Actual source code: mpiaijkok.kokkos.cxx
1: #include <petsc_kokkos.hpp>
2: #include <petscvec_kokkos.hpp>
3: #include <petscpkg_version.h>
4: #include <petsc/private/sfimpl.h>
5: #include <../src/mat/impls/aij/seq/kokkos/aijkok.hpp>
6: #include <../src/mat/impls/aij/mpi/mpiaij.h>
7: #include <KokkosSparse_spadd.hpp>
8: #include <KokkosSparse_spgemm.hpp>
10: static PetscErrorCode MatAssemblyEnd_MPIAIJKokkos(Mat A, MatAssemblyType mode)
11: {
12: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)A->data;
14: PetscFunctionBegin;
15: PetscCall(MatAssemblyEnd_MPIAIJ(A, mode));
16: /* E.g., MatCreateSubMatrix() calls MatCreateMPIAIJWithSeqAIJ(comm,A,B,..), which creates Bnew of SEQAIJ and destroys B of SEQAIJKOKKOS.
17: Thus we finalize A/B/lvec's type in MatAssemblyEnd() to handle various cases.
18: */
19: if (mode == MAT_FINAL_ASSEMBLY) {
20: PetscCall(MatSetType(mpiaij->A, MATSEQAIJKOKKOS));
21: PetscCall(MatSetType(mpiaij->B, MATSEQAIJKOKKOS));
22: PetscCall(VecSetType(mpiaij->lvec, VECSEQKOKKOS));
23: }
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: // If mat was set to use the "set values with a hash table" mechanism, discard it and restore the cached ops
33: if (mat->hash_active) {
34: mat->ops[0] = mpiaij->cops;
35: mat->hash_active = PETSC_FALSE;
36: }
38: PetscCall(PetscLayoutSetUp(mat->rmap));
39: PetscCall(PetscLayoutSetUp(mat->cmap));
40: #if defined(PETSC_USE_DEBUG)
41: if (d_nnz) {
42: PetscInt i;
43: 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]);
44: }
45: if (o_nnz) {
46: PetscInt i;
47: 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]);
48: }
49: #endif
50: #if defined(PETSC_USE_CTABLE)
51: PetscCall(PetscHMapIDestroy(&mpiaij->colmap));
52: #else
53: PetscCall(PetscFree(mpiaij->colmap));
54: #endif
55: PetscCall(PetscFree(mpiaij->garray));
56: PetscCall(VecDestroy(&mpiaij->lvec));
57: PetscCall(VecScatterDestroy(&mpiaij->Mvctx));
58: /* Because the B will have been resized we simply destroy it and create a new one each time */
59: PetscCall(MatDestroy(&mpiaij->B));
61: if (!mpiaij->A) {
62: PetscCall(MatCreate(PETSC_COMM_SELF, &mpiaij->A));
63: PetscCall(MatSetSizes(mpiaij->A, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
64: }
65: if (!mpiaij->B) {
66: PetscMPIInt size;
67: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
68: PetscCall(MatCreate(PETSC_COMM_SELF, &mpiaij->B));
69: PetscCall(MatSetSizes(mpiaij->B, mat->rmap->n, size > 1 ? mat->cmap->N : 0, mat->rmap->n, size > 1 ? mat->cmap->N : 0));
70: }
71: PetscCall(MatSetType(mpiaij->A, MATSEQAIJKOKKOS));
72: PetscCall(MatSetType(mpiaij->B, MATSEQAIJKOKKOS));
73: PetscCall(MatSeqAIJSetPreallocation(mpiaij->A, d_nz, d_nnz));
74: PetscCall(MatSeqAIJSetPreallocation(mpiaij->B, o_nz, o_nnz));
75: mat->preallocated = PETSC_TRUE;
76: PetscFunctionReturn(PETSC_SUCCESS);
77: }
79: static PetscErrorCode MatMult_MPIAIJKokkos(Mat mat, Vec xx, Vec yy)
80: {
81: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
82: PetscInt nt;
84: PetscFunctionBegin;
85: PetscCall(VecGetLocalSize(xx, &nt));
86: 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);
87: PetscCall(VecScatterBegin(mpiaij->Mvctx, xx, mpiaij->lvec, INSERT_VALUES, SCATTER_FORWARD));
88: PetscCall((*mpiaij->A->ops->mult)(mpiaij->A, xx, yy));
89: PetscCall(VecScatterEnd(mpiaij->Mvctx, xx, mpiaij->lvec, INSERT_VALUES, SCATTER_FORWARD));
90: PetscCall((*mpiaij->B->ops->multadd)(mpiaij->B, mpiaij->lvec, yy, yy));
91: PetscFunctionReturn(PETSC_SUCCESS);
92: }
94: static PetscErrorCode MatMultAdd_MPIAIJKokkos(Mat mat, Vec xx, Vec yy, Vec zz)
95: {
96: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
97: PetscInt nt;
99: PetscFunctionBegin;
100: PetscCall(VecGetLocalSize(xx, &nt));
101: 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);
102: PetscCall(VecScatterBegin(mpiaij->Mvctx, xx, mpiaij->lvec, INSERT_VALUES, SCATTER_FORWARD));
103: PetscCall((*mpiaij->A->ops->multadd)(mpiaij->A, xx, yy, zz));
104: PetscCall(VecScatterEnd(mpiaij->Mvctx, xx, mpiaij->lvec, INSERT_VALUES, SCATTER_FORWARD));
105: PetscCall((*mpiaij->B->ops->multadd)(mpiaij->B, mpiaij->lvec, zz, zz));
106: PetscFunctionReturn(PETSC_SUCCESS);
107: }
109: static PetscErrorCode MatMultTranspose_MPIAIJKokkos(Mat mat, Vec xx, Vec yy)
110: {
111: Mat_MPIAIJ *mpiaij = (Mat_MPIAIJ *)mat->data;
112: PetscInt nt;
114: PetscFunctionBegin;
115: PetscCall(VecGetLocalSize(xx, &nt));
116: 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);
117: PetscCall((*mpiaij->B->ops->multtranspose)(mpiaij->B, xx, mpiaij->lvec));
118: PetscCall((*mpiaij->A->ops->multtranspose)(mpiaij->A, xx, yy));
119: PetscCall(VecScatterBegin(mpiaij->Mvctx, mpiaij->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
120: PetscCall(VecScatterEnd(mpiaij->Mvctx, mpiaij->lvec, yy, ADD_VALUES, SCATTER_REVERSE));
121: PetscFunctionReturn(PETSC_SUCCESS);
122: }
124: /* Merge the "A, B" matrices of mat into a matrix C. mat's type is MPIAIJKOKKOS. C's type is MATSEQAIJKOKKOS.
125: A is put before B. C's size would be A->rmap->n by (A->cmap->n + B->cmap->n).
126: C still uses local column ids. Their corresponding global column ids are returned in glob.
127: */
128: static PetscErrorCode MatMPIAIJGetLocalMatMerge_MPIAIJKokkos(Mat mat, MatReuse reuse, IS *glob, Mat *C)
129: {
130: Mat Ad, Ao;
131: const PetscInt *cmap;
133: PetscFunctionBegin;
134: PetscCall(MatMPIAIJGetSeqAIJ(mat, &Ad, &Ao, &cmap));
135: PetscCall(MatSeqAIJKokkosMergeMats(Ad, Ao, reuse, C));
136: if (glob) {
137: PetscInt cst, i, dn, on, *gidx;
138: PetscCall(MatGetLocalSize(Ad, NULL, &dn));
139: PetscCall(MatGetLocalSize(Ao, NULL, &on));
140: PetscCall(MatGetOwnershipRangeColumn(mat, &cst, NULL));
141: PetscCall(PetscMalloc1(dn + on, &gidx));
142: for (i = 0; i < dn; i++) gidx[i] = cst + i;
143: for (i = 0; i < on; i++) gidx[i + dn] = cmap[i];
144: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)Ad), dn + on, gidx, PETSC_OWN_POINTER, glob));
145: }
146: PetscFunctionReturn(PETSC_SUCCESS);
147: }
149: /* Structs used in matrix products of type C=AB, C=A^tB and C=B^tAB */
150: struct MatMatStruct {
151: PetscInt n, *garray; // C's garray and its size.
152: KokkosCsrMatrix Cd, Co; // C is in split form matrices (all in local column indcies)
153: KokkosCsrMatrix C1, C2, C3, C4; // intermediate mat products
154: KokkosCsrMatrix C2_mid, C4_mid; // alias of C2, C4; share their a[], i[], but with different j[] (hence column size)
155: PetscIntKokkosView E_NzLeft;
156: PetscSF sf = nullptr; // SF to bcast or reduce matrices E to F
157: MatScalarKokkosView rootBuf, leafBuf;
158: KokkosCsrMatrix Fd, Fo; // F in split form
160: KernelHandle kh1; // compute C1, add C1+C3 or C1+Fd
161: KernelHandle kh2; // compute C2, add C2+C4 or C2+Fo
162: KernelHandle kh3; // compute C3
163: KernelHandle kh4; // compute C4
165: PetscInt E_TeamSize; // kernel launching parameters in merging E or splitting F
166: PetscInt E_VectorLength;
167: PetscInt E_RowsPerTeam;
168: PetscInt F_TeamSize;
169: PetscInt F_VectorLength;
170: PetscInt F_RowsPerTeam;
172: ~MatMatStruct()
173: {
174: PetscFunctionBegin;
175: PetscCallAbort(PETSC_COMM_SELF, PetscSFDestroy(&sf));
176: PetscFunctionReturnVoid();
177: }
178: };
180: struct MatMatStruct_AB : public MatMatStruct {
181: PetscIntKokkosView F_NzLeft; // plans to split F (in leafbuf) into Fd, Fo
182: PetscIntKokkosView irootloc; // plans to put E (i.e., Bd, Bo) into rootBuf
183: PetscIntKokkosView rowoffset;
184: };
186: struct MatMatStruct_AtB : public MatMatStruct {
187: MatColIdxKokkosView Fdjmap; // plans to reduce data in rootBuf to Fd, Fo
188: MatColIdxKokkosView Fdjperm;
189: MatColIdxKokkosView Fojmap;
190: MatColIdxKokkosView Fojperm;
191: };
193: struct MatProductData_MPIAIJKokkos {
194: MatMatStruct_AB *mmAB = nullptr;
195: MatMatStruct_AtB *mmAtB = nullptr;
196: PetscBool reusesym = PETSC_FALSE;
197: Mat Z = nullptr; // store Z=AB in computing BtAB
199: ~MatProductData_MPIAIJKokkos()
200: {
201: delete mmAB;
202: delete mmAtB;
203: PetscCallAbort(PETSC_COMM_SELF, MatDestroy(&Z));
204: }
205: };
207: static PetscErrorCode MatProductDataDestroy_MPIAIJKokkos(void *data)
208: {
209: PetscFunctionBegin;
210: PetscCallCXX(delete static_cast<MatProductData_MPIAIJKokkos *>(data));
211: PetscFunctionReturn(PETSC_SUCCESS);
212: }
214: /* MatSetMPIAIJKokkosWithSplitSeqAIJKokkosMatrices - Set the diag and offdiag matrices of a MATMPIAIJKOKKOS matrix.
215: It is similar to MatCreateMPIAIJWithSplitArrays.
217: Input Parameters:
218: + mat - the MATMPIAIJKOKKOS matrix, which should have its type and layout set, but should not have its diag, offdiag matrices set
219: . A - the diag matrix using local col ids
220: - B - the offdiag matrix using global col ids
222: Output Parameter:
223: . mat - the updated MATMPIAIJKOKKOS matrix
224: */
225: static PetscErrorCode MatSetMPIAIJKokkosWithSplitSeqAIJKokkosMatrices(Mat mat, Mat A, Mat B, PetscInt *garray)
226: {
227: Mat_MPIAIJ *mpiaij = static_cast<Mat_MPIAIJ *>(mat->data);
228: PetscInt m, n, M, N, Am, An, Bm, Bn;
230: PetscFunctionBegin;
231: PetscCall(MatGetSize(mat, &M, &N));
232: PetscCall(MatGetLocalSize(mat, &m, &n));
233: PetscCall(MatGetLocalSize(A, &Am, &An));
234: PetscCall(MatGetLocalSize(B, &Bm, &Bn));
236: PetscCheck(m == Am && m == Bm, PETSC_COMM_SELF, PETSC_ERR_PLIB, "local number of rows do not match");
237: PetscCheck(n == An, PETSC_COMM_SELF, PETSC_ERR_PLIB, "local number of columns do not match");
238: // PetscCheck(N == Bn, PETSC_COMM_SELF, PETSC_ERR_PLIB, "global number of columns do not match");
239: PetscCheck(!mpiaij->A && !mpiaij->B, PETSC_COMM_SELF, PETSC_ERR_PLIB, "A, B of the MPIAIJ matrix are not empty");
240: mpiaij->A = A;
241: mpiaij->B = B;
242: mpiaij->garray = garray;
244: mat->preallocated = PETSC_TRUE;
245: mat->nooffprocentries = PETSC_TRUE; /* See MatAssemblyBegin_MPIAIJ. In effect, making MatAssemblyBegin a nop */
247: PetscCall(MatSetOption(mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
248: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
249: /* MatAssemblyEnd is critical here. It sets mat->offloadmask according to A and B's, and
250: also gets mpiaij->B compacted, with its col ids and size reduced
251: */
252: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
253: PetscCall(MatSetOption(mat, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
254: PetscCall(MatSetOption(mat, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
255: PetscFunctionReturn(PETSC_SUCCESS);
256: }
258: // Adapted from Kokkos-Kernels spmv_launch_parameters(), to get parameters in Kokkos nested loops which we used to merge or
259: // split csr matrices. The rule is to have "vector_length * team_size" be around 256 on GPUs (e.g., for a CUDA thread block)
260: template <class ExecutionSpace>
261: static PetscErrorCode MatMergeGetLaunchParameters(PetscInt numRows, PetscInt nnz, PetscInt rows_per_thread, PetscInt &team_size, PetscInt &vector_length, PetscInt &rows_per_team)
262: {
263: Kokkos::TeamPolicy<ExecutionSpace> teamPolicy(128, Kokkos::AUTO);
265: PetscFunctionBegin;
266: PetscInt nnz_per_row = numRows ? (nnz / numRows) : 0; // we might meet empty matrices
268: if (nnz_per_row < 1) nnz_per_row = 1;
270: int max_vector_length = teamPolicy.vector_length_max();
272: if (vector_length < 1) {
273: vector_length = 1;
274: while (vector_length < max_vector_length && vector_length * 6 < nnz_per_row) vector_length *= 2;
275: }
277: // Determine rows per thread
278: if (rows_per_thread < 1) {
279: if (KokkosKernels::Impl::kk_is_gpu_exec_space<ExecutionSpace>()) rows_per_thread = 1;
280: else {
281: if (nnz_per_row < 20 && nnz > 5000000) {
282: rows_per_thread = 256;
283: } else rows_per_thread = 64;
284: }
285: }
287: if (team_size < 1) {
288: if (KokkosKernels::Impl::kk_is_gpu_exec_space<ExecutionSpace>()) {
289: team_size = 256 / vector_length;
290: } else {
291: team_size = 1;
292: }
293: }
295: rows_per_team = rows_per_thread * team_size;
297: if (rows_per_team < 0) {
298: PetscInt nnz_per_team = 4096;
299: PetscInt conc = ExecutionSpace().concurrency();
300: while ((conc * nnz_per_team * 4 > nnz) && (nnz_per_team > 256)) nnz_per_team /= 2;
301: rows_per_team = (nnz_per_team + nnz_per_row - 1) / nnz_per_row;
302: }
303: PetscFunctionReturn(PETSC_SUCCESS);
304: }
306: /*
307: Reduce two sets of global indices into local ones
309: Input Parameters:
310: + n1 - size of garray1[], the first set
311: . garray1[n1] - a sorted global index array (without duplicates)
312: . m - size of indices[], the second set
313: - indices[m] - a unsorted global index array (might have duplicates), which will be updated on output into local ones
315: Output Parameters:
316: + n2 - size of garray2[], the merged set, which combines garray1[] and indices[]
317: . garray2[n2] - allocated by callee using PetscMalloc1(). Contains sorted unique global indices (without duplicates). Caller needs to free it.
318: . map[n1] - allocated by caller. It gives garray1[i] = garray2[map[i]]
319: - indices[m] - on output, global indices in this array are rewritten with local ones, i.e, indices_input[i] = garray2[indices_output[i]]
321: Example, say
322: n1 = 5
323: garray1[5] = {1, 4, 7, 8, 10}
324: m = 4
325: indices[4] = {2, 4, 8, 9}
327: Combining them together, we have 7 global indices in garray2[]
328: n2 = 7
329: garray2[7] = {1, 2, 4, 7, 8, 9, 10}
331: And we have map[] to connect "garray1[i] = garray2[map[i]], i=[0,n1)"
332: map[5] = {0, 2, 3, 4, 6}
334: On output, indices[] is updated with local indices
335: indices[4] = {1, 2, 4, 5}
336: */
337: static PetscErrorCode ReduceTwoSetsOfGlobalIndices(PetscInt n1, const PetscInt *garray1, PetscInt m, PetscInt *indices, PetscInt *n2_, PetscInt **garray2_, PetscInt *map)
338: {
339: PetscHMapI g2l = nullptr;
340: PetscHashIter iter;
341: PetscInt tot, key, val; // total unique global indices. key is global id; val is local id
342: PetscInt n2, *garray2;
344: PetscFunctionBegin;
345: tot = 0;
346: PetscCall(PetscHMapICreateWithSize(n1, &g2l));
347: for (PetscInt i = 0; i < m; i++) { // insert those in indices[]
348: PetscCall(PetscHMapIGetWithDefault(g2l, indices[i], -1, &val)); // if not exist, val is set with -1
349: if (val < 0) PetscCall(PetscHMapISet(g2l, indices[i], tot++)); // val < 0 means gid is not in the hash table yet
350: }
352: for (PetscInt i = 0; i < n1; i++) { // insert those in garray1[]
353: PetscCall(PetscHMapIGetWithDefault(g2l, garray1[i], -1, &val));
354: if (val < 0) PetscCall(PetscHMapISet(g2l, garray1[i], tot++));
355: }
357: // Pull out (unique) globals in the hash table and put them in garray2[]
358: n2 = tot;
359: PetscCall(PetscMalloc1(n2, &garray2));
360: tot = 0;
361: PetscHashIterBegin(g2l, iter);
362: while (!PetscHashIterAtEnd(g2l, iter)) {
363: PetscHashIterGetKey(g2l, iter, key);
364: PetscHashIterNext(g2l, iter);
365: garray2[tot++] = key;
366: }
368: // Sort garray2[] and then map them to local indices starting from 0
369: PetscCall(PetscSortInt(n2, garray2));
370: PetscCall(PetscHMapIClear(g2l));
371: for (PetscInt i = 0; i < tot; i++) PetscCall(PetscHMapISet(g2l, garray2[i], i)); // i is the local id
373: // Rewrite indices[] with local indices
374: for (PetscInt i = 0; i < m; i++) {
375: PetscCall(PetscHMapIGetWithDefault(g2l, indices[i], -1, &val));
376: PetscAssert(val >= 0, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Met a negative local column index");
377: indices[i] = val;
378: }
379: // Record the map that maps garray1[i] to garray2[map[i]]
380: for (PetscInt i = 0; i < n1; i++) PetscCall(PetscHMapIGetWithDefault(g2l, garray1[i], -1, &map[i]));
381: PetscCall(PetscHMapIDestroy(&g2l));
382: *n2_ = n2;
383: *garray2_ = garray2;
384: PetscFunctionReturn(PETSC_SUCCESS);
385: }
387: /*
388: MatMPIAIJKokkosReduce - Reduce rows of a MPIAIJKOKKOS matrix (E, in split form) to produce another matrix (F, also in split form, stored in mm)
390: 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.
392: Think each row of E as a leaf, then the given ownerSF specifies roots for the leaves. Roots may connect to multiple leaves.
393: 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.
395: Input Parameters:
396: + comm - MPI communicator of E
397: . A - diag block of E, using local column indices
398: . B - off-diag block of E, using local column indices
399: . cstart - (global) start column of Ed
400: . cend - (global) end column + 1 of Ed. In other words, E's column ownership is in range of [cstart, cend)
401: . garray1[n1] - global column indices of Eo. Here n1 is Eo's column size.
402: . ownerSF - the SF specifies ownership (root) of rows in E
403: . reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
404: - mm - to stash intermediate data structures for reuse
406: Output Parameters:
407: + map[n1] - allocated by caller. It maps garray1[] to garray2[]. See more at ReduceTwoSetsOfGlobalIndices().
408: - mm - contains various info, such as garray2[], F (Fd, Fo) etc.
410: Notes:
411: When reuse = MAT_REUSE_MATRIX, cstart, cend, garray1, ownerSF, map are not significant.
413: */
414: 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)
415: {
416: PetscFunctionBegin;
417: if (reuse == MAT_INITIAL_MATRIX) {
418: PetscInt Em = A.numRows(), Fm;
419: PetscInt n1 = B.numCols();
421: PetscCall(PetscSFGetGraph(ownerSF, &Fm, NULL, NULL, NULL)); // Fm = #rows of F = nroots of ownerSF
423: // Do the analysis on host
424: auto Ai_h = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), A.graph.row_map);
425: auto Aj_h = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), A.graph.entries);
426: auto Bi_h = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), B.graph.row_map);
427: auto Bj_h = Kokkos::create_mirror_view_and_copy(Kokkos::HostSpace(), B.graph.entries);
428: const MatRowMapType *Ai = Ai_h.data(), *Bi = Bi_h.data();
429: const MatColIdxType *Aj = Aj_h.data(), *Bj = Bj_h.data();
431: // Count how many nonzeros of each row in E are in the left of the diag block [cstart,cend)
432: PetscIntKokkosViewHost E_NzLeft_h(NoInit("E_NzLeft_h"), Em), E_RowLen_h(NoInit("E_RowLen_h"), Em);
433: PetscInt *E_NzLeft = E_NzLeft_h.data(), *E_RowLen = E_RowLen_h.data();
434: for (PetscInt i = 0; i < Em; i++) {
435: const PetscInt *first, *last, *it;
436: PetscInt count, step;
437: // std::lower_bound(first,last,cstart), but need to use global column indices
438: first = Bj + Bi[i];
439: last = Bj + Bi[i + 1];
440: count = last - first;
441: while (count > 0) {
442: it = first;
443: step = count / 2;
444: it += step;
445: if (garray1[*it] < cstart) { // map local to global
446: first = ++it;
447: count -= step + 1;
448: } else count = step;
449: }
450: E_NzLeft[i] = first - (Bj + Bi[i]);
451: E_RowLen[i] = (Ai[i + 1] - Ai[i]) + (Bi[i + 1] - Bi[i]);
452: }
454: // Get length of rows (i.e., sizes of leaves) that contribute to my roots
455: const PetscMPIInt *iranks, *ranks;
456: const PetscInt *ioffset, *irootloc, *roffset, *rmine;
457: PetscInt niranks, nranks;
458: MPI_Request *reqs;
459: PetscMPIInt tag;
460: PetscSF reduceSF;
461: PetscInt *sdisp, *rdisp;
463: PetscCall(PetscCommGetNewTag(comm, &tag));
464: PetscCall(PetscSFGetLeafRanks(ownerSF, &niranks, &iranks, &ioffset, &irootloc)); // get leaf ranks connecting to roots on this process (I'll recv from them)
465: PetscCall(PetscSFGetRootRanks(ownerSF, &nranks, &ranks, &roffset, &rmine, NULL)); // get root ranks this process connects (I'll send to them)
467: // Find out length of each row I will receive. Even for the same row index, when they are from
468: // different senders, they might have different lengths (and sparsity patterns)
469: PetscInt sendRowCnt = roffset[nranks], recvRowCnt = ioffset[niranks];
470: PetscInt *sendRowLen, *recvRowLen; // lengths of rows of I need to send/recv per process
472: PetscCall(PetscMalloc5(sendRowCnt, &sendRowLen, recvRowCnt + 1, &recvRowLen, nranks, &sdisp, niranks + 1, &rdisp, nranks + niranks, &reqs));
474: for (PetscInt i = 0; i < sendRowCnt; i++) sendRowLen[i] = E_RowLen[rmine[i]];
475: recvRowLen[0] = 0; // since we will make it in CSR format later
476: recvRowLen++; // advance the pointer now
477: 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]); }
478: 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]); }
479: PetscCallMPI(MPI_Waitall(nranks + niranks, reqs, MPI_STATUSES_IGNORE));
481: // Build the real PetscSF for reducing E rows (buffer to buffer)
482: rdisp[0] = 0;
483: for (PetscInt i = 0; i < niranks; i++) {
484: rdisp[i + 1] = rdisp[i];
485: for (PetscInt j = ioffset[i]; j < ioffset[i + 1]; j++) { rdisp[i + 1] += recvRowLen[j]; }
486: }
487: recvRowLen--; // put it back into csr format
488: for (PetscInt i = 0; i < recvRowCnt; i++) recvRowLen[i + 1] += recvRowLen[i];
490: for (PetscInt i = 0; i < nranks; i++) { MPI_Irecv(&sdisp[i], 1, MPIU_INT, ranks[i], tag, comm, &reqs[i]); }
491: for (PetscInt i = 0; i < niranks; i++) { MPI_Isend(&rdisp[i], 1, MPIU_INT, iranks[i], tag, comm, &reqs[nranks + i]); }
492: PetscCallMPI(MPI_Waitall(nranks + niranks, reqs, MPI_STATUSES_IGNORE));
494: PetscInt nleaves = 0, Enz = 0; // leaves are nonzeros I will send
495: PetscInt nroots = rdisp[niranks]; // roots are nonzeros I will recv
496: PetscSFNode *iremote;
498: for (PetscInt i = 0; i < Em; i++) Enz += E_RowLen[i];
499: PetscAssert(A.nnz() + B.nnz() == Enz, comm, PETSC_ERR_PLIB, "Enz should be equal to sum of nnz of A and B");
500: PetscCallMPI(PetscMalloc1(Enz, &iremote)); // no free, since we give ownership to reduceSF
502: for (PetscInt i = 0; i < nranks; i++) {
503: PetscInt count = 0;
504: for (PetscInt j = roffset[i]; j < roffset[i + 1]; j++) count += E_RowLen[rmine[j]];
505: for (PetscInt j = 0; j < count; j++) {
506: iremote[nleaves + j].rank = ranks[i];
507: iremote[nleaves + j].index = sdisp[i] + j;
508: }
509: nleaves += count;
510: }
511: PetscCheck(nleaves == Enz, comm, PETSC_ERR_PLIB, "nleaves should be equal to Enz");
513: PetscCall(PetscSFCreate(comm, &reduceSF));
514: PetscCall(PetscSFSetGraph(reduceSF, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
516: // Copy (global) column indices of the needed rows in E to sendCol[], and then PetscSFReduce to recvCol[]
517: PetscInt *sendCol, *recvCol;
518: PetscCall(PetscMalloc2(nleaves, &sendCol, nroots, &recvCol));
519: for (PetscInt k = 0; k < roffset[nranks]; k++) {
520: PetscInt i = rmine[k]; // row to be copied
521: PetscInt *buf = &sendCol[Ai[i] + Bi[i]];
522: PetscInt nzLeft = E_NzLeft[i];
523: PetscInt alen = Ai[i + 1] - Ai[i], blen = Bi[i + 1] - Bi[i];
524: for (PetscInt j = 0; j < alen + blen; j++) {
525: if (j < nzLeft) {
526: buf[j] = garray1[Bj[Bi[i] + j]]; // left B, in global
527: } else if (j < nzLeft + alen) {
528: buf[j] = Aj[Ai[i] + j - nzLeft] + cstart; // diag A, also in global
529: } else {
530: buf[j] = garray1[Bj[Bi[i] + j - alen]]; // right B, in global
531: }
532: }
533: }
534: PetscCall(PetscSFReduceWithMemTypeBegin(reduceSF, MPIU_INT, PETSC_MEMTYPE_HOST, sendCol, PETSC_MEMTYPE_HOST, recvCol, MPI_REPLACE));
535: PetscCall(PetscSFReduceEnd(reduceSF, MPIU_INT, sendCol, recvCol, MPI_REPLACE));
537: // 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
538: PetscInt *recvRowPerm, *recvColSorted;
539: PetscInt *recvNzPerm, *recvNzPermSorted;
540: PetscCall(PetscMalloc4(recvRowCnt, &recvRowPerm, nroots, &recvColSorted, nroots, &recvNzPerm, nroots, &recvNzPermSorted));
542: for (PetscInt i = 0; i < nroots; i++) recvNzPerm[i] = i; // numbering all received nonzeros
543: 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[]
544: PetscCall(PetscSortIntWithPermutation(recvRowCnt, irootloc, recvRowPerm)); // irootloc[] (owned by ownerSF) won't be changed
546: // i[] array, nz are always easiest to compute
547: MatRowMapKokkosViewHost Fdi_h(NoInit("Fdi_h"), Fm + 1), Foi_h(NoInit("Foi_h"), Fm + 1);
548: MatRowMapType *Fdi, *Foi;
549: PetscInt FnzDups = 0, Fdnz = 0, FdnzDups = 0, Fonz = 0, FonzDups = 0; // nz (with or without dups) in F, Fd, Fo
550: PetscInt iter;
552: Kokkos::deep_copy(Fdi_h, 0); // zero, as we will do 'val++' on them
553: Kokkos::deep_copy(Foi_h, 0);
554: Fdi = Fdi_h.data() + 1; // +1 for easy indexing in code below
555: Foi = Foi_h.data() + 1;
556: iter = 0;
557: while (iter < recvRowCnt) { // iter over received rows
558: PetscInt curRowIdx = irootloc[recvRowPerm[iter]];
559: PetscInt dupRows = 1; // current row has this many contributing rows (of various sparsity patterns)
561: while (iter + dupRows < recvRowCnt && irootloc[recvRowPerm[iter + dupRows]] == curRowIdx) dupRows++;
563: // Copy column indices (and their permutation) of these rows into recvColSorted & recvNzPermSorted
564: PetscInt nz = 0; // nz (with dups) in the current row
565: PetscInt *jbuf = recvColSorted + FnzDups;
566: PetscInt *pbuf = recvNzPermSorted + FnzDups;
567: PetscInt *jbuf2 = jbuf; // temp pointers
568: PetscInt *pbuf2 = pbuf;
569: for (PetscInt d = 0; d < dupRows; d++) {
570: PetscInt i = recvRowPerm[iter + d];
571: PetscInt len = recvRowLen[i + 1] - recvRowLen[i];
572: PetscCall(PetscArraycpy(jbuf2, &recvCol[recvRowLen[i]], len));
573: PetscCall(PetscArraycpy(pbuf2, &recvNzPerm[recvRowLen[i]], len));
574: jbuf2 += len;
575: pbuf2 += len;
576: nz += len;
577: }
578: PetscCall(PetscIntSortSemiOrderedWithArray(nz, jbuf, pbuf)); // It could be improved with k-way merge sort, since the rows are already sorted
580: // 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
581: PetscInt cur = 0;
582: while (cur < nz) {
583: PetscInt curColIdx = jbuf[cur];
584: PetscInt dups = 1;
586: while (cur + dups < nz && jbuf[cur + dups] == curColIdx) dups++;
587: if (curColIdx >= cstart && curColIdx < cend) {
588: Fdi[curRowIdx]++;
589: FdnzDups += dups;
590: } else {
591: Foi[curRowIdx]++;
592: FonzDups += dups;
593: }
594: cur += dups;
595: }
597: FnzDups += nz;
598: iter += dupRows; // Move to next unique row
599: }
601: Fdi = Fdi_h.data(); // restore Fdi, Foi and make them CSR
602: Foi = Foi_h.data();
603: for (PetscInt i = 0; i < Fm; i++) {
604: Fdi[i + 1] += Fdi[i];
605: Foi[i + 1] += Foi[i];
606: }
607: Fdnz = Fdi[Fm];
608: Fonz = Foi[Fm];
609: PetscCall(PetscFree2(sendCol, recvCol));
611: // Allocate j, jmap, jperm for Fd and Fo
612: MatColIdxKokkosViewHost Fdj_h(NoInit("Fdj_h"), Fdnz), Foj_h(NoInit("Foj_h"), Fonz);
613: MatRowMapKokkosViewHost Fdjmap_h(NoInit("Fdjmap_h"), Fdnz + 1), Fojmap_h(NoInit("Fojmap_h"), Fonz + 1); // +1 to make csr
614: MatRowMapKokkosViewHost Fdjperm_h(NoInit("Fdjperm_h"), FdnzDups), Fojperm_h(NoInit("Fojperm_h"), FonzDups);
615: MatColIdxType *Fdj = Fdj_h.data(), *Foj = Foj_h.data();
616: MatRowMapType *Fdjmap = Fdjmap_h.data(), *Fojmap = Fojmap_h.data();
617: MatRowMapType *Fdjperm = Fdjperm_h.data(), *Fojperm = Fojperm_h.data();
619: // Scan recvColSorted[] again, and fill j, jmap, jperm for Fd and Fo
620: Fdjmap[0] = 0;
621: Fojmap[0] = 0;
622: FnzDups = 0;
623: Fdnz = 0;
624: Fonz = 0;
625: iter = 0; // iter over received rows
626: while (iter < recvRowCnt) {
627: PetscInt curRowIdx = irootloc[recvRowPerm[iter]]; // current row idx
628: PetscInt dupRows = 1; // It has this many contributing rows (of various lengths)
629: PetscInt nz = 0; // nz (with dups) in the current row
631: while (iter + dupRows < recvRowCnt && irootloc[recvRowPerm[iter + dupRows]] == curRowIdx) dupRows++;
632: for (PetscInt d = 0; d < dupRows; d++) {
633: PetscInt i = recvRowPerm[iter + d];
634: nz += recvRowLen[i + 1] - recvRowLen[i];
635: }
637: PetscInt *jbuf = recvColSorted + FnzDups;
638: // Scan columns (in jbuf[0,nz) of this row, copy them and their permutation to j[] and jperm[] of Fd and Fo
639: PetscInt cur = 0;
640: while (cur < nz) {
641: PetscInt curColIdx = jbuf[cur];
642: PetscInt dups = 1;
644: while (cur + dups < nz && jbuf[cur + dups] == curColIdx) dups++;
645: if (curColIdx >= cstart && curColIdx < cend) {
646: Fdj[Fdnz] = curColIdx - cstart; // easily convert to local
647: Fdjmap[Fdnz + 1] = Fdjmap[Fdnz] + dups;
648: for (PetscInt j = 0; j < dups; j++) Fdjperm[Fdjmap[Fdnz] + j] = recvNzPermSorted[FnzDups + j];
649: FdnzDups += dups;
650: Fdnz++;
651: } else {
652: Foj[Fonz] = curColIdx; // in global
653: Fojmap[Fonz + 1] = Fojmap[Fonz] + dups;
654: for (PetscInt j = 0; j < dups; j++) Fojperm[Fojmap[Fonz] + j] = recvNzPermSorted[FnzDups + j];
655: FonzDups += dups;
656: Fonz++;
657: }
658: cur += dups;
659: FnzDups += dups;
660: }
661: iter += dupRows; // Move to next unique row
662: }
663: PetscCall(PetscFree4(recvRowPerm, recvColSorted, recvNzPerm, recvNzPermSorted));
664: PetscCall(PetscFree5(sendRowLen, recvRowLen, sdisp, rdisp, reqs));
666: // Combine global column indices in garray1[] and Foj[]
667: PetscInt n2, *garray2;
669: PetscCall(ReduceTwoSetsOfGlobalIndices(n1, garray1, Fonz, Foj, &n2, &garray2, map));
670: mm->sf = reduceSF;
671: mm->leafBuf = MatScalarKokkosView(NoInit("leafBuf"), nleaves);
672: mm->rootBuf = MatScalarKokkosView(NoInit("rootBuf"), nroots);
673: mm->garray = garray2; // give ownership, so no free
674: mm->n = n2;
675: mm->E_NzLeft = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), E_NzLeft_h);
676: mm->Fdjmap = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdjmap_h);
677: mm->Fdjperm = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdjperm_h);
678: mm->Fojmap = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fojmap_h);
679: mm->Fojperm = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fojperm_h);
681: // Output Fd and Fo in KokkosCsrMatrix format
682: MatScalarKokkosView Fda_d(NoInit("Fda_d"), Fdnz);
683: MatRowMapKokkosView Fdi_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdi_h);
684: MatColIdxKokkosView Fdj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdj_h);
685: MatScalarKokkosView Foa_d(NoInit("Foa_d"), Fonz);
686: MatRowMapKokkosView Foi_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Foi_h);
687: MatColIdxKokkosView Foj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Foj_h);
689: PetscCallCXX(mm->Fd = KokkosCsrMatrix("Fd", Fm, cend - cstart, Fdnz, Fda_d, Fdi_d, Fdj_d));
690: PetscCallCXX(mm->Fo = KokkosCsrMatrix("Fo", Fm, n2, Fonz, Foa_d, Foi_d, Foj_d)); // Fo's column size is n2, length of garray2[]
692: // Compute kernel launch parameters in merging E
693: PetscInt teamSize, vectorLength, rowsPerTeam;
695: teamSize = vectorLength = rowsPerTeam = -1;
696: PetscCall(MatMergeGetLaunchParameters<DefaultExecutionSpace>(Em, Enz, -1, teamSize, vectorLength, rowsPerTeam));
697: mm->E_TeamSize = teamSize;
698: mm->E_VectorLength = vectorLength;
699: mm->E_RowsPerTeam = rowsPerTeam;
700: } else PetscCheck(reuse == MAT_REUSE_MATRIX, comm, PETSC_ERR_PLIB, "Unsupported MatReuse enum %d", reuse);
702: // Handy aliases
703: auto &Aa = A.values;
704: auto &Ba = B.values;
705: const auto &Ai = A.graph.row_map;
706: const auto &Bi = B.graph.row_map;
707: const auto &E_NzLeft = mm->E_NzLeft;
708: auto &leafBuf = mm->leafBuf;
709: auto &rootBuf = mm->rootBuf;
710: PetscSF reduceSF = mm->sf;
711: PetscInt Em = A.numRows();
712: PetscInt teamSize = mm->E_TeamSize;
713: PetscInt vectorLength = mm->E_VectorLength;
714: PetscInt rowsPerTeam = mm->E_RowsPerTeam;
715: PetscInt workSets = (Em + rowsPerTeam - 1) / rowsPerTeam;
717: // Copy rows in A/B of E to leafBuf, then pass it to rootBuf
718: PetscCallCXX(Kokkos::parallel_for(
719: Kokkos::TeamPolicy<>(PetscGetKokkosExecutionSpace(), workSets, teamSize, vectorLength), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
720: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, 0, rowsPerTeam), [&](PetscInt k) {
721: PetscInt i = t.league_rank() * rowsPerTeam + k; // i-th row in F
722: if (i < Em) {
723: PetscInt disp = Ai(i) + Bi(i);
724: PetscInt alen = Ai(i + 1) - Ai(i);
725: PetscInt blen = Bi(i + 1) - Bi(i);
726: PetscInt nzleft = E_NzLeft(i);
728: Kokkos::parallel_for(Kokkos::ThreadVectorRange(t, alen + blen), [&](PetscInt j) {
729: MatScalar &val = leafBuf(disp + j);
730: if (j < nzleft) { // B left
731: val = Ba(Bi(i) + j);
732: } else if (j < nzleft + alen) { // diag A
733: val = Aa(Ai(i) + j - nzleft);
734: } else { // B right
735: val = Ba(Bi(i) + j - alen);
736: }
737: });
738: }
739: });
740: }));
741: PetscCall(PetscSFReduceWithMemTypeBegin(reduceSF, MPIU_SCALAR, PETSC_MEMTYPE_KOKKOS, leafBuf.data(), PETSC_MEMTYPE_KOKKOS, rootBuf.data(), MPI_REPLACE));
742: PetscFunctionReturn(PETSC_SUCCESS);
743: }
745: // To finish MatMPIAIJKokkosReduce.
746: 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)
747: {
748: auto &leafBuf = mm->leafBuf;
749: auto &rootBuf = mm->rootBuf;
750: auto &Fda = mm->Fd.values;
751: const auto &Fdjmap = mm->Fdjmap;
752: const auto &Fdjperm = mm->Fdjperm;
753: auto Fdnz = mm->Fd.nnz();
754: auto &Foa = mm->Fo.values;
755: const auto &Fojmap = mm->Fojmap;
756: const auto &Fojperm = mm->Fojperm;
757: auto Fonz = mm->Fo.nnz();
758: PetscSF reduceSF = mm->sf;
760: PetscFunctionBegin;
761: PetscCall(PetscSFReduceEnd(reduceSF, MPIU_SCALAR, leafBuf.data(), rootBuf.data(), MPI_REPLACE));
763: // Reduce data in rootBuf to Fd and Fo
764: PetscCallCXX(Kokkos::parallel_for(
765: Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, Fdnz), KOKKOS_LAMBDA(const MatRowMapType i) {
766: PetscScalar sum = 0.0;
767: for (MatRowMapType k = Fdjmap(i); k < Fdjmap(i + 1); k++) sum += rootBuf(Fdjperm(k));
768: Fda(i) = sum;
769: }));
771: PetscCallCXX(Kokkos::parallel_for(
772: Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, Fonz), KOKKOS_LAMBDA(const MatRowMapType i) {
773: PetscScalar sum = 0.0;
774: for (MatRowMapType k = Fojmap(i); k < Fojmap(i + 1); k++) sum += rootBuf(Fojperm(k));
775: Foa(i) = sum;
776: }));
777: PetscFunctionReturn(PETSC_SUCCESS);
778: }
780: /*
781: MatMPIAIJKokkosBcast - Bcast local rows of a MPIAIJKOKKOS matrix (E) to produce a local matrix (F, stored in mm) in split form
783: This is a complex routine. It is essentially the MPIAIJKOKKOS counterpart of MatGetBrowsOfAoCols_MPIAIJ, but supports
784: device and involves various index mapping.
786: In the given ownerSF, leaves correspond to rows in F, and roots correspond to rows in E. Roots may connect to multiple leaves.
787: 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
788: 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.
789: F has the same column layout as E.
791: Conceptually F has global column indices. In this routine, we spit F into diagonal Fd and off-diagonal Fo.
792: Fd uses local column indices, which are easy to compute. We just need to subtract the "local column range start" from the global indices.
793: Fo had global column indices at first. We will reduce them into local ones. In doing that, we also take into account the global
794: 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
795: column indices in Fo and update Fo with local indices.
797: Input Parameters:
798: + E - the MPIAIJKOKKOS matrix
799: . ownerSF - the ownership SF (insignificant in MAT_REUSE_MATRIX)
800: . reuse - either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX
801: - mm - to stash matproduct intermediate data structures
803: Output Parameters:
804: + map[n1] - allocated by caller. It maps garray1[] to garray2[]. See more at ReduceTwoSetsOfGlobalIndices.
805: - mm - contains various info, such as garray2[], Fd, Fo, etc.
807: Notes:
808: When reuse = MAT_REUSE_MATRIX, ownerSF, map are not significant.
809: The routine is provide in split-phase form MatMPIAIJKokkosBcastBegin/End() to provide computation/communication opportunities.
810: */
811: static PetscErrorCode MatMPIAIJKokkosBcastBegin(Mat E, PetscSF ownerSF, MatReuse reuse, PetscInt *map, MatMatStruct_AB *mm)
812: {
813: Mat_MPIAIJ *empi = static_cast<Mat_MPIAIJ *>(E->data);
814: Mat A = empi->A, B = empi->B; // diag and off-diag
815: Mat_SeqAIJKokkos *akok = static_cast<Mat_SeqAIJKokkos *>(A->spptr), *bkok = static_cast<Mat_SeqAIJKokkos *>(B->spptr);
816: PetscInt Em = E->rmap->n; // #local rows
817: MPI_Comm comm;
819: PetscFunctionBegin;
820: PetscCallMPI(PetscObjectGetComm((PetscObject)E, &comm));
821: if (reuse == MAT_INITIAL_MATRIX) {
822: Mat_SeqAIJ *aseq = static_cast<Mat_SeqAIJ *>(A->data), *bseq = static_cast<Mat_SeqAIJ *>(B->data);
823: PetscInt n1 = B->cmap->n, *Ai = aseq->i, *Aj = aseq->j, *Bi = bseq->i, *Bj = bseq->j;
824: const PetscInt *garray1 = empi->garray; // its size is n1
825: PetscInt cstart, cend;
826: PetscSF bcastSF;
828: PetscCall(MatGetOwnershipRangeColumn(E, &cstart, &cend));
830: // Count how many nonzeros of each row in E are in the left of the diag block [cstart,cend)
831: PetscIntKokkosViewHost E_NzLeft_h(NoInit("E_NzLeft_h"), Em), E_RowLen_h(NoInit("E_RowLen_h"), Em);
832: PetscInt *E_NzLeft = E_NzLeft_h.data(), *E_RowLen = E_RowLen_h.data();
833: for (PetscInt i = 0; i < Em; i++) {
834: const PetscInt *first, *last, *it;
835: PetscInt count, step;
836: // std::lower_bound(first,last,cstart), but need to use global column indices
837: first = Bj + Bi[i];
838: last = Bj + Bi[i + 1];
839: count = last - first;
840: while (count > 0) {
841: it = first;
842: step = count / 2;
843: it += step;
844: if (empi->garray[*it] < cstart) { // map local to global
845: first = ++it;
846: count -= step + 1;
847: } else count = step;
848: }
849: E_NzLeft[i] = first - (Bj + Bi[i]);
850: E_RowLen[i] = (Ai[i + 1] - Ai[i]) + (Bi[i + 1] - Bi[i]);
851: }
853: // Compute row pointer Fi of F
854: PetscInt *Fi, Fm, Fnz;
855: PetscCall(PetscSFGetGraph(ownerSF, NULL, &Fm, NULL, NULL)); // Fm = #rows of F = nleaves of ownerSF
856: PetscCall(PetscMalloc1(Fm + 1, &Fi));
857: Fi[0] = 0;
858: PetscCall(PetscSFBcastWithMemTypeBegin(ownerSF, MPIU_INT, PETSC_MEMTYPE_HOST, E_RowLen, PETSC_MEMTYPE_HOST, &Fi[1], MPI_REPLACE));
859: PetscCall(PetscSFBcastEnd(ownerSF, MPIU_INT, E_RowLen, &Fi[1], MPI_REPLACE));
860: for (PetscInt i = 0; i < Fm; i++) Fi[i + 1] += Fi[i];
861: Fnz = Fi[Fm];
863: // Build the real PetscSF for bcasting E rows (buffer to buffer)
864: const PetscMPIInt *iranks, *ranks;
865: const PetscInt *ioffset, *irootloc, *roffset;
866: PetscInt niranks, nranks, *sdisp, *rdisp;
867: MPI_Request *reqs;
868: PetscMPIInt tag;
870: PetscCall(PetscSFGetLeafRanks(ownerSF, &niranks, &iranks, &ioffset, &irootloc)); // get leaf ranks referencing roots on this process
871: PetscCall(PetscSFGetRootRanks(ownerSF, &nranks, &ranks, &roffset, NULL, NULL)); // recv info
872: PetscCall(PetscMalloc3(niranks + 1, &sdisp, nranks, &rdisp, niranks + nranks, &reqs));
874: sdisp[0] = 0; // send displacement
875: for (PetscInt i = 0; i < niranks; i++) {
876: sdisp[i + 1] = sdisp[i];
877: for (PetscInt j = ioffset[i]; j < ioffset[i + 1]; j++) {
878: PetscInt r = irootloc[j]; // row to be sent
879: sdisp[i + 1] += E_RowLen[r];
880: }
881: }
883: PetscCallMPI(PetscCommGetNewTag(comm, &tag));
884: for (PetscInt i = 0; i < nranks; i++) PetscCallMPI(MPI_Irecv(&rdisp[i], 1, MPIU_INT, ranks[i], tag, comm, &reqs[i]));
885: for (PetscInt i = 0; i < niranks; i++) PetscCallMPI(MPI_Isend(&sdisp[i], 1, MPIU_INT, iranks[i], tag, comm, &reqs[nranks + i]));
886: PetscCallMPI(MPI_Waitall(niranks + nranks, reqs, MPI_STATUSES_IGNORE));
888: PetscInt nleaves = Fnz; // leaves are nonzeros I will receive
889: PetscInt nroots = sdisp[niranks]; // roots are nonzeros I will send
890: PetscSFNode *iremote; // give ownership to bcastSF
891: PetscCall(PetscMalloc1(nleaves, &iremote));
892: for (PetscInt i = 0; i < nranks; i++) { // for each sender rank
893: PetscInt k = 0;
894: 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]
895: iremote[j].rank = ranks[i];
896: iremote[j].index = rdisp[i] + k; // their root location
897: k++;
898: }
899: }
900: PetscCall(PetscSFCreate(comm, &bcastSF));
901: PetscCall(PetscSFSetGraph(bcastSF, nroots, nleaves, NULL, PETSC_OWN_POINTER, iremote, PETSC_OWN_POINTER));
902: PetscCall(PetscFree3(sdisp, rdisp, reqs));
904: // Build a plan (rowoffset, irootloc, E_NzLeft) to copy rows in E to rootdata of bcastSF in parallel
905: PetscIntKokkosViewHost rowoffset_h(NoInit("rowoffset_h"), ioffset[niranks] + 1);
906: PetscInt *rowoffset = rowoffset_h.data(); // for each entry (row) indicated in irootloc[], we calculate its destinate offset in copying
907: rowoffset[0] = 0;
908: for (PetscInt i = 0; i < ioffset[niranks]; i++) { rowoffset[i + 1] = rowoffset[i] + E_RowLen[irootloc[i]]; }
910: // Copy (global) column indices of the needed rows in E to a buffer, and then bcast to Fj[]
911: PetscInt *jbuf, *Fj;
912: PetscCall(PetscMalloc2(nroots, &jbuf, Fnz, &Fj));
913: for (PetscInt k = 0; k < ioffset[niranks]; k++) {
914: PetscInt i = irootloc[k]; // row to be copied
915: PetscInt *buf = &jbuf[rowoffset[k]];
916: PetscInt nzLeft = E_NzLeft[i];
917: PetscInt alen = Ai[i + 1] - Ai[i], blen = Bi[i + 1] - Bi[i];
918: for (PetscInt j = 0; j < alen + blen; j++) {
919: if (j < nzLeft) {
920: buf[j] = empi->garray[Bj[Bi[i] + j]]; // left B, in global
921: } else if (j < nzLeft + alen) {
922: buf[j] = Aj[Ai[i] + j - nzLeft] + cstart; // diag A, also in global
923: } else {
924: buf[j] = empi->garray[Bj[Bi[i] + j - alen]]; // right B, in global
925: }
926: }
927: }
928: PetscCall(PetscSFBcastWithMemTypeBegin(bcastSF, MPIU_INT, PETSC_MEMTYPE_HOST, jbuf, PETSC_MEMTYPE_HOST, Fj, MPI_REPLACE));
929: PetscCall(PetscSFBcastEnd(bcastSF, MPIU_INT, jbuf, Fj, MPI_REPLACE));
931: // Build a plan (i.e., F_NzLeft) to split F into Fd and Fo
932: MatRowMapKokkosViewHost Fdi_h(NoInit("Fdi_h"), Fm + 1), Foi_h(NoInit("Foi_h"), Fm + 1); // row pointer of Fd, Fo
933: 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.
934: MatRowMapType *Fdi = Fdi_h.data(), *Foi = Foi_h.data();
935: MatColIdxType *F_NzLeft = F_NzLeft_h.data();
937: Fdi[0] = Foi[0] = 0;
938: for (PetscInt i = 0; i < Fm; i++) {
939: PetscInt *first, *last, *lb1, *lb2;
940: // cut the row into: Left, [cstart, cend), Right
941: first = Fj + Fi[i];
942: last = Fj + Fi[i + 1];
943: lb1 = std::lower_bound(first, last, cstart);
944: F_NzLeft[i] = lb1 - first;
945: lb2 = std::lower_bound(first, last, cend);
946: Fdi[i + 1] = lb2 - lb1; // row i length in Fdi
947: Foi[i + 1] = (Fi[i + 1] - Fi[i]) - Fdi[i + 1]; // row i length in Foi
948: }
949: for (PetscInt i = 0; i < Fm; i++) {
950: Fdi[i + 1] += Fdi[i];
951: Foi[i + 1] += Foi[i];
952: }
954: // Fill Fdj[] and Foj[], i.e., columns of Fd and Fo. Fdj[] are local, but Foj[] are not yet.
955: PetscInt Fdnz = Fdi[Fm], Fonz = Foi[Fm];
956: MatColIdxKokkosViewHost Fdj_h(NoInit("Fdj_h"), Fdnz), Foj_h(NoInit("Foj_h"), Fonz);
957: MatColIdxType *Fdj = Fdj_h.data(), *Foj = Foj_h.data(), gid;
959: for (PetscInt i = 0; i < Fm; i++) {
960: PetscInt nzLeft = F_NzLeft[i];
961: PetscInt len = Fdi[i + 1] - Fdi[i]; // diag row len
962: for (PetscInt j = 0; j < Fi[i + 1] - Fi[i]; j++) {
963: gid = Fj[Fi[i] + j];
964: if (j < nzLeft) { // left, in global
965: Foj[Foi[i] + j] = gid;
966: } else if (j < nzLeft + len) { // diag, in local
967: Fdj[Fdi[i] + j - nzLeft] = gid - cstart;
968: } else { // right, in global
969: Foj[Foi[i] + j - len] = gid;
970: }
971: }
972: }
973: PetscCall(PetscFree2(jbuf, Fj));
974: PetscCall(PetscFree(Fi));
976: // Reduce global indices in Foj[] and garray1[] into local ones
977: PetscInt n2, *garray2;
978: PetscCall(ReduceTwoSetsOfGlobalIndices(n1, garray1, Fonz, Foj, &n2, &garray2, map));
980: // Record the plans built above, for reuse
981: PetscIntKokkosViewHost tmp(const_cast<PetscInt *>(irootloc), ioffset[niranks]); // irootloc[] is owned by ownerSF. We create a copy for safety
982: PetscIntKokkosViewHost irootloc_h(NoInit("irootloc_h"), ioffset[niranks]);
983: Kokkos::deep_copy(irootloc_h, tmp);
984: mm->sf = bcastSF;
985: mm->E_NzLeft = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), E_NzLeft_h);
986: mm->F_NzLeft = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), F_NzLeft_h);
987: mm->irootloc = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), irootloc_h);
988: mm->rowoffset = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), rowoffset_h);
989: mm->rootBuf = MatScalarKokkosView(NoInit("rootBuf"), nroots);
990: mm->leafBuf = MatScalarKokkosView(NoInit("leafBuf"), nleaves);
991: mm->garray = garray2;
992: mm->n = n2;
994: // Output Fd and Fo in KokkosCsrMatrix format
995: MatScalarKokkosView Fda_d(NoInit("Fda_d"), Fdnz), Foa_d(NoInit("Foa_d"), Fonz);
996: MatRowMapKokkosView Fdi_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdi_h);
997: MatColIdxKokkosView Fdj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Fdj_h);
998: MatRowMapKokkosView Foi_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Foi_h);
999: MatColIdxKokkosView Foj_d = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), Foj_h);
1001: PetscCallCXX(mm->Fd = KokkosCsrMatrix("Fd", Fm, cend - cstart, Fdnz, Fda_d, Fdi_d, Fdj_d));
1002: PetscCallCXX(mm->Fo = KokkosCsrMatrix("Fo", Fm, n2, Fonz, Foa_d, Foi_d, Foj_d));
1004: // Compute kernel launch parameters in merging E or splitting F
1005: PetscInt teamSize, vectorLength, rowsPerTeam;
1007: teamSize = vectorLength = rowsPerTeam = -1;
1008: PetscCall(MatMergeGetLaunchParameters<DefaultExecutionSpace>(mm->irootloc.extent(0), mm->rootBuf.extent(0), -1, teamSize, vectorLength, rowsPerTeam));
1009: mm->E_TeamSize = teamSize;
1010: mm->E_VectorLength = vectorLength;
1011: mm->E_RowsPerTeam = rowsPerTeam;
1013: teamSize = vectorLength = rowsPerTeam = -1;
1014: PetscCall(MatMergeGetLaunchParameters<DefaultExecutionSpace>(Fm, Fnz, -1, teamSize, vectorLength, rowsPerTeam));
1015: mm->F_TeamSize = teamSize;
1016: mm->F_VectorLength = vectorLength;
1017: mm->F_RowsPerTeam = rowsPerTeam;
1018: } else PetscCheck(reuse == MAT_REUSE_MATRIX, comm, PETSC_ERR_PLIB, "Unsupported MatReuse enum %d", reuse);
1020: // Sync E's value to device
1021: akok->a_dual.sync_device();
1022: bkok->a_dual.sync_device();
1024: // Handy aliases
1025: const auto &Aa = akok->a_dual.view_device();
1026: const auto &Ba = bkok->a_dual.view_device();
1027: const auto &Ai = akok->i_dual.view_device();
1028: const auto &Bi = bkok->i_dual.view_device();
1030: // Fetch the plans
1031: PetscIntKokkosView &E_NzLeft = mm->E_NzLeft;
1032: PetscSF &bcastSF = mm->sf;
1033: MatScalarKokkosView &rootBuf = mm->rootBuf;
1034: MatScalarKokkosView &leafBuf = mm->leafBuf;
1035: PetscIntKokkosView &irootloc = mm->irootloc;
1036: PetscIntKokkosView &rowoffset = mm->rowoffset;
1038: PetscInt teamSize = mm->E_TeamSize;
1039: PetscInt vectorLength = mm->E_VectorLength;
1040: PetscInt rowsPerTeam = mm->E_RowsPerTeam;
1041: PetscInt workSets = (irootloc.extent(0) + rowsPerTeam - 1) / rowsPerTeam;
1043: // Copy rows in A/B of E to rootBuf, then bcast it to leafBuf
1044: PetscCallCXX(Kokkos::parallel_for(
1045: Kokkos::TeamPolicy<>(PetscGetKokkosExecutionSpace(), workSets, teamSize, vectorLength), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
1046: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, 0, rowsPerTeam), [&](PetscInt k) {
1047: size_t r = t.league_rank() * rowsPerTeam + k; // r-th entry in irootloc[]
1048: if (r < irootloc.extent(0)) {
1049: PetscInt i = irootloc(r); // row i of E
1050: PetscInt disp = rowoffset(r);
1051: PetscInt alen = Ai(i + 1) - Ai(i);
1052: PetscInt blen = Bi(i + 1) - Bi(i);
1053: PetscInt nzleft = E_NzLeft(i);
1055: Kokkos::parallel_for(Kokkos::ThreadVectorRange(t, alen + blen), [&](PetscInt j) {
1056: if (j < nzleft) { // B left
1057: rootBuf(disp + j) = Ba(Bi(i) + j);
1058: } else if (j < nzleft + alen) { // diag A
1059: rootBuf(disp + j) = Aa(Ai(i) + j - nzleft);
1060: } else { // B right
1061: rootBuf(disp + j) = Ba(Bi(i) + j - alen);
1062: }
1063: });
1064: }
1065: });
1066: }));
1067: PetscCall(PetscSFBcastWithMemTypeBegin(bcastSF, MPIU_SCALAR, PETSC_MEMTYPE_KOKKOS, rootBuf.data(), PETSC_MEMTYPE_KOKKOS, leafBuf.data(), MPI_REPLACE));
1068: PetscFunctionReturn(PETSC_SUCCESS);
1069: }
1071: // To finish MatMPIAIJKokkosBcast.
1072: static PetscErrorCode MatMPIAIJKokkosBcastEnd(Mat E, PetscSF ownerSF, MatReuse reuse, PetscInt *map, MatMatStruct_AB *mm)
1073: {
1074: PetscFunctionBegin;
1075: const auto &Fd = mm->Fd;
1076: const auto &Fo = mm->Fo;
1077: const auto &Fdi = Fd.graph.row_map;
1078: const auto &Foi = Fo.graph.row_map;
1079: auto &Fda = Fd.values;
1080: auto &Foa = Fo.values;
1081: auto Fm = Fd.numRows();
1083: PetscIntKokkosView &F_NzLeft = mm->F_NzLeft;
1084: PetscSF &bcastSF = mm->sf;
1085: MatScalarKokkosView &rootBuf = mm->rootBuf;
1086: MatScalarKokkosView &leafBuf = mm->leafBuf;
1087: PetscInt teamSize = mm->F_TeamSize;
1088: PetscInt vectorLength = mm->F_VectorLength;
1089: PetscInt rowsPerTeam = mm->F_RowsPerTeam;
1090: PetscInt workSets = (Fm + rowsPerTeam - 1) / rowsPerTeam;
1092: PetscCall(PetscSFBcastEnd(bcastSF, MPIU_SCALAR, rootBuf.data(), leafBuf.data(), MPI_REPLACE));
1094: // Update Fda and Foa with new data in leafBuf (as if it is Fa)
1095: PetscCallCXX(Kokkos::parallel_for(
1096: Kokkos::TeamPolicy<>(PetscGetKokkosExecutionSpace(), workSets, teamSize, vectorLength), KOKKOS_LAMBDA(const KokkosTeamMemberType &t) {
1097: Kokkos::parallel_for(Kokkos::TeamThreadRange(t, 0, rowsPerTeam), [&](PetscInt k) {
1098: PetscInt i = t.league_rank() * rowsPerTeam + k; // i-th row in F
1099: if (i < Fm) {
1100: PetscInt nzLeft = F_NzLeft(i);
1101: PetscInt alen = Fdi(i + 1) - Fdi(i);
1102: PetscInt blen = Foi(i + 1) - Foi(i);
1103: PetscInt Fii = Fdi(i) + Foi(i);
1105: Kokkos::parallel_for(Kokkos::ThreadVectorRange(t, alen + blen), [&](PetscInt j) {
1106: PetscScalar val = leafBuf(Fii + j);
1107: if (j < nzLeft) { // left
1108: Foa(Foi(i) + j) = val;
1109: } else if (j < nzLeft + alen) { // diag
1110: Fda(Fdi(i) + j - nzLeft) = val;
1111: } else { // right
1112: Foa(Foi(i) + j - alen) = val;
1113: }
1114: });
1115: }
1116: });
1117: }));
1118: PetscFunctionReturn(PETSC_SUCCESS);
1119: }
1121: static PetscErrorCode MatProductSymbolic_MPIAIJKokkos_AtB(Mat_Product *product, Mat A, Mat B, MatMatStruct_AtB *mm)
1122: {
1123: Mat_MPIAIJ *ampi = static_cast<Mat_MPIAIJ *>(A->data);
1124: Mat_MPIAIJ *bmpi = static_cast<Mat_MPIAIJ *>(B->data);
1125: KokkosCsrMatrix Adt, Aot, Ad, Ao, Bd, Bo;
1126: PetscInt cstart, cend;
1127: MPI_Comm comm;
1129: PetscFunctionBegin;
1130: PetscCall(PetscObjectGetComm((PetscObject)B, &comm));
1131: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(ampi->A, &Adt));
1132: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(ampi->B, &Aot));
1133: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->A, &Ad));
1134: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->B, &Ao));
1135: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->A, &Bd));
1136: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->B, &Bo));
1138: // TODO: add command line options to select spgemm algorithms
1139: auto spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_DEFAULT; // default is TPL if enabled, otherwise KK
1141: // CUDA-10.2's spgemm has bugs. We prefer the SpGEMMreuse APIs introduced in cuda-11.4
1142: #if defined(KOKKOSKERNELS_ENABLE_TPL_CUSPARSE)
1143: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
1144: spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_KK;
1145: #endif
1146: #endif
1148: PetscCallCXX(mm->kh1.create_spgemm_handle(spgemm_alg));
1149: PetscCallCXX(mm->kh2.create_spgemm_handle(spgemm_alg));
1150: PetscCallCXX(mm->kh3.create_spgemm_handle(spgemm_alg));
1151: PetscCallCXX(mm->kh4.create_spgemm_handle(spgemm_alg));
1153: // Aot * (B's diag + B's off-diag)
1154: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh3, Aot, false, Bd, false, mm->C3));
1155: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh4, Aot, false, Bo, false, mm->C4));
1156: // KK spgemm_symbolic() only populates the result's row map, but not its columns.
1157: // TODO: Remove the fake spgemm_numeric() after KK fixed this problem.
1158: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh3, Aot, false, Bd, false, mm->C3));
1159: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh4, Aot, false, Bo, false, mm->C4));
1160: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
1162: PetscCallCXX(sort_crs_matrix(mm->C3));
1163: PetscCallCXX(sort_crs_matrix(mm->C4));
1164: #endif
1166: // Reduce E (i.e., C3 and C4)'s rows to form F, and overlap the communication
1167: PetscIntKokkosViewHost map_h(NoInit("map_h"), bmpi->B->cmap->n);
1168: PetscCall(MatGetOwnershipRangeColumn(B, &cstart, &cend));
1169: PetscCall(MatMPIAIJKokkosReduceBegin(comm, mm->C3, mm->C4, cstart, cend, bmpi->garray, ampi->Mvctx, MAT_INITIAL_MATRIX, map_h.data(), mm));
1171: // Adt * (B's diag + B's off-diag)
1172: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh1, Adt, false, Bd, false, mm->C1));
1173: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh2, Adt, false, Bo, false, mm->C2_mid));
1174: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh1, Adt, false, Bd, false, mm->C1));
1175: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh2, Adt, false, Bo, false, mm->C2_mid));
1176: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
1177: PetscCallCXX(sort_crs_matrix(mm->C1));
1178: PetscCallCXX(sort_crs_matrix(mm->C2_mid));
1179: #endif
1181: PetscCall(MatMPIAIJKokkosReduceEnd(comm, mm->C3, mm->C4, cstart, cend, bmpi->garray, ampi->Mvctx, MAT_INITIAL_MATRIX, map_h.data(), mm));
1183: // Create C2, which shares a, i arrays with C2_mid, but with new column indices and potentially larger column size
1184: MatColIdxKokkosView oldj = mm->C2_mid.graph.entries, newj(NoInit("j"), oldj.extent(0));
1185: PetscIntKokkosView map = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), map_h);
1186: PetscCallCXX(Kokkos::parallel_for(Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, oldj.extent(0)), KOKKOS_LAMBDA(const PetscInt i) { newj(i) = map(oldj(i)); }));
1187: 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));
1189: // C = (C1+Fd, C2+Fo)
1190: PetscCallCXX(mm->kh1.create_spadd_handle(true)); // C1, Fd are sorted
1191: PetscCallCXX(mm->kh2.create_spadd_handle(true)); // C2, Fo are sorted
1192: PetscCallCXX(KokkosSparse::spadd_symbolic(&mm->kh1, mm->C1, mm->Fd, mm->Cd));
1193: PetscCallCXX(KokkosSparse::spadd_symbolic(&mm->kh2, mm->C2, mm->Fo, mm->Co));
1194: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh1, 1.0, mm->C1, 1.0, mm->Fd, mm->Cd));
1195: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh2, 1.0, mm->C2, 1.0, mm->Fo, mm->Co));
1196: PetscFunctionReturn(PETSC_SUCCESS);
1197: }
1199: static PetscErrorCode MatProductNumeric_MPIAIJKokkos_AtB(Mat_Product *product, Mat A, Mat B, MatMatStruct_AtB *mm)
1200: {
1201: Mat_MPIAIJ *ampi = static_cast<Mat_MPIAIJ *>(A->data);
1202: Mat_MPIAIJ *bmpi = static_cast<Mat_MPIAIJ *>(B->data);
1203: KokkosCsrMatrix Adt, Aot, Bd, Bo;
1204: MPI_Comm comm;
1206: PetscFunctionBegin;
1207: PetscCall(PetscObjectGetComm((PetscObject)B, &comm));
1208: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(ampi->A, &Adt));
1209: PetscCall(MatSeqAIJKokkosGenerateTranspose_Private(ampi->B, &Aot));
1210: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->A, &Bd));
1211: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->B, &Bo));
1213: // Aot * (B's diag + B's off-diag)
1214: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh3, Aot, false, Bd, false, mm->C3));
1215: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh4, Aot, false, Bo, false, mm->C4));
1217: // Reduce E (i.e., C3 and C4)'s rows to form F, and overlap the communication
1218: PetscCall(MatMPIAIJKokkosReduceBegin(comm, mm->C3, mm->C4, 0, 0, NULL, NULL, MAT_REUSE_MATRIX, NULL, mm));
1220: // Adt * (B's diag + B's off-diag)
1221: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh1, Adt, false, Bd, false, mm->C1));
1222: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh2, Adt, false, Bo, false, mm->C2_mid));
1224: PetscCall(MatMPIAIJKokkosReduceEnd(comm, mm->C3, mm->C4, 0, 0, NULL, NULL, MAT_REUSE_MATRIX, NULL, mm));
1226: // C = (C1+Fd, C2+Fo)
1227: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh1, 1.0, mm->C1, 1.0, mm->Fd, mm->Cd));
1228: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh2, 1.0, mm->C2, 1.0, mm->Fo, mm->Co));
1229: PetscFunctionReturn(PETSC_SUCCESS);
1230: }
1232: /* MatProductSymbolic_MPIAIJKokkos_AB - AB flavor of MatProductSymbolic_MPIAIJKokkos
1234: Input Parameters:
1235: + product - Mat_Product which carried out the computation. Passed in to access info about this mat product.
1236: . A - an MPIAIJKOKKOS matrix
1237: . B - an MPIAIJKOKKOS matrix
1238: - mm - a struct used to stash intermediate data when computing AB. Persist from symbolic to numeric operations.
1239: */
1240: static PetscErrorCode MatProductSymbolic_MPIAIJKokkos_AB(Mat_Product *product, Mat A, Mat B, MatMatStruct_AB *mm)
1241: {
1242: Mat_MPIAIJ *ampi = static_cast<Mat_MPIAIJ *>(A->data);
1243: Mat_MPIAIJ *bmpi = static_cast<Mat_MPIAIJ *>(B->data);
1244: KokkosCsrMatrix Ad, Ao, Bd, Bo;
1246: PetscFunctionBegin;
1247: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->A, &Ad));
1248: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->B, &Ao));
1249: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->A, &Bd));
1250: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->B, &Bo));
1252: // TODO: add command line options to select spgemm algorithms
1253: auto spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_DEFAULT; // default is TPL if enabled, otherwise KK
1255: // CUDA-10.2's spgemm has bugs. We prefer the SpGEMMreuse APIs introduced in cuda-11.4
1256: #if defined(KOKKOSKERNELS_ENABLE_TPL_CUSPARSE)
1257: #if PETSC_PKG_CUDA_VERSION_LT(11, 4, 0)
1258: spgemm_alg = KokkosSparse::SPGEMMAlgorithm::SPGEMM_KK;
1259: #endif
1260: #endif
1262: mm->kh1.create_spgemm_handle(spgemm_alg);
1263: mm->kh2.create_spgemm_handle(spgemm_alg);
1264: mm->kh3.create_spgemm_handle(spgemm_alg);
1265: mm->kh4.create_spgemm_handle(spgemm_alg);
1267: // Bcast B's rows to form F, and overlap the communication
1268: PetscIntKokkosViewHost map_h(NoInit("map_h"), bmpi->B->cmap->n);
1269: PetscCall(MatMPIAIJKokkosBcastBegin(B, ampi->Mvctx, MAT_INITIAL_MATRIX, map_h.data(), mm));
1271: // A's diag * (B's diag + B's off-diag)
1272: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh1, Ad, false, Bd, false, mm->C1));
1273: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh2, Ad, false, Bo, false, mm->C2_mid)); // C2 aliases with C2_mid, except with new column indices
1274: // KK spgemm_symbolic() only populates the result's row map, but not its columns.
1275: // TODO: Remove the fake spgemm_numeric() after KK fixed this problem.
1276: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh1, Ad, false, Bd, false, mm->C1));
1277: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh2, Ad, false, Bo, false, mm->C2_mid));
1278: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
1279: PetscCallCXX(sort_crs_matrix(mm->C1));
1280: PetscCallCXX(sort_crs_matrix(mm->C2_mid));
1281: #endif
1283: PetscCall(MatMPIAIJKokkosBcastEnd(B, ampi->Mvctx, MAT_INITIAL_MATRIX, map_h.data(), mm));
1285: // A's off-diag * (F's diag + F's off-diag)
1286: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh3, Ao, false, mm->Fd, false, mm->C3));
1287: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh3, Ao, false, mm->Fd, false, mm->C3));
1288: PetscCallCXX(KokkosSparse::spgemm_symbolic(mm->kh4, Ao, false, mm->Fo, false, mm->C4));
1289: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh4, Ao, false, mm->Fo, false, mm->C4));
1290: #if PETSC_PKG_KOKKOS_KERNELS_VERSION_LT(4, 0, 0)
1291: PetscCallCXX(sort_crs_matrix(mm->C3));
1292: PetscCallCXX(sort_crs_matrix(mm->C4));
1293: #endif
1295: // Create C2, which shares a, i arrays with C2_mid, but with new column indices and potentially larger column size
1296: MatColIdxKokkosView oldj = mm->C2_mid.graph.entries, newj(NoInit("j"), oldj.extent(0));
1297: PetscIntKokkosView map = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), map_h);
1298: PetscCallCXX(Kokkos::parallel_for(Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, oldj.extent(0)), KOKKOS_LAMBDA(const PetscInt i) { newj(i) = map(oldj(i)); }));
1299: 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);
1301: // C = (Cd, Co) = (C1+C3, C2+C4)
1302: mm->kh1.create_spadd_handle(true); // C1, C3 are sorted
1303: mm->kh2.create_spadd_handle(true); // C2, C4 are sorted
1304: PetscCallCXX(KokkosSparse::spadd_symbolic(&mm->kh1, mm->C1, mm->C3, mm->Cd));
1305: PetscCallCXX(KokkosSparse::spadd_symbolic(&mm->kh2, mm->C2, mm->C4, mm->Co));
1306: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh1, 1.0, mm->C1, 1.0, mm->C3, mm->Cd));
1307: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh2, 1.0, mm->C2, 1.0, mm->C4, mm->Co));
1308: PetscFunctionReturn(PETSC_SUCCESS);
1309: }
1311: static PetscErrorCode MatProductNumeric_MPIAIJKokkos_AB(Mat_Product *product, Mat A, Mat B, MatMatStruct_AB *mm)
1312: {
1313: Mat_MPIAIJ *ampi = static_cast<Mat_MPIAIJ *>(A->data);
1314: Mat_MPIAIJ *bmpi = static_cast<Mat_MPIAIJ *>(B->data);
1315: KokkosCsrMatrix Ad, Ao, Bd, Bo;
1317: PetscFunctionBegin;
1318: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->A, &Ad));
1319: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(ampi->B, &Ao));
1320: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->A, &Bd));
1321: PetscCall(MatSeqAIJKokkosGetKokkosCsrMatrix(bmpi->B, &Bo));
1323: // Bcast B's rows to form F, and overlap the communication
1324: PetscCall(MatMPIAIJKokkosBcastBegin(B, NULL, MAT_REUSE_MATRIX, NULL, mm));
1326: // A's diag * (B's diag + B's off-diag)
1327: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh1, Ad, false, Bd, false, mm->C1));
1328: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh2, Ad, false, Bo, false, mm->C2_mid));
1330: PetscCall(MatMPIAIJKokkosBcastEnd(B, NULL, MAT_REUSE_MATRIX, NULL, mm));
1332: // A's off-diag * (F's diag + F's off-diag)
1333: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh3, Ao, false, mm->Fd, false, mm->C3));
1334: PetscCallCXX(KokkosSparse::spgemm_numeric(mm->kh4, Ao, false, mm->Fo, false, mm->C4));
1336: // C = (Cd, Co) = (C1+C3, C2+C4)
1337: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh1, 1.0, mm->C1, 1.0, mm->C3, mm->Cd));
1338: PetscCallCXX(KokkosSparse::spadd_numeric(&mm->kh2, 1.0, mm->C2, 1.0, mm->C4, mm->Co));
1339: PetscFunctionReturn(PETSC_SUCCESS);
1340: }
1342: static PetscErrorCode MatProductNumeric_MPIAIJKokkos(Mat C)
1343: {
1344: Mat_MPIAIJ *cmpi = static_cast<Mat_MPIAIJ *>(C->data);
1345: Mat_Product *product;
1346: MatProductData_MPIAIJKokkos *pdata;
1347: MatProductType ptype;
1348: Mat A, B;
1350: PetscFunctionBegin;
1351: MatCheckProduct(C, 1); // make sure C is a product
1352: product = C->product;
1353: pdata = static_cast<MatProductData_MPIAIJKokkos *>(product->data);
1354: ptype = product->type;
1355: A = product->A;
1356: B = product->B;
1358: // See if numeric has already been done in symbolic (e.g., user calls MatMatMult(A,B,MAT_INITIAL_MATRIX,..,C)).
1359: // If yes, skip the numeric, but reset the flag so that next time when user calls MatMatMult(E,F,MAT_REUSE_MATRIX,..,C),
1360: // we still do numeric.
1361: if (pdata->reusesym) { // numeric reuses results from symbolic
1362: pdata->reusesym = PETSC_FALSE;
1363: PetscFunctionReturn(PETSC_SUCCESS);
1364: }
1366: if (ptype == MATPRODUCT_AB) {
1367: PetscCall(MatProductNumeric_MPIAIJKokkos_AB(product, A, B, pdata->mmAB));
1368: } else if (ptype == MATPRODUCT_AtB) {
1369: PetscCall(MatProductNumeric_MPIAIJKokkos_AtB(product, A, B, pdata->mmAtB));
1370: } else if (ptype == MATPRODUCT_PtAP) { // BtAB, computed by Z = AB; C= BtZ
1371: PetscCall(MatProductNumeric_MPIAIJKokkos_AB(product, A, B, pdata->mmAB));
1372: PetscCall(MatProductNumeric_MPIAIJKokkos_AtB(product, B, pdata->Z, pdata->mmAtB));
1373: }
1375: PetscCall(MatSeqAIJKokkosModifyDevice(cmpi->A)); // mark that A, B on device are modified
1376: PetscCall(MatSeqAIJKokkosModifyDevice(cmpi->B));
1377: PetscFunctionReturn(PETSC_SUCCESS);
1378: }
1380: static PetscErrorCode MatProductSymbolic_MPIAIJKokkos(Mat C)
1381: {
1382: Mat A, B;
1383: Mat_Product *product;
1384: MatProductType ptype;
1385: MatProductData_MPIAIJKokkos *pdata;
1386: MatMatStruct *mm = NULL;
1387: PetscInt m, n, M, N;
1388: Mat Cd, Co;
1389: MPI_Comm comm;
1391: PetscFunctionBegin;
1392: PetscCall(PetscObjectGetComm((PetscObject)C, &comm));
1393: MatCheckProduct(C, 1);
1394: product = C->product;
1395: PetscCheck(!product->data, comm, PETSC_ERR_PLIB, "Product data not empty");
1396: ptype = product->type;
1397: A = product->A;
1398: B = product->B;
1400: switch (ptype) {
1401: case MATPRODUCT_AB:
1402: m = A->rmap->n;
1403: n = B->cmap->n;
1404: M = A->rmap->N;
1405: N = B->cmap->N;
1406: break;
1407: case MATPRODUCT_AtB:
1408: m = A->cmap->n;
1409: n = B->cmap->n;
1410: M = A->cmap->N;
1411: N = B->cmap->N;
1412: break;
1413: case MATPRODUCT_PtAP:
1414: m = B->cmap->n;
1415: n = B->cmap->n;
1416: M = B->cmap->N;
1417: N = B->cmap->N;
1418: break; /* BtAB */
1419: default:
1420: SETERRQ(comm, PETSC_ERR_PLIB, "Not for product type %s", MatProductTypes[ptype]);
1421: }
1423: PetscCall(MatSetSizes(C, m, n, M, N));
1424: PetscCall(PetscLayoutSetUp(C->rmap));
1425: PetscCall(PetscLayoutSetUp(C->cmap));
1426: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
1428: pdata = new MatProductData_MPIAIJKokkos();
1429: pdata->reusesym = product->api_user;
1431: if (ptype == MATPRODUCT_AB) {
1432: auto mmAB = new MatMatStruct_AB();
1433: PetscCall(MatProductSymbolic_MPIAIJKokkos_AB(product, A, B, mmAB));
1434: mm = pdata->mmAB = mmAB;
1435: } else if (ptype == MATPRODUCT_AtB) {
1436: auto mmAtB = new MatMatStruct_AtB();
1437: PetscCall(MatProductSymbolic_MPIAIJKokkos_AtB(product, A, B, mmAtB));
1438: mm = pdata->mmAtB = mmAtB;
1439: } else if (ptype == MATPRODUCT_PtAP) { // C = BtAB, computed as Z = AB; C= BtZ
1440: Mat Zd, Zo, Z; // Zd, Zo are owned by pdata->Z
1442: auto mmAB = new MatMatStruct_AB();
1443: PetscCall(MatProductSymbolic_MPIAIJKokkos_AB(product, A, B, mmAB)); // Z stored as mmAB->{Cd, Co}
1444: PetscCall(MatCreateSeqAIJKokkosWithKokkosCsrMatrix(PETSC_COMM_SELF, mmAB->Cd, &Zd));
1445: PetscCall(MatCreateSeqAIJKokkosWithKokkosCsrMatrix(PETSC_COMM_SELF, mmAB->Co, &Zo));
1446: pdata->mmAB = mmAB;
1448: m = A->rmap->n; // Z's layout
1449: n = B->cmap->n;
1450: M = A->rmap->N;
1451: N = B->cmap->N;
1452: PetscCall(MatCreate(comm, &Z));
1453: PetscCall(MatSetSizes(Z, m, n, M, N));
1454: PetscCall(PetscLayoutSetUp(Z->rmap));
1455: PetscCall(PetscLayoutSetUp(Z->cmap));
1456: PetscCall(MatSetType(Z, MATMPIAIJKOKKOS));
1457: PetscCall(MatSetMPIAIJKokkosWithSplitSeqAIJKokkosMatrices(Z, Zd, Zo, mmAB->garray));
1459: auto mmAtB = new MatMatStruct_AtB();
1460: PetscCall(MatProductSymbolic_MPIAIJKokkos_AtB(product, B, Z, mmAtB)); // final result C stored as mmAtB->{Cd, Co}
1462: pdata->Z = Z; // give ownership to pdata
1463: mm = pdata->mmAtB = mmAtB;
1464: }
1466: PetscCall(MatCreateSeqAIJKokkosWithKokkosCsrMatrix(PETSC_COMM_SELF, mm->Cd, &Cd));
1467: PetscCall(MatCreateSeqAIJKokkosWithKokkosCsrMatrix(PETSC_COMM_SELF, mm->Co, &Co));
1468: PetscCall(MatSetMPIAIJKokkosWithSplitSeqAIJKokkosMatrices(C, Cd, Co, mm->garray));
1470: C->product->data = pdata;
1471: C->product->destroy = MatProductDataDestroy_MPIAIJKokkos;
1472: C->ops->productnumeric = MatProductNumeric_MPIAIJKokkos;
1473: PetscFunctionReturn(PETSC_SUCCESS);
1474: }
1476: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_MPIAIJKokkos(Mat mat)
1477: {
1478: Mat_Product *product = mat->product;
1479: PetscBool match = PETSC_FALSE;
1480: PetscBool usecpu = PETSC_FALSE;
1482: PetscFunctionBegin;
1483: MatCheckProduct(mat, 1);
1484: if (!product->A->boundtocpu && !product->B->boundtocpu) PetscCall(PetscObjectTypeCompare((PetscObject)product->B, ((PetscObject)product->A)->type_name, &match));
1485: if (match) { /* we can always fallback to the CPU if requested */
1486: switch (product->type) {
1487: case MATPRODUCT_AB:
1488: if (product->api_user) {
1489: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatMatMult", "Mat");
1490: PetscCall(PetscOptionsBool("-matmatmult_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
1491: PetscOptionsEnd();
1492: } else {
1493: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AB", "Mat");
1494: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatMatMult", usecpu, &usecpu, NULL));
1495: PetscOptionsEnd();
1496: }
1497: break;
1498: case MATPRODUCT_AtB:
1499: if (product->api_user) {
1500: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatTransposeMatMult", "Mat");
1501: PetscCall(PetscOptionsBool("-mattransposematmult_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
1502: PetscOptionsEnd();
1503: } else {
1504: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_AtB", "Mat");
1505: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatTransposeMatMult", usecpu, &usecpu, NULL));
1506: PetscOptionsEnd();
1507: }
1508: break;
1509: case MATPRODUCT_PtAP:
1510: if (product->api_user) {
1511: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatPtAP", "Mat");
1512: PetscCall(PetscOptionsBool("-matptap_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
1513: PetscOptionsEnd();
1514: } else {
1515: PetscOptionsBegin(PetscObjectComm((PetscObject)mat), ((PetscObject)mat)->prefix, "MatProduct_PtAP", "Mat");
1516: PetscCall(PetscOptionsBool("-mat_product_algorithm_backend_cpu", "Use CPU code", "MatPtAP", usecpu, &usecpu, NULL));
1517: PetscOptionsEnd();
1518: }
1519: break;
1520: default:
1521: break;
1522: }
1523: match = (PetscBool)!usecpu;
1524: }
1525: if (match) {
1526: switch (product->type) {
1527: case MATPRODUCT_AB:
1528: case MATPRODUCT_AtB:
1529: case MATPRODUCT_PtAP:
1530: mat->ops->productsymbolic = MatProductSymbolic_MPIAIJKokkos;
1531: break;
1532: default:
1533: break;
1534: }
1535: }
1536: /* fallback to MPIAIJ ops */
1537: if (!mat->ops->productsymbolic) PetscCall(MatProductSetFromOptions_MPIAIJ(mat));
1538: PetscFunctionReturn(PETSC_SUCCESS);
1539: }
1541: // Mirror of MatCOOStruct_MPIAIJ on device
1542: struct MatCOOStruct_MPIAIJKokkos {
1543: PetscCount n;
1544: PetscSF sf;
1545: PetscCount Annz, Bnnz;
1546: PetscCount Annz2, Bnnz2;
1547: PetscCountKokkosView Ajmap1, Aperm1;
1548: PetscCountKokkosView Bjmap1, Bperm1;
1549: PetscCountKokkosView Aimap2, Ajmap2, Aperm2;
1550: PetscCountKokkosView Bimap2, Bjmap2, Bperm2;
1551: PetscCountKokkosView Cperm1;
1552: MatScalarKokkosView sendbuf, recvbuf;
1554: MatCOOStruct_MPIAIJKokkos(const MatCOOStruct_MPIAIJ *coo_h) :
1555: n(coo_h->n),
1556: sf(coo_h->sf),
1557: Annz(coo_h->Annz),
1558: Bnnz(coo_h->Bnnz),
1559: Annz2(coo_h->Annz2),
1560: Bnnz2(coo_h->Bnnz2),
1561: Ajmap1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Ajmap1, coo_h->Annz + 1))),
1562: Aperm1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Aperm1, coo_h->Atot1))),
1563: Bjmap1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bjmap1, coo_h->Bnnz + 1))),
1564: Bperm1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bperm1, coo_h->Btot1))),
1565: Aimap2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Aimap2, coo_h->Annz2))),
1566: Ajmap2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Ajmap2, coo_h->Annz2 + 1))),
1567: Aperm2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Aperm2, coo_h->Atot2))),
1568: Bimap2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bimap2, coo_h->Bnnz2))),
1569: Bjmap2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bjmap2, coo_h->Bnnz2 + 1))),
1570: Bperm2(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Bperm2, coo_h->Btot2))),
1571: Cperm1(Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), PetscCountKokkosViewHost(coo_h->Cperm1, coo_h->sendlen))),
1572: sendbuf(Kokkos::create_mirror_view(Kokkos::WithoutInitializing, DefaultMemorySpace(), MatScalarKokkosViewHost(coo_h->sendbuf, coo_h->sendlen))),
1573: recvbuf(Kokkos::create_mirror_view(Kokkos::WithoutInitializing, DefaultMemorySpace(), MatScalarKokkosViewHost(coo_h->recvbuf, coo_h->recvlen)))
1574: {
1575: PetscCallVoid(PetscObjectReference((PetscObject)sf));
1576: }
1578: ~MatCOOStruct_MPIAIJKokkos() { PetscCallVoid(PetscSFDestroy(&sf)); }
1579: };
1581: static PetscErrorCode MatCOOStructDestroy_MPIAIJKokkos(void *data)
1582: {
1583: PetscFunctionBegin;
1584: PetscCallCXX(delete static_cast<MatCOOStruct_MPIAIJKokkos *>(data));
1585: PetscFunctionReturn(PETSC_SUCCESS);
1586: }
1588: static PetscErrorCode MatSetPreallocationCOO_MPIAIJKokkos(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
1589: {
1590: PetscContainer container_h, container_d;
1591: MatCOOStruct_MPIAIJ *coo_h;
1592: MatCOOStruct_MPIAIJKokkos *coo_d;
1594: PetscFunctionBegin;
1595: PetscCall(MatSetPreallocationCOO_MPIAIJ(mat, coo_n, coo_i, coo_j)); /* mpiaij->A,B's type is set to seqaijkokkos */
1596: mat->preallocated = PETSC_TRUE;
1597: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
1598: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
1599: PetscCall(MatZeroEntries(mat));
1601: // Copy the COO struct to device
1602: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container_h));
1603: PetscCall(PetscContainerGetPointer(container_h, (void **)&coo_h));
1604: PetscCallCXX(coo_d = new MatCOOStruct_MPIAIJKokkos(coo_h));
1606: // Put the COO struct in a container and then attach that to the matrix
1607: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container_d));
1608: PetscCall(PetscContainerSetPointer(container_d, coo_d));
1609: PetscCall(PetscContainerSetUserDestroy(container_d, MatCOOStructDestroy_MPIAIJKokkos));
1610: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Device", (PetscObject)container_d));
1611: PetscCall(PetscContainerDestroy(&container_d));
1612: PetscFunctionReturn(PETSC_SUCCESS);
1613: }
1615: static PetscErrorCode MatSetValuesCOO_MPIAIJKokkos(Mat mat, const PetscScalar v[], InsertMode imode)
1616: {
1617: Mat_MPIAIJ *mpiaij = static_cast<Mat_MPIAIJ *>(mat->data);
1618: Mat A = mpiaij->A, B = mpiaij->B;
1619: MatScalarKokkosView Aa, Ba;
1620: MatScalarKokkosView v1;
1621: PetscMemType memtype;
1622: PetscContainer container;
1623: MatCOOStruct_MPIAIJKokkos *coo;
1625: PetscFunctionBegin;
1626: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", (PetscObject *)&container));
1627: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
1629: const auto &n = coo->n;
1630: const auto &Annz = coo->Annz;
1631: const auto &Annz2 = coo->Annz2;
1632: const auto &Bnnz = coo->Bnnz;
1633: const auto &Bnnz2 = coo->Bnnz2;
1634: const auto &vsend = coo->sendbuf;
1635: const auto &v2 = coo->recvbuf;
1636: const auto &Ajmap1 = coo->Ajmap1;
1637: const auto &Ajmap2 = coo->Ajmap2;
1638: const auto &Aimap2 = coo->Aimap2;
1639: const auto &Bjmap1 = coo->Bjmap1;
1640: const auto &Bjmap2 = coo->Bjmap2;
1641: const auto &Bimap2 = coo->Bimap2;
1642: const auto &Aperm1 = coo->Aperm1;
1643: const auto &Aperm2 = coo->Aperm2;
1644: const auto &Bperm1 = coo->Bperm1;
1645: const auto &Bperm2 = coo->Bperm2;
1646: const auto &Cperm1 = coo->Cperm1;
1648: PetscCall(PetscGetMemType(v, &memtype)); /* Return PETSC_MEMTYPE_HOST when v is NULL */
1649: if (PetscMemTypeHost(memtype)) { /* If user gave v[] in host, we need to copy it to device if any */
1650: v1 = Kokkos::create_mirror_view_and_copy(DefaultMemorySpace(), MatScalarKokkosViewHost((PetscScalar *)v, n));
1651: } else {
1652: v1 = MatScalarKokkosView((PetscScalar *)v, n); /* Directly use v[]'s memory */
1653: }
1655: if (imode == INSERT_VALUES) {
1656: PetscCall(MatSeqAIJGetKokkosViewWrite(A, &Aa)); /* write matrix values */
1657: PetscCall(MatSeqAIJGetKokkosViewWrite(B, &Ba));
1658: } else {
1659: PetscCall(MatSeqAIJGetKokkosView(A, &Aa)); /* read & write matrix values */
1660: PetscCall(MatSeqAIJGetKokkosView(B, &Ba));
1661: }
1663: PetscCall(PetscLogGpuTimeBegin());
1664: /* Pack entries to be sent to remote */
1665: Kokkos::parallel_for(Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, vsend.extent(0)), KOKKOS_LAMBDA(const PetscCount i) { vsend(i) = v1(Cperm1(i)); });
1667: /* Send remote entries to their owner and overlap the communication with local computation */
1668: PetscCall(PetscSFReduceWithMemTypeBegin(coo->sf, MPIU_SCALAR, PETSC_MEMTYPE_KOKKOS, vsend.data(), PETSC_MEMTYPE_KOKKOS, v2.data(), MPI_REPLACE));
1669: /* Add local entries to A and B in one kernel */
1670: Kokkos::parallel_for(
1671: Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, Annz + Bnnz), KOKKOS_LAMBDA(PetscCount i) {
1672: PetscScalar sum = 0.0;
1673: if (i < Annz) {
1674: for (PetscCount k = Ajmap1(i); k < Ajmap1(i + 1); k++) sum += v1(Aperm1(k));
1675: Aa(i) = (imode == INSERT_VALUES ? 0.0 : Aa(i)) + sum;
1676: } else {
1677: i -= Annz;
1678: for (PetscCount k = Bjmap1(i); k < Bjmap1(i + 1); k++) sum += v1(Bperm1(k));
1679: Ba(i) = (imode == INSERT_VALUES ? 0.0 : Ba(i)) + sum;
1680: }
1681: });
1682: PetscCall(PetscSFReduceEnd(coo->sf, MPIU_SCALAR, vsend.data(), v2.data(), MPI_REPLACE));
1684: /* Add received remote entries to A and B in one kernel */
1685: Kokkos::parallel_for(
1686: Kokkos::RangePolicy<>(PetscGetKokkosExecutionSpace(), 0, Annz2 + Bnnz2), KOKKOS_LAMBDA(PetscCount i) {
1687: if (i < Annz2) {
1688: for (PetscCount k = Ajmap2(i); k < Ajmap2(i + 1); k++) Aa(Aimap2(i)) += v2(Aperm2(k));
1689: } else {
1690: i -= Annz2;
1691: for (PetscCount k = Bjmap2(i); k < Bjmap2(i + 1); k++) Ba(Bimap2(i)) += v2(Bperm2(k));
1692: }
1693: });
1694: PetscCall(PetscLogGpuTimeEnd());
1696: if (imode == INSERT_VALUES) {
1697: PetscCall(MatSeqAIJRestoreKokkosViewWrite(A, &Aa)); /* Increase A & B's state etc. */
1698: PetscCall(MatSeqAIJRestoreKokkosViewWrite(B, &Ba));
1699: } else {
1700: PetscCall(MatSeqAIJRestoreKokkosView(A, &Aa));
1701: PetscCall(MatSeqAIJRestoreKokkosView(B, &Ba));
1702: }
1703: PetscFunctionReturn(PETSC_SUCCESS);
1704: }
1706: static PetscErrorCode MatDestroy_MPIAIJKokkos(Mat A)
1707: {
1708: PetscFunctionBegin;
1709: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMPIAIJSetPreallocation_C", NULL));
1710: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatMPIAIJGetLocalMatMerge_C", NULL));
1711: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1712: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1713: PetscCall(MatDestroy_MPIAIJ(A));
1714: PetscFunctionReturn(PETSC_SUCCESS);
1715: }
1717: static PetscErrorCode MatShift_MPIAIJKokkos(Mat A, PetscScalar a)
1718: {
1719: Mat_MPIAIJ *mpiaij = static_cast<Mat_MPIAIJ *>(A->data);
1720: PetscBool congruent;
1722: PetscFunctionBegin;
1723: PetscCall(MatHasCongruentLayouts(A, &congruent));
1724: if (congruent) { // square matrix and the diagonals are solely in the diag block
1725: PetscCall(MatShift(mpiaij->A, a));
1726: } else { // too hard, use the general version
1727: PetscCall(MatShift_Basic(A, a));
1728: }
1729: PetscFunctionReturn(PETSC_SUCCESS);
1730: }
1732: static PetscErrorCode MatSetOps_MPIAIJKokkos(Mat B)
1733: {
1734: PetscFunctionBegin;
1735: B->ops->assemblyend = MatAssemblyEnd_MPIAIJKokkos;
1736: B->ops->mult = MatMult_MPIAIJKokkos;
1737: B->ops->multadd = MatMultAdd_MPIAIJKokkos;
1738: B->ops->multtranspose = MatMultTranspose_MPIAIJKokkos;
1739: B->ops->productsetfromoptions = MatProductSetFromOptions_MPIAIJKokkos;
1740: B->ops->destroy = MatDestroy_MPIAIJKokkos;
1741: B->ops->shift = MatShift_MPIAIJKokkos;
1743: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJSetPreallocation_C", MatMPIAIJSetPreallocation_MPIAIJKokkos));
1744: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatMPIAIJGetLocalMatMerge_C", MatMPIAIJGetLocalMatMerge_MPIAIJKokkos));
1745: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_MPIAIJKokkos));
1746: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_MPIAIJKokkos));
1747: PetscFunctionReturn(PETSC_SUCCESS);
1748: }
1750: PETSC_INTERN PetscErrorCode MatConvert_MPIAIJ_MPIAIJKokkos(Mat A, MatType mtype, MatReuse reuse, Mat *newmat)
1751: {
1752: Mat B;
1753: Mat_MPIAIJ *a;
1755: PetscFunctionBegin;
1756: if (reuse == MAT_INITIAL_MATRIX) {
1757: PetscCall(MatDuplicate(A, MAT_COPY_VALUES, newmat));
1758: } else if (reuse == MAT_REUSE_MATRIX) {
1759: PetscCall(MatCopy(A, *newmat, SAME_NONZERO_PATTERN));
1760: }
1761: B = *newmat;
1763: B->boundtocpu = PETSC_FALSE;
1764: PetscCall(PetscFree(B->defaultvectype));
1765: PetscCall(PetscStrallocpy(VECKOKKOS, &B->defaultvectype));
1766: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATMPIAIJKOKKOS));
1768: a = static_cast<Mat_MPIAIJ *>(A->data);
1769: if (a->A) PetscCall(MatSetType(a->A, MATSEQAIJKOKKOS));
1770: if (a->B) PetscCall(MatSetType(a->B, MATSEQAIJKOKKOS));
1771: if (a->lvec) PetscCall(VecSetType(a->lvec, VECSEQKOKKOS));
1772: PetscCall(MatSetOps_MPIAIJKokkos(B));
1773: PetscFunctionReturn(PETSC_SUCCESS);
1774: }
1776: /*MC
1777: MATAIJKOKKOS - "mpiaijkokkos", a matrix type to be used for CSR sparse matrices with Kokkos
1779: A matrix type using Kokkos-Kernels CrsMatrix type for portability across different device types
1781: Options Database Key:
1782: . -mat_type aijkokkos - sets the matrix type to `MATAIJKOKKOS`
1784: Level: beginner
1786: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJKokkos()`, `MATSEQAIJKOKKOS`, `MATSEQAIJ`, `MATMPIAIJ`
1787: M*/
1788: PETSC_EXTERN PetscErrorCode MatCreate_MPIAIJKokkos(Mat A)
1789: {
1790: PetscFunctionBegin;
1791: PetscCall(PetscKokkosInitializeCheck());
1792: PetscCall(MatCreate_MPIAIJ(A));
1793: PetscCall(MatConvert_MPIAIJ_MPIAIJKokkos(A, MATMPIAIJKOKKOS, MAT_INPLACE_MATRIX, &A));
1794: PetscFunctionReturn(PETSC_SUCCESS);
1795: }
1797: /*@C
1798: MatCreateAIJKokkos - Creates a sparse matrix in `MATAIJKOKOS` (compressed row) format
1799: (the default parallel PETSc format). This matrix will ultimately pushed down
1800: to Kokkos for calculations.
1802: Collective
1804: Input Parameters:
1805: + comm - MPI communicator, set to `PETSC_COMM_SELF`
1806: . m - number of local rows (or `PETSC_DECIDE` to have calculated if `M` is given)
1807: This value should be the same as the local size used in creating the
1808: y vector for the matrix-vector product y = Ax.
1809: . n - This value should be the same as the local size used in creating the
1810: x vector for the matrix-vector product y = Ax. (or `PETSC_DECIDE` to have
1811: calculated if N is given) For square matrices n is almost always `m`.
1812: . M - number of global rows (or `PETSC_DETERMINE` to have calculated if `m` is given)
1813: . N - number of global columns (or `PETSC_DETERMINE` to have calculated if `n` is given)
1814: . d_nz - number of nonzeros per row in DIAGONAL portion of local submatrix
1815: (same value is used for all local rows)
1816: . d_nnz - array containing the number of nonzeros in the various rows of the
1817: DIAGONAL portion of the local submatrix (possibly different for each row)
1818: or `NULL`, if `d_nz` is used to specify the nonzero structure.
1819: The size of this array is equal to the number of local rows, i.e `m`.
1820: For matrices you plan to factor you must leave room for the diagonal entry and
1821: put in the entry even if it is zero.
1822: . o_nz - number of nonzeros per row in the OFF-DIAGONAL portion of local
1823: submatrix (same value is used for all local rows).
1824: - o_nnz - array containing the number of nonzeros in the various rows of the
1825: OFF-DIAGONAL portion of the local submatrix (possibly different for
1826: each row) or `NULL`, if `o_nz` is used to specify the nonzero
1827: structure. The size of this array is equal to the number
1828: of local rows, i.e `m`.
1830: Output Parameter:
1831: . A - the matrix
1833: Level: intermediate
1835: Notes:
1836: It is recommended that one use the `MatCreate()`, `MatSetType()` and/or `MatSetFromOptions()`,
1837: MatXXXXSetPreallocation() paradigm instead of this routine directly.
1838: [MatXXXXSetPreallocation() is, for example, `MatSeqAIJSetPreallocation()`]
1840: The AIJ format, also called compressed row storage), is fully compatible with standard Fortran
1841: storage. That is, the stored row and column indices can begin at
1842: either one (as in Fortran) or zero.
1844: .seealso: [](ch_matrices), `Mat`, `MATAIJKOKOS`, `MATSEQAIJKOKOS`, `MATMPIAIJKOKOS`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`,
1845: `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MATMPIAIJKOKKOS`, `MATAIJKOKKOS`
1846: @*/
1847: 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)
1848: {
1849: PetscMPIInt size;
1851: PetscFunctionBegin;
1852: PetscCall(MatCreate(comm, A));
1853: PetscCall(MatSetSizes(*A, m, n, M, N));
1854: PetscCallMPI(MPI_Comm_size(comm, &size));
1855: if (size > 1) {
1856: PetscCall(MatSetType(*A, MATMPIAIJKOKKOS));
1857: PetscCall(MatMPIAIJSetPreallocation(*A, d_nz, d_nnz, o_nz, o_nnz));
1858: } else {
1859: PetscCall(MatSetType(*A, MATSEQAIJKOKKOS));
1860: PetscCall(MatSeqAIJSetPreallocation(*A, d_nz, d_nnz));
1861: }
1862: PetscFunctionReturn(PETSC_SUCCESS);
1863: }