen; j++) {
913: if (j < nzLeft) {
914: buf[j] = empi->garray[Bj[Bi[i] + j]]; // left B, in global
915: } else if (j < nzLeft + alen) {
916: buf[j] = Aj[Ai[i] + j - nzLeft] + cstart; // diag A, also in global
917: } else {
918: buf[j] = empi->garray[Bj[Bi[i] + j - alen]]; // right B, in global
919: }
920: }
921: }
922: PetscCall(PetscSFBcastWithMemTypeBegin(bcastSF, MPIU_INT, PETSC_MEMTYPE_HOST, jbuf, PETSC_MEMTYPE_HOST, Fj, MPI_REPLACE));
923: PetscCall(PetscSFBcastEnd(bcastSF, MPIU_INT, jbuf, Fj, MPI_REPLACE));
925: // Build a plan (i.e., F_NzLeft) to split F into Fd and Fo
926: MatRowMapKokkosViewHost Fdi_h(NoInit("Fdi_h"), Fm + 1), Foi_h(NoInit("Foi_h"), Fm + 1); // row pointer of Fd, Fo
927: MatColIdxKokkosViewHost F_NzLeft_h(NoInit("F_NzLeft_h"), Fm); // split each row of F into Left, Diag, Right. We only need to record #nz in Left and Diag.
928: MatRowMapType *Fdi = Fdi_h.data(), *Foi = Foi_h.data();
929: MatColIdxType *F_NzLeft = F_NzLeft_h.data();
931: Fdi[0] = Foi[0] = 0;
932: for (PetscInt i = 0; i < Fm; i++) {
933: PetscInt *first, *last, *lb1, *lb2;
934: // cut the row into: Left, [cstart, cend), Right
935: first = Fj + Fi[i];
936: last = Fj + Fi[i + 1];
937: lb1 = std::lower_bound(first, last, cstart);
938: F_NzLeft[i] = lb1 - first;
939: lb2 = std::lower_bound(first, last, cend);
940: Fdi[i + 1] = lb2 - lb1; // row i length in Fdi
941: Foi[i + 1] = (Fi[i + 1] - Fi[i]) - Fdi[i + 1]; // row i length in Foi
942: }
943: for (PetscInt i = 0; i < Fm; i++) {
944: Fdi[i + 1] += Fdi[i];
945: Foi[i + 1] += Foi[i];
946: }
948: // Fill Fdj[] and Foj[], i.e., columns of Fd and Fo. Fdj[] are local, but Foj[] are not yet.
949: PetscInt Fdnz = Fdi[Fm], Fonz = Foi[Fm];
950: MatColIdxKokkosViewHost Fdj_h(NoInit("Fdj_h"), Fdnz), Foj_h(NoInit("Foj_h"), Fonz);
951: MatColIdxType *Fdj = Fdj_h.data(), *Foj = Foj_h.data(), gid;
953: for (PetscInt i = 0; i < Fm; i++) {
954: PetscInt nzLeft = F_NzLeft[i];
955: PetscInt len = Fdi[i + 1] - Fdi[i]; // diag row len
956: for (PetscInt j = 0; j < Fi[i + 1] - Fi[i]; j++) {
957: gid = Fj[Fi[i] + j];
958: if (j < nzLeft) { // left, in global
959: Foj[Foi[i] + j] = gid;
960: } else if (j < nzLeft + len) { // diag, in local
961: Fdj[Fdi[i] + j - nzLeft] = gid - cstart;
962: } else { // right, in global
963: Foj[Foi[i] + j - len] = gid;
964: }
965: }
966: }
967: PetscCall(PetscFree2(jbuf, Fj));
968: PetscCall(PetscFree(Fi));
970: // Reduce global indices in Foj[] and garray1[] into local ones
971: PetscInt n2, *garray2;
972: PetscCall(ReduceTwoSetsOfGlobalIndices(n1, garray1, Fonz, Foj, &n2, &garray2, map));
974: // Record the plans built above, for reuse
975: PetscIntKokkosViewHost tmp(const_cast<PetscInt *>(irootloc), ioffset[niranks]); // irootloc[] is owned by ownerSF. We create a copy for safety
976: PetscIntKokkosViewHost irootloc_h(NoInit("irootloc_h"), ioffset[niranks]);
977: Kokkos::deep_copy(irootloc_h, tmp);
978: mm->sf = bcastSF;
979: mm->E_NzLeft = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), E_NzLeft_h);
980: mm->F_NzLeft = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), F_NzLeft_h);
981: mm->irootloc = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), irootloc_h);
982: mm->rowoffset = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), rowoffset_h);
983: mm->rootBuf = MatScalarKokkosView(NoInit("rootBuf"), nroots);
984: mm->leafBuf = MatScalarKokkosView(NoInit("leafBuf"), nleaves);
985: mm->garray = garray2;
986: mm->n = n2;
988: // Output Fd and Fo in KokkosCsrMatrix format
989: MatScalarKokkosView Fda_d(NoInit("Fda_d"), Fdnz), Foa_d(NoInit("Foa_d"), Fonz);
990: MatRowMapKokkosView Fdi_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdi_h);
991: MatColIdxKokkosView Fdj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdj_h);
992: MatRowMapKokkosView Foi_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Foi_h);
993: MatColIdxKokkosView Foj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Foj_h);
995: PetscCallCXX(mm->Fd = KokkosCsrMatrix("Fd", Fm, cend - cstart, Fdnz, Fda_d, Fdi_d, Fdj_d));
996: PetscCallCXX(mm->Fo = KokkosCsrMatrix("Fo", Fm, n2, Fonz, Foa_d, Foi_d, Foj_d));
998: // Compute kernel launch parameters in merging E or splitting F
999: PetscInt teamSize, vectorLength, rowsPerTeam;
1001: teamSize = vectorLength = rowsPerTeam = -1;
1002: PetscCall(MatMergeGetLaunchParameters<DefaultExecutionSpace>(mm->irootloc.extent(0), mm->rootBuf.extent(0), -1, teamSize, vectorLength, rowsPerTeam));
1003: mm->E_TeamSize = teamSize;
1004: mm->E_VectorLength = vectorLength;
1005: mm->E_RowsPerTeam = rowsPerTeam;
1007: teamSize = vectorLength = rowsPerTeam = -1;
1008: PetscCall(MatMergeGetLaunchParameters<DefaultExecutionSpace>(Fm, Fnz, -1, teamSize, vectorLength, rowsPerTeam));
1009: mm->F_TeamSize = teamSize;
1010: mm->F_VectorLength = vectorLength;
1011: mm->F_RowsPerTeam = rowsPerTeam;
1012: } else PetscCheck(reuse == MAT_REUSE_MATRIX, comm, PETSC_ERR_PLIB, "Unsupported MatReuse enum %d", reuse);
1014: // Sync E's value to device
1015: akok->a_dual.sync_device();
1016: bkok->a_dual.sync_device();
1018: // Handy aliases
1019: const auto &Aa = akok->a_dual.view_device();
1020: const auto &Ba = bkok->a_dual.view_device();
1021: const auto &Ai = akok->i_dual.view_device();
1022: const auto &Bi = bkok->i_dual.view_device();
1024: // Fetch the plans
1025: PetscIntKokkosView &E_NzLeft = mm->E_NzLeft;
1026: PetscSF &bcastSF = mm->sf;
1027: MatScalarKokkosView &rootBuf = mm->rootBuf;
1028: MatScalarKokkosView &leafBuf = mm->leafBuf;
1029: PetscIntKokkosView &irootloc = mm->irootloc;
1030: PetscIntKokkosView &rowoffset = mm->rowoffset;
1032: PetscInt teamSize = mm->E_TeamSize;
1033: PetscInt vectorLength = mm->E_VectorLength;
1034: PetscInt rowsPerTeam = mm->E_RowsPerTeam;
1035: PetscInt workSets = (irootloc.extent(0) + rowsPerTeam - 1) / rowsPerTeam;
1037: // Copy rows in A/B of E to rootBuf, then bcast it to leafBuf
1038: PetscCallCXX(Kokkos::parallel_for(
1039: Kokkos::TeamPolicy<>(workSets, teamSize, vectorLength), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
1040: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, 0, rowsPerTeam), [&](PetscInt k) {
1041: size_t r = t.league_rank() * rowsPerTeam + k; // r-th entry in irootloc[]
1042: if (r < irootloc.extent(0)) {
1043: PetscInt i = irootloc(r); // row i of E
1044: PetscInt disp = rowoffset(r);
1045: PetscInt alen = Ai(i + 1) - Ai(i);
1046: PetscInt blen = Bi(i + 1) - Bi(i);
1047: PetscInt nzleft = E_NzLeft(i);
1049: Kokkos::parallel_for(Kokkos::ThreadVectorRange(t, alen + blen), [&](PetscInt j) {
1050: if (j < nzleft) { // B left
1051: rootBuf(disp + j) = Ba(Bi(i) + j);
1052: } else if (j < nzleft + alen) { // diag A
1053: rootBuf(disp + j) = Aa(Ai(i) + j - nzleft);
1054: } else { // B right
1055: rootBuf(disp + j) = Ba(Bi(i) + j - alen);
1056: }
1057: });
1058: }
1059: });
1060: }));
1061: PetscCall(PetscSFBcastWithMemTypeBegin(bcastSF, MPIU_SCALAR, PETSC_MEMTYPE_KOKKOS, rootBuf.data(), PETSC_MEMTYPE_KOKKOS, leafBuf.data(), MPI_REPLACE));
1062: PetscFunctionReturn(PETSC_SUCCESS);
1063: }
1065: // To finish MatMPIAIJKokkosBcast.
1066: static PetscErrorCode MatMPIAIJKokkosBcastEnd(Mat E, PetscSF ownerSF, MatReuse reuse, PetscInt *map, MatMatStruct_AB *mm)
1067: {
1068: PetscFunctionBegin;
1069: const auto &Fd = mm->Fd;
1070: const auto &Fo = mm->Fo;
1071: const auto &Fdi = Fd.graph.row_map;
1072: const auto &Foi = Fo.graph.row_map;
1073: auto &Fda = Fd.values;
1074: auto &Foa = Fo.values;
1075: auto Fm = Fd.numRows();
1077: PetscIntKokkosView &F_NzLeft = mm->F_NzLeft;
1078: PetscSF &bcastSF = mm->sf;
1079: MatScalarKokkosView &rootBuf = mm->rootBuf;
1080: MatScalarKokkosView &leafBuf = mm->leafBuf;
1081: PetscInt teamSize = mm->F_TeamSize;
1082: PetscInt vectorLength = mm->F_VectorLength;
1083: PetscInt rowsPerTeam = mm->F_RowsPerTeam;
1084: PetscInt workSets = (Fm + rowsPerTeam - 1) / rowsPerTeam;
1086: PetscCall(PetscSFBcastEnd(bcastSF, MPIU_SCALAR, rootBuf.data(), leafBuf.data(), MPI_REPLACE));
1088: // Update Fda and Foa with new data in leafBuf (as if it is Fa)
1089: PetscCallCXX(Kokkos::parallel_for(
1090: Kokkos::TeamPolicy<>(workSets, teamSize, vectorLength), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
1091: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, 0, rowsPerTeam), [&](PetscInt k) {
1092: PetscInt i = t.league_rank() * rowsPerTeam + k; // i-th row in F
1093: if (i < Fm) {
1094: PetscInt nzLeft = F_NzLeft(i);
1095: PetscInt alen = Fdi(i + 1) - Fdi(i);
1096: PetscInt blen = Foi(i + 1) - Foi(i);
1097: PetscInt Fii = Fdi(i) + Foi(i);
1099: Kokkos::parallel_for(Kokkos::ThreadVectorRange(t, alen + blen), [&](PetscInt j) {
1100: PetscScalar val = leafBuf(Fii + j);
1101: if (j < nzLeft) { // left
1102: Foa(Foi(i) + j) = val;
1103: } else if (j < nzLeft + alen) { // diag
1104: Fda(Fdi(i) + j - nzLeft) = val;
1105: } else { // right
1106: Foa(Foi(i) + j - alen) = val;
1107: }
1108: });
1109: }
1110: });
1111: }));
1112: PetscFunctionReturn(PETSC_SUCCESS);
1113: }
1115: static PetscErrorCode MatProductSymbolic_MPIAIJKokkos_AtB(Mat_Product *product, Mat A, Mat B, MatMatStruct_AtB *mm)
1116: {
1117: Mat_MPIAIJ *ampi = static_cast<Mat_MPIAIJ *>(A->data);
1118: Mat_MPIAIJ *bmpi = static_cast<Mat_MPIAIJ *>(B->data);
1119: KokkosCsrMatrix Adt, Aot, Ad, Ao, Bd, Bo;
1120: PetscInt cstart, cend;
1121: MPI_Comm comm;
1123: PetscFunctionBegin;
1124: PetscCall(PetscObjectGetComm((PetscObject)B, &comm));
1125: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(ampi->A, &Adt));
1126: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(ampi->B, &Aot));
1127: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->A, &Ad));
1128: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->B, &Ao));
1129: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->A, &Bd));
1130: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->B, &Bo));
1132: // TODO: add command line options to select spgemm algorithms
1133: auto spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_DEFAULT; // default is TPL if enabled, otherwise KK
1135: // CUDA-10.2's spgemm has bugs. We prefer the SpGEMMreuse APIs introduced in cuda-11.4
1136: #if defined(KOKKOSKERNELS_ENABLE_TPL_CUSPARSE)
1137: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
1138: spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_KK;
1139: #endif
1140: #endif
1142: PetscCallCXX(mm->kh1.create_spgemm_handle(spgemm_alg));
1143: PetscCallCXX(mm->kh2.create_spgemm_handle(spgemm_alg));
1144: PetscCallCXX(mm->kh3.create_spgemm_handle(spgemm_alg));
1145: PetscCallCXX(mm->kh4.create_spgemm_handle(spgemm_alg));
1147: // Aot * (B's diag + B's off-diag)
1148: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh3, Aot, false, Bd, false, mm->C3));
1149: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh4, Aot, false, Bo, false, mm->C4));
1150: // KK spgemm_symbolic() only populates the result's row map, but not its columns.
1151: // TODO: Remove the fake spgemm_numeric() after KK fixed this problem.
1152: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh3, Aot, false, Bd, false, mm->C3));
1153: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh4, Aot, false, Bo, false, mm->C4));
1154: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
1155: PetscCallCXX(sort_crs_matrix(mm->C3));
1156: PetscCallCXX(sort_crs_matrix(mm->C4));
1157: #endif
1159: // Reduce E (i.e., C3 and C4)'s rows to form F, and overlap the communication
1160: PetscIntKokkosViewHost map_h(NoInit("map_h"), bmpi->B->cmap->n);
1161: PetscCall(MatGetOwnershipRangeColumn(B, &cstart, &cend));
1162: PetscCall(MatMPIAIJKokkosReduceBegin(comm, mm->C3, mm->C4, cstart, cend, bmpi->garray, ampi->Mvctx, MAT_INITIAL_MATRIX, map_h.data(), mm));
1164: // Adt * (B's diag + B's off-diag)
1165: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh1, Adt, false, Bd, false, mm->C1));
1166: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh2, Adt, false, Bo, false, mm->C2_mid));
1167: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh1, Adt, false, Bd, false, mm->C1));
1168: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh2, Adt, false, Bo, false, mm->C2_mid));
1169: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
1170: PetscCallCXX(sort_crs_matrix(mm->C1));
1171: PetscCallCXX(sort_crs_matrix(mm->C2_mid));
1172: #endif
1174: PetscCall(MatMPIAIJKokkosReduceEnd(comm, mm->C3, mm->C4, cstart, cend, bmpi->garray, ampi->Mvctx, MAT_INITIAL_MATRIX, map_h.data(), mm));
1176: // Create C2, which shares a, i arrays with C2_mid, but with new column indices and potentially larger column size
1177: MatColIdxKokkosView oldj = mm->C2_mid.graph.entries, newj(NoInit("j"), oldj.extent(0));
1178: PetscIntKokkosView map = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), map_h);
1179: PetscCallCXX(Kokkos::parallel_for(
1180: oldj.extent(0), KOKKOS_LAMBDA(const PetscInt i) { newj(i) = map(oldj(i)); }));
1181: PetscCallCXX(mm->C2 = KokkosCsrMatrix("C2", mm->C2_mid.numRows(), mm->n /*new column size*/, mm->C2_mid.nnz(), mm->C2_mid.values, mm->C2_mid.graph.row_map, newj));
1183: // C = (C1+Fd, C2+Fo)
1184: PetscCallCXX(mm->kh1.create_spadd_handle(true)); // C1, Fd are sorted
1185: PetscCallCXX(mm->kh2.create_spadd_handle(true)); // C2, Fo are sorted
1186: PetscCallCXX(KokkosSparse::spadd_symbolic(&mm->kh1, mm->C1, mm->Fd, mm->Cd));
1187: PetscCallCXX(KokkosSparse::spadd_symbolic(&mm->kh2, mm->C2, mm->Fo, mm->Co));
1188: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh1, 1.0, mm->C1, 1.0, mm->Fd, mm->Cd));
1189: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh2, 1.0, mm->C2, 1.0, mm->Fo, mm->Co));
1190: PetscFunctionReturn(PETSC_SUCCESS);
1191: }
1193: static PetscErrorCode MatProductNumeric_MPIAIJKokkos_AtB(Mat_Product *product, Mat A, Mat B, MatMatStruct_AtB *mm)
1194: {
1195: Mat_MPIAIJ *ampi = static_cast<Mat_MPIAIJ *>(A->data);
1196: Mat_MPIAIJ *bmpi = static_cast<Mat_MPIAIJ *>(B->data);
1197: KokkosCsrMatrix Adt, Aot, Bd, Bo;
1198: MPI_Comm comm;
1200: PetscFunctionBegin;
1201: PetscCall(PetscObjectGetComm((PetscObject)B, &comm));
1202: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(ampi->A, &Adt));
1203: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(ampi->B, &Aot));
1204: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->A, &Bd));
1205: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->B, &Bo));
1207: // Aot * (B's diag + B's off-diag)
1208: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh3, Aot, false, Bd, false, mm->C3));
1209: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh4, Aot, false, Bo, false, mm->C4));
1211: // Reduce E (i.e., C3 and C4)'s rows to form F, and overlap the communication
1212: PetscCall(MatMPIAIJKokkosReduceBegin(comm, mm->C3, mm->C4, 0, 0, NULL, NULL, MAT_REUSE_MATRIX, NULL, mm));
1214: // Adt * (B's diag + B's off-diag)
1215: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh1, Adt, false, Bd, false, mm->C1));
1216: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh2, Adt, false, Bo, false, mm->C2_mid));
1218: PetscCall(MatMPIAIJKokkosReduceEnd(comm, mm->C3, mm->C4, 0, 0, NULL, NULL, MAT_REUSE_MATRIX, NULL, mm));
1220: // C = (C1+Fd, C2+Fo)
1221: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh1, 1.0, mm->C1, 1.0, mm->Fd, mm->Cd));
1222: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh2, 1.0, mm->C2, 1.0, mm->Fo, mm->Co));
1223: PetscFunctionReturn(PETSC_SUCCESS);
1224: }
1226: /* MatProductSymbolic_MPIAIJKokkos_AB - AB flavor of MatProductSymbolic_MPIAIJKokkos
1228: Input Parameters:
1229: + product - Mat_Product which carried out the computation. Passed in to access info about this mat product.
1230: . A - an MPIAIJKOKKOS matrix
1231: . B - an MPIAIJKOKKOS matrix
1232: - mm - a struct used to stash intermediate data when computing AB. Persist from symbolic to numeric operations.
1233: */
1234: static PetscErrorCode MatProductSymbolic_MPIAIJKokkos_AB(Mat_Product *product, Mat A, Mat B, MatMatStruct_AB *mm)
1235: {
1236: Mat_MPIAIJ *ampi = static_cast<Mat_MPIAIJ *>(A->data);
1237: Mat_MPIAIJ *bmpi = static_cast<Mat_MPIAIJ *>(B->data);
1238: KokkosCsrMatrix Ad, Ao, Bd, Bo;
1240: PetscFunctionBegin;
1241: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->A, &Ad));
1242: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->B, &Ao));
1243: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->A, &Bd));
1244: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->B, &Bo));
1246: // TODO: add command line options to select spgemm algorithms
1247: auto spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_DEFAULT; // default is TPL if enabled, otherwise KK
1249: // CUDA-10.2's spgemm has bugs. We prefer the SpGEMMreuse APIs introduced in cuda-11.4
1250: #if defined(KOKKOSKERNELS_ENABLE_TPL_CUSPARSE)
1251: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
1252: spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_KK;
1253: #endif
1254: #endif
1256: mm->kh1.create_spgemm_handle(spgemm_alg);
1257: mm->kh2.create_spgemm_handle(spgemm_alg);
1258: mm->kh3.create_spgemm_handle(spgemm_alg);
1259: mm->kh4.create_spgemm_handle(spgemm_alg);
1261: // Bcast B's rows to form F, and overlap the communication
1262: PetscIntKokkosViewHost map_h(NoInit("map_h"), bmpi->B->cmap->n);
1263: PetscCall(MatMPIAIJKokkosBcastBegin(B, ampi->Mvctx, MAT_INITIAL_MATRIX, map_h.data(), mm));
1265: // A's diag * (B's diag + B's off-diag)
1266: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh1, Ad, false, Bd, false, mm->C1));
1267: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh2, Ad, false, Bo, false, mm->C2_mid)); // C2 aliases with C2_mid, except with new column indices
1268: // KK spgemm_symbolic() only populates the result's row map, but not its columns.
1269: // TODO: Remove the fake spgemm_numeric() after KK fixed this problem.
1270: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh1, Ad, false, Bd, false, mm->C1));
1271: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh2, Ad, false, Bo, false, mm->C2_mid));
1272: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
1273: PetscCallCXX(sort_crs_matrix(mm->C1));
1274: PetscCallCXX(sort_crs_matrix(mm->C2_mid));
1275: #endif
1277: PetscCall(MatMPIAIJKokkosBcastEnd(B, ampi->Mvctx, MAT_INITIAL_MATRIX, map_h.data(), mm));
1279: // A's off-diag * (F's diag + F's off-diag)
1280: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh3, Ao, false, mm->Fd, false, mm->C3));
1281: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh3, Ao, false, mm->Fd, false, mm->C3));
1282: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh4, Ao, false, mm->Fo, false, mm->C4));
1283: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh4, Ao, false, mm->Fo, false, mm->C4));
1284: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
1285: PetscCallCXX(sort_crs_matrix(mm->C3));
1286: PetscCallCXX(sort_crs_matrix(mm->C4));
1287: #endif
1289: // Create C2, which shares a, i arrays with C2_mid, but with new column indices and potentially larger column size
1290: MatColIdxKokkosView oldj = mm->C2_mid.graph.entries, newj(NoInit("j"), oldj.extent(0));
1291: PetscIntKokkosView map = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), map_h);
1292: PetscCallCXX(Kokkos::parallel_for(
1293: oldj.extent(0), KOKKOS_LAMBDA(const PetscInt i) { newj(i) = map(oldj(i)); }));
1294: mm->C2 = KokkosCsrMatrix("C2", mm->C2_mid.numRows(), mm->n /*new column size*/, mm->C2_mid.nnz(), mm->C2_mid.values, mm->C2_mid.graph.row_map, newj);
1296: // C = (Cd, Co) = (C1+C3, C2+C4)
1297: mm->kh1.create_spadd_handle(true); // C1, C3 are sorted
1298: mm->kh2.create_spadd_handle(true); // C2, C4 are sorted
1299: PetscCallCXX(KokkosSparse::spadd_symbolic(&mm->kh1, mm->C1, mm->C3, mm->Cd));
1300: PetscCallCXX(KokkosSparse::spadd_symbolic(&mm->kh2, mm->C2, mm->C4, mm->Co));
1301: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh1, 1.0, mm->C1, 1.0, mm->C3, mm->Cd));
1302: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh2, 1.0, mm->C2, 1.0, mm->C4, mm->Co));
1303: PetscFunctionReturn(PETSC_SUCCESS);
1304: }
1306: static PetscErrorCode MatProductNumeric_MPIAIJKokkos_AB(Mat_Product *product, Mat A, Mat B, MatMatStruct_AB *mm)
1307: {
1308: Mat_MPIAIJ *ampi = static_cast<Mat_MPIAIJ *>(A->data);
1309: Mat_MPIAIJ *bmpi = static_cast<Mat_MPIAIJ *>(B->data);
1310: KokkosCsrMatrix Ad, Ao, Bd, Bo;
1312: PetscFunctionBegin;
1313: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->A, &Ad));
1314: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->B, &Ao));
1315: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->A, &Bd));
1316: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->B, &Bo));
1318: // Bcast B's rows to form F, and overlap the communication
1319: PetscCall(MatMPIAIJKokkosBcastBegin(B, NULL, MAT_REUSE_MATRIX, NULL, mm));
1321: // A's diag * (B's diag + B's off-diag)
1322: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh1, Ad, false, Bd, false, mm->C1));
1323: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh2, Ad, false, Bo, false, mm->C2_mid));
1325: PetscCall(MatMPIAIJKokkosBcastEnd(B, NULL, MAT_REUSE_MATRIX, NULL, mm));
1327: // A's off-diag * (F's diag + F's off-diag)
1328: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh3, Ao, false, mm->Fd, false, mm->C3));
1329: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh4, Ao, false, mm->Fo, false, mm->C4));
1331: // C = (Cd, Co) = (C1+C3, C2+C4)
1332: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh1, 1.0, mm->C1, 1.0, mm->C3, mm->Cd));
1333: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh2, 1.0, mm->C2, 1.0, mm->C4, mm->Co));
1334: PetscFunctionReturn(PETSC_SUCCESS);
1335: }
1337: static PetscErrorCode MatProductNumeric_MPIAIJKokkos(Mat C)
1338: {
1339: Mat_MPIAIJ *cmpi = static_cast<Mat_MPIAIJ *>(C->data);
1340: Mat_Product *product;
1341: MatProductData_MPIAIJKokkos *pdata;
1342: MatProductType ptype;
1343: Mat A, B;
1345: PetscFunctionBegin;
1346: MatCheckProduct(C, 1); // make sure C is a product
1347: product = C->product;
1348: pdata = static_cast<MatProductData_MPIAIJKokkos *>(product->data);
1349: ptype = product->type;
1350: A = product->A;
1351: B = product->B;
1353: // See if numeric has already been done in symbolic (e.g., user calls MatMatMult(A,B,MAT_INITIAL_MATRIX,..,C)).
1354: // If yes, skip the numeric, but reset the flag so that next time when user calls MatMatMult(E,F,MAT_REUSE_MATRIX,..,C),
1355: // we still do numeric.
1356: if (pdata->reusesym) { // numeric reuses results from symbolic
1357: pdata->reusesym = PETSC_FALSE;
1358: PetscFunctionReturn(PETSC_SUCCESS);
1359: }
1361: if (ptype == MATPRODUCT_AB) {
1362: PetscCall(MatProductNumeric_MPIAIJKokkos_AB(product, A, B, pdata->mmAB));
1363: } else if (ptype == MATPRODUCT_AtB) {
1364: PetscCall(MatProductNumeric_MPIAIJKokkos_AtB(product, A, B, pdata->mmAtB));
1365: } else if (ptype == MATPRODUCT_PtAP) { // BtAB, computed by Z = AB; C= BtZ
1366: PetscCall(MatProductNumeric_MPIAIJKokkos_AB(product, A, B, pdata->mmAB));
1367: PetscCall(MatProductNumeric_MPIAIJKokkos_AtB(product, B, pdata->Z, pdata->mmAtB));
1368: }
1370: PetscCall(MatSeqAIJKokkosModifyDevice(cmpi->A)); // mark that A, B on device are modified
1371: PetscCall(MatSeqAIJKokkosModifyDevice(cmpi->B));
1372: PetscFunctionReturn(PETSC_SUCCESS);
1373: }
1375: static PetscErrorCode MatProductSymbolic_MPIAIJKokkos(Mat C)
1376: {
1377: Mat A, B;
1378: Mat_Product *product;
1379: MatProductType ptype;
1380: MatProductData_MPIAIJKokkos *pdata;
1381: MatMatStruct *mm = NULL;
1382: PetscInt m, n, M, N;
1383: Mat Cd, Co;
1384: MPI_Comm comm;
1386: PetscFunctionBegin;
1387: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
1388: MatCheckProduct(C, 1);
1389: product = C->product;
1390: PetscCheck(!product->data, comm, PETSC_ERR_PLIB, "Product data not empty");
1391: ptype = product->type;
1392: A = product->A;
1393: B = product->B;
1395: switch (ptype) {
1396: case MATPRODUCT_AB:
1397: m = A->rmap->n;
1398: n = B->cmap->n;
1399: M = A->rmap->N;
1400: N = B->cmap->N;
1401: break;
1402: case MATPRODUCT_AtB:
1403: m = A->cmap->n;
1404: n = B->cmap->n;
1405: M = A->cmap->N;
1406: N = B->cmap->N;
1407: break;
1408: case MATPRODUCT_PtAP:
1409: m = B->cmap->n;
1410: n = B->cmap->n;
1411: M = B->cmap->N;
1412: N = B->cmap->N;
1413: break; /* BtAB */
1414: default:
1415: SETERRQ(comm, PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
1416: }
1418: PetscCall(MatSetSizes(C, m, n, M, N));
1419: PetscCall(PetscLayoutSetUp(C->rmap));
1420: PetscCall(PetscLayoutSetUp(C->cmap));
1421: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
1423: pdata = new MatProductData_MPIAIJKokkos();
1424: pdata->reusesym = product->api_user;
1426: if (ptype == MATPRODUCT_AB) {
1427: auto mmAB = new MatMatStruct_AB();
1428: PetscCall(MatProductSymbolic_MPIAIJKokkos_AB(product, A, B, mmAB));
1429: mm = pdata->mmAB = mmAB;
1430: } else if (ptype == MATPRODUCT_AtB) {
1431: auto mmAtB = new MatMatStruct_AtB();
1432: PetscCall(MatProductSymbolic_MPIAIJKokkos_AtB(product, A, B, mmAtB));
1433: mm = pdata->mmAtB = mmAtB;
1434: } else if (ptype == MATPRODUCT_PtAP) { // C = BtAB, computed as Z = AB; C= BtZ
1435: Mat Zd, Zo, Z; // Zd, Zo are owned by pdata->Z
1437: auto mmAB = new MatMatStruct_AB();
1438: PetscCall(MatProductSymbolic_MPIAIJKokkos_AB(product, A, B, mmAB)); // Z stored as mmAB->{Cd, Co}
1439: PetscCall(MatCreateSeqAIJKokkosWithKokkosCsrMatrix(PETSC_COMM_SELF, mmAB->Cd, &Zd));
1440: PetscCall(MatCreateSeqAIJKokkosWithKokkosCsrMatrix(PETSC_COMM_SELF, mmAB->Co, &Zo));
1441: pdata->mmAB = mmAB;
1443: m = A->rmap->n; // Z's layout
1444: n = B->cmap->n;
1445: M = A->rmap->N;
1446: N = B->cmap->N;
1447: PetscCall(MatCreate(comm, &Z));
1448: PetscCall(MatSetSizes(Z, m, n, M, N));
1449: PetscCall(PetscLayoutSetUp(Z->rmap));
1450: PetscCall(PetscLayoutSetUp(Z->cmap));
1451: PetscCall(MatSetType(Z, MATMPIAIJKOKKOS));
1452: PetscCall(MatSetMPIAIJKokkosWithSplitSeqAIJKokkosMatrices(Z, Zd, Zo, mmAB->garray));
1454: auto mmAtB = new MatMatStruct_AtB();
1455: PetscCall(MatProductSymbolic_MPIAIJKokkos_AtB(product, B, Z, mmAtB)); // final result C stored as mmAtB->{Cd, Co}
1457: pdata->Z = Z; // give ownership to pdata
1458: mm = pdata->mmAtB = mmAtB;
1459: }
1461: PetscCall(MatCreateSeqAIJKokkosWithKokkosCsrMatrix(PETSC_COMM_SELF, mm->Cd, &Cd));
1462: PetscCall(MatCreateSeqAIJKokkosWithKokkosCsrMatrix(PETSC_COMM_SELF, mm->Co, &Co));
1463: PetscCall(MatSetMPIAIJKokkosWithSplitSeqAIJKokkosMatrices(C, Cd, Co, mm->garray));
1465: C->product->data = pdata;
1466: C->product->destroy = MatProductDataDestroy_MPIAIJKokkos;
1467: C->ops->productnumeric = MatProductNumeric_MPIAIJKokkos;
1468: PetscFunctionReturn(PETSC_SUCCESS);
1469: }
1471: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIAIJKokkos(Mat mat)
1472: {
1473: Mat_Product *product = mat->product;
1474: PetscBool match = PETSC_FALSE;
1475: PetscBool usecpu = PETSC_FALSE;
1477: PetscFunctionBegin;
1478: MatCheckProduct(mat, 1);
1479: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
1480: if (match) { /* we can always fallback to the CPU if requested */
1481: switch (product->type) {
1482: case MATPRODUCT_AB:
1483: if (product->api_user) {
1484: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
1485: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
1486: PetscOptionsEnd();
1487: } else {
1488: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
1489: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
1490: PetscOptionsEnd();
1491: }
1492: break;
1493: case MATPRODUCT_AtB:
1494: if (product->api_user) {
1495: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
1496: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
1497: PetscOptionsEnd();
1498: } else {
1499: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
1500: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
1501: PetscOptionsEnd();
1502: }
1503: break;
1504: case MATPRODUCT_PtAP:
1505: if (product->api_user) {
1506: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
1507: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
1508: PetscOptionsEnd();
1509: } else {
1510: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
1511: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
1512: PetscOptionsEnd();
1513: }
1514: break;
1515: default:
1516: break;
1517: }
1518: match = (PetscBool)!usecpu;
1519: }
1520: if (match) {
1521: switch (product->type) {
1522: case MATPRODUCT_AB:
1523: case MATPRODUCT_AtB:
1524: case MATPRODUCT_PtAP:
1525: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJKokkos;
1526: break;
1527: default:
1528: break;
1529: }
1530: }
1531: /* fallback to MPIAIJ ops */
1532: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
1533: PetscFunctionReturn(PETSC_SUCCESS);
1534: }
1536: // Mirror of MatCOOStruct_MPIAIJ on device
1537: struct MatCOOStruct_MPIAIJKokkos {
1538: PetscCount n;
1539: PetscSF sf;
1540: PetscCount Annz, Bnnz;
1541: PetscCount Annz2, Bnnz2;
1542: PetscCountKokkosView Ajmap1, Aperm1;
1543: PetscCountKokkosView Bjmap1, Bperm1;
1544: PetscCountKokkosView Aimap2, Ajmap2, Aperm2;
1545: PetscCountKokkosView Bimap2, Bjmap2, Bperm2;
1546: PetscCountKokkosView Cperm1;
1547: MatScalarKokkosView sendbuf, recvbuf;
1549: MatCOOStruct_MPIAIJKokkos(const MatCOOStruct_MPIAIJ *coo_h) :
1550: n(coo_h->n),
1551: sf(coo_h->sf),
1552: Annz(coo_h->Annz),
1553: Bnnz(coo_h->Bnnz),
1554: Annz2(coo_h->Annz2),
1555: Bnnz2(coo_h->Bnnz2),
1556: Ajmap1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Ajmap1, coo_h->Annz + 1))),
1557: Aperm1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Aperm1, coo_h->Atot1))),
1558: Bjmap1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bjmap1, coo_h->Bnnz + 1))),
1559: Bperm1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bperm1, coo_h->Btot1))),
1560: Aimap2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Aimap2, coo_h->Annz2))),
1561: Ajmap2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Ajmap2, coo_h->Annz2 + 1))),
1562: Aperm2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Aperm2, coo_h->Atot2))),
1563: Bimap2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bimap2, coo_h->Bnnz2))),
1564: Bjmap2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bjmap2, coo_h->Bnnz2 + 1))),
1565: Bperm2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bperm2, coo_h->Btot2))),
1566: Cperm1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Cperm1, coo_h->sendlen))),
1567: sendbuf(Kokkos::create_mirror_view(Kokkos::WithoutInitializing, DefaultMemorySpace(), MatScalarKokkosViewHost(coo_h->sendbuf, coo_h->sendlen))),
1568: recvbuf(Kokkos::create_mirror_view(Kokkos::WithoutInitializing, DefaultMemorySpace(), MatScalarKokkosViewHost(coo_h->recvbuf, coo_h->recvlen)))
1569: {
1570: PetscCallVoid(PetscObjectReference((PetscObject)sf));
1571: }
1573: ~MatCOOStruct_MPIAIJKokkos() { PetscCallVoid(PetscSFDestroy(&sf)); }
1574: };
1576: static PetscErrorCode MatCOOStructDestroy_MPIAIJKokkos(void *data)
1577: {
1578: PetscFunctionBegin;
1579: PetscCallCXX(delete static_cast<MatCOOStruct_MPIAIJKokkos *>(data));
1580: PetscFunctionReturn(PETSC_SUCCESS);
1581: }
1583: static PetscErrorCode MatSetPreallocationCOO_MPIAIJKokkos(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
1584: {
1585: PetscContainer container_h, container_d;
1586: MatCOOStruct_MPIAIJ *coo_h;
1587: MatCOOStruct_MPIAIJKokkos *coo_d;
1589: PetscFunctionBegin;
1590: PetscCall(MatSetPreallocationCOO_MPIAIJ(mat, coo_n, coo_i, coo_j)); /* mpiaij->A,B's type is set to seqaijkokkos */
1591: mat->preallocated = PETSC_TRUE;
1592: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
1593: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
1594: PetscCall(MatZeroEntries(mat));
1596: // Copy the COO struct to device
1597: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container_h));
1598: PetscCall(PetscContainerGetPointer(container_h, (void **)&coo_h));
1599: PetscCallCXX(coo_d = new MatCOOStruct_MPIAIJKokkos(coo_h));
1601: // Put the COO struct in a container and then attach that to the matrix
1602: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container_d));
1603: PetscCall(PetscContainerSetPointer(container_d, coo_d));
1604: PetscCall(PetscContainerSetUserDestroy(container_d, MatCOOStructDestroy_MPIAIJKokkos));
1605: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Device", (PetscObject)container_d));
1606: PetscCall(PetscContainerDestroy(&container_d));
1607: PetscFunctionReturn(PETSC_SUCCESS);
1608: }
1610: static PetscErrorCode MatSetValuesCOO_MPIAIJKokkos(Mat mat, const PetscScalar v[], InsertMode imode)
1611: {
1612: Mat_MPIAIJ *mpiaij = static_cast<Mat_MPIAIJ *>(mat->data);
1613: Mat A = mpiaij->A, B = mpiaij->B;
1614: MatScalarKokkosView Aa, Ba;
1615: MatScalarKokkosView v1;
1616: PetscMemType memtype;
1617: PetscContainer container;
1618: MatCOOStruct_MPIAIJKokkos *coo;
1620: PetscFunctionBegin;
1621: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", (PetscObject *)&container));
1622: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
1624: const auto &n = coo->n;
1625: const auto &Annz = coo->Annz;
1626: const auto &Annz2 = coo->Annz2;
1627: const auto &Bnnz = coo->Bnnz;
1628: const auto &Bnnz2 = coo->Bnnz2;
1629: const auto &vsend = coo->sendbuf;
1630: const auto &v2 = coo->recvbuf;
1631: const auto &Ajmap1 = coo->Ajmap1;
1632: const auto &Ajmap2 = coo->Ajmap2;
1633: const auto &Aimap2 = coo->Aimap2;
1634: const auto &Bjmap1 = coo->Bjmap1;
1635: const auto &Bjmap2 = coo->Bjmap2;
1636: const auto &Bimap2 = coo->Bimap2;
1637: const auto &Aperm1 = coo->Aperm1;
1638: const auto &Aperm2 = coo->Aperm2;
1639: const auto &Bperm1 = coo->Bperm1;
1640: const auto &Bperm2 = coo->Bperm2;
1641: const auto &Cperm1 = coo->Cperm1;
1643: PetscCall(PetscGetMemType(v, &memtype)); /* Return PETSC_MEMTYPE_HOST when v is NULL */
1644: if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we need to copy it to device if any */
1645: v1 = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), MatScalarKokkosViewHost((PetscScalar *)v, n));
1646: } else {
1647: v1 = MatScalarKokkosView((PetscScalar *)v, n); /* Directly use v[]'s memory */
1648: }
1650: if (imode == INSERT_VALUES) {
1651: PetscCall(MatSeqAIJGetKokkosViewWrite(A, &Aa)); /* write matrix values */
1652: PetscCall(MatSeqAIJGetKokkosViewWrite(B, &Ba));
1653: } else {
1654: PetscCall(MatSeqAIJGetKokkosView(A, &Aa)); /* read & write matrix values */
1655: PetscCall(MatSeqAIJGetKokkosView(B, &Ba));
1656: }
1658: PetscCall(PetscLogGpuTimeBegin());
1659: /* Pack entries to be sent to remote */
1660: Kokkos::parallel_for(
1661: vsend.extent(0), KOKKOS_LAMBDA(const PetscCount i) { vsend(i) = v1(Cperm1(i)); });
1663: /* Send remote entries to their owner and overlap the communication with local computation */
1664: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_KOKKOS, vsend.data(), PETSC_MEMTYPE_KOKKOS, v2.data(), MPI_REPLACE));
1665: /* Add local entries to A and B in one kernel */
1666: Kokkos::parallel_for(
1667: Annz + Bnnz, KOKKOS_LAMBDA(PetscCount i) {
1668: PetscScalar sum = 0.0;
1669: if (i < Annz) {
1670: for (PetscCount k = Ajmap1(i); k < Ajmap1(i + 1); k++) sum += v1(Aperm1(k));
1671: Aa(i) = (imode == INSERT_VALUES ? 0.0 : Aa(i)) + sum;
1672: } else {
1673: i -= Annz;
1674: for (PetscCount k = Bjmap1(i); k < Bjmap1(i + 1); k++) sum += v1(Bperm1(k));
1675: Ba(i) = (imode == INSERT_VALUES ? 0.0 : Ba(i)) + sum;
1676: }
1677: });
1678: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, vsend.data(), v2.data(), MPI_REPLACE));
1680: /* Add received remote entries to A and B in one kernel */
1681: Kokkos::parallel_for(
1682: Annz2 + Bnnz2, KOKKOS_LAMBDA(PetscCount i) {
1683: if (i < Annz2) {
1684: for (PetscCount k = Ajmap2(i); k < Ajmap2(i + 1); k++) Aa(Aimap2(i)) += v2(Aperm2(k));
1685: } else {
1686: i -= Annz2;
1687: for (PetscCount k = Bjmap2(i); k < Bjmap2(i + 1); k++) Ba(Bimap2(i)) += v2(Bperm2(k));
1688: }
1689: });
1690: PetscCall(PetscLogGpuTimeEnd());
1692: if (imode == INSERT_VALUES) {
1693: PetscCall(MatSeqAIJRestoreKokkosViewWrite(A, &Aa)); /* Increase A & B's state etc. */
1694: PetscCall(MatSeqAIJRestoreKokkosViewWrite(B, &Ba));
1695: } else {
1696: PetscCall(MatSeqAIJRestoreKokkosView(A, &Aa));
1697: PetscCall(MatSeqAIJRestoreKokkosView(B, &Ba));
1698: }
1699: PetscFunctionReturn(PETSC_SUCCESS);
1700: }
1702: static PetscErrorCode MatDestroy_MPIAIJKokkos(Mat A)
1703: {
1704: PetscFunctionBegin;
1705: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMPIAIJSetPreallocation_C", NULL));
1706: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", NULL));
1707: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1708: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1709: PetscCall(MatDestroy_MPIAIJ(A));
1710: PetscFunctionReturn(PETSC_SUCCESS);
1711: }
1713: static PetscErrorCode MatSetOps_MPIAIJKokkos(Mat B)
1714: {
1715: PetscFunctionBegin;
1716: B->ops->assemblyend = MatAssemblyEnd_MPIAIJKokkos;
1717: B->ops->mult = MatMult_MPIAIJKokkos;
1718: B->ops->multadd = MatMultAdd_MPIAIJKokkos;
1719: B->ops->multtranspose = MatMultTranspose_MPIAIJKokkos;
1720: B->ops->productsetfromoptions = MatProductSetFromOptions_MPIAIJKokkos;
1721: B->ops->destroy = MatDestroy_MPIAIJKokkos;
1723: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJKokkos));
1724: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJGetLocalMatMerge_C", MatMPIAIJGetLocalMatMerge_MPIAIJKokkos));
1725: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJKokkos));
1726: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJKokkos));
1727: PetscFunctionReturn(PETSC_SUCCESS);
1728: }
1730: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat A, MatType mtype, MatReuse reuse, Mat *newmat)
1731: {
1732: Mat B;
1733: Mat_MPIAIJ *a;
1735: PetscFunctionBegin;
1736: if (reuse == MAT_INITIAL_MATRIX) {
1737: PetscCall(MatDuplicate(A, MAT_COPY_VALUES, newmat));
1738: } else if (reuse == MAT_REUSE_MATRIX) {
1739: PetscCall(MatCopy(A, *newmat, SAME_NONZERO_PATTERN));
1740: }
1741: B = *newmat;
1743: B->boundtocpu = PETSC_FALSE;
1744: PetscCall(PetscFree(B->defaultvectype));
1745: PetscCall(PetscStrallocpy(VECKOKKOS, &B->defaultvectype));
1746: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJKOKKOS));
1748: a = static_cast<Mat_MPIAIJ *>(A->data);
1749: if (a->A) PetscCall(MatSetType(a->A, MATSEQAIJKOKKOS));
1750: if (a->B) PetscCall(MatSetType(a->B, MATSEQAIJKOKKOS));
1751: if (a->lvec) PetscCall(VecSetType(a->lvec, VECSEQKOKKOS));
1752: PetscCall(MatSetOps_MPIAIJKokkos(B));
1753: PetscFunctionReturn(PETSC_SUCCESS);
1754: }
1756: /*MC
1757: MATAIJKOKKOS - "mpiaijkokkos", a matrix type to be used for CSR sparse matrices with Kokkos
1759: A matrix type type using Kokkos-Kernels CrsMatrix type for portability across different device types
1761: Options Database Key:
1762: . -mat_type aijkokkos - sets the matrix type to `MATAIJKOKKOS`
1764: Level: beginner
1766: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJKokkos()`, `MATSEQAIJKOKKOS`, `MATSEQAIJ`, `MATMPIAIJ`
1767: M*/
1768: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJKokkos(Mat A)
1769: {
1770: PetscFunctionBegin;
1771: PetscCall(PetscKokkosInitializeCheck());
1772: PetscCall(MatCreate_MPIAIJ(A));
1773: PetscCall(MatConvert_MPIAIJ_MPIAIJKokkos(A, MATMPIAIJKOKKOS, MAT_INPLACE_MATRIX, &A));
1774: PetscFunctionReturn(PETSC_SUCCESS);
1775: }
1777: /*@C
1778: MatCreateAIJKokkos - Creates a sparse matrix in `MATAIJKOKOS` (compressed row) format
1779: (the default parallel PETSc format). This matrix will ultimately pushed down
1780: to Kokkos for calculations.
1782: Collective
1784: Input Parameters:
1785: + comm - MPI communicator, set to `PETSC_COMM_SELF`
1786: . m - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given)
1787: This value should be the same as the local size used in creating the
1788: y vector for the matrix-vector product y = Ax.
1789: . n - This value should be the same as the local size used in creating the
1790: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
1791: calculated if N is given) For square matrices n is almost always `m`.
1792: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
1793: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
1794: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
1795: (same value is used for all local rows)
1796: . d_nnz - array containing the number of nonzeros in the various rows of the
1797: DIAGONAL portion of the local submatrix (possibly different for each row)
1798: or `NULL`, if `d_nz` is used to specify the nonzero structure.
1799: The size of this array is equal to the number of local rows, i.e `m`.
1800: For matrices you plan to factor you must leave room for the diagonal entry and
1801: put in the entry even if it is zero.
1802: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
1803: submatrix (same value is used for all local rows).
1804: - o_nnz - array containing the number of nonzeros in the various rows of the
1805: OFF-DIAGONAL portion of the local submatrix (possibly different for
1806: each row) or `NULL`, if `o_nz` is used to specify the nonzero
1807: structure. The size of this array is equal to the number
1808: of local rows, i.e `m`.
1810: Output Parameter:
1811: . A - the matrix
1813: Level: intermediate
1815: Notes:
1816: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
1817: MatXXXXSetPreallocation() paradigm instead of this routine directly.
1818: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
1820: The AIJ format, also called compressed row storage), is fully compatible with standard Fortran
1821: storage. That is, the stored row and column indices can begin at
1822: either one (as in Fortran) or zero.
1824: .seealso: [](ch_matrices), `Mat`, `MATAIJKOKOS`, `MATSEQAIJKOKOS`, `MATMPIAIJKOKOS`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`,
1825: `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MATMPIAIJKOKKOS`, `MATAIJKOKKOS`
1826: @*/
1827: PetscErrorCode MatCreateAIJKokkos(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt M, PetscInt N, PetscInt d_nz, const PetscInt d_nnz[], PetscInt o_nz, const PetscInt o_nnz[], Mat *A)
1828: {
1829: PetscMPIInt size;
1831: PetscFunctionBegin;
1832: PetscCall(MatCreate(comm, A));
1833: PetscCall(MatSetSizes(*A, m, n, M, N));
1834: PetscCallMPI(MPI_Comm_size(comm, &size));
1835: if (size > 1) {
1836: PetscCall(MatSetType(*A, MATMPIAIJKOKKOS));
1837: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
1838: } else {
1839: PetscCall(MatSetType(*A, MATSEQAIJKOKKOS));
1840: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
1841: }
1842: PetscFunctionReturn(PETSC_SUCCESS);
1843: }