Actual source code: aijperm.c
1: /*
2: Defines basic operations for the MATSEQAIJPERM matrix class.
3: This class is derived from the MATSEQAIJ class and retains the
4: compressed row storage (aka Yale sparse matrix format) but augments
5: it with some permutation information that enables some operations
6: to be more vectorizable. A physically rearranged copy of the matrix
7: may be stored if the user desires.
9: Eventually a variety of permutations may be supported.
10: */
12: #include <../src/mat/impls/aij/seq/aij.h>
14: #if defined(PETSC_USE_AVX512_KERNELS) && defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
15: #include <immintrin.h>
17: #if !defined(_MM_SCALE_8)
18: #define _MM_SCALE_8 8
19: #endif
20: #if !defined(_MM_SCALE_4)
21: #define _MM_SCALE_4 4
22: #endif
23: #endif
25: #define NDIM 512
26: /* NDIM specifies how many rows at a time we should work with when
27: * performing the vectorized mat-vec. This depends on various factors
28: * such as vector register length, etc., and I really need to add a
29: * way for the user (or the library) to tune this. I'm setting it to
30: * 512 for now since that is what Ed D'Azevedo was using in his Fortran
31: * routines. */
33: typedef struct {
34: PetscObjectState nonzerostate; /* used to determine if the nonzero structure has changed and hence the permutations need updating */
36: PetscInt ngroup;
37: PetscInt *xgroup;
38: /* Denotes where groups of rows with same number of nonzeros
39: * begin and end, i.e., xgroup[i] gives us the position in iperm[]
40: * where the ith group begins. */
42: PetscInt *nzgroup; /* how many nonzeros each row that is a member of group i has. */
43: PetscInt *iperm; /* The permutation vector. */
45: /* Some of this stuff is for Ed's recursive triangular solve.
46: * I'm not sure what I need yet. */
47: PetscInt blocksize;
48: PetscInt nstep;
49: PetscInt *jstart_list;
50: PetscInt *jend_list;
51: PetscInt *action_list;
52: PetscInt *ngroup_list;
53: PetscInt **ipointer_list;
54: PetscInt **xgroup_list;
55: PetscInt **nzgroup_list;
56: PetscInt **iperm_list;
57: } Mat_SeqAIJPERM;
59: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJPERM_SeqAIJ(Mat A, MatType type, MatReuse reuse, Mat *newmat)
60: {
61: /* This routine is only called to convert a MATAIJPERM to its base PETSc type, */
62: /* so we will ignore 'MatType type'. */
63: Mat B = *newmat;
64: Mat_SeqAIJPERM *aijperm = (Mat_SeqAIJPERM *)A->spptr;
66: PetscFunctionBegin;
67: if (reuse == MAT_INITIAL_MATRIX) {
68: PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));
69: aijperm = (Mat_SeqAIJPERM *)B->spptr;
70: }
72: /* Reset the original function pointers. */
73: B->ops->assemblyend = MatAssemblyEnd_SeqAIJ;
74: B->ops->destroy = MatDestroy_SeqAIJ;
75: B->ops->duplicate = MatDuplicate_SeqAIJ;
76: B->ops->mult = MatMult_SeqAIJ;
77: B->ops->multadd = MatMultAdd_SeqAIJ;
79: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaijperm_seqaij_C", NULL));
81: /* Free everything in the Mat_SeqAIJPERM data structure.*/
82: PetscCall(PetscFree(aijperm->xgroup));
83: PetscCall(PetscFree(aijperm->nzgroup));
84: PetscCall(PetscFree(aijperm->iperm));
85: PetscCall(PetscFree(B->spptr));
87: /* Change the type of B to MATSEQAIJ. */
88: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
90: *newmat = B;
91: PetscFunctionReturn(PETSC_SUCCESS);
92: }
94: static PetscErrorCode MatDestroy_SeqAIJPERM(Mat A)
95: {
96: Mat_SeqAIJPERM *aijperm = (Mat_SeqAIJPERM *)A->spptr;
98: PetscFunctionBegin;
99: if (aijperm) {
100: /* If MatHeaderMerge() was used then this SeqAIJPERM matrix will not have a spprt. */
101: PetscCall(PetscFree(aijperm->xgroup));
102: PetscCall(PetscFree(aijperm->nzgroup));
103: PetscCall(PetscFree(aijperm->iperm));
104: PetscCall(PetscFree(A->spptr));
105: }
106: /* Change the type of A back to SEQAIJ and use MatDestroy_SeqAIJ()
107: * to destroy everything that remains. */
108: PetscCall(PetscObjectChangeTypeName((PetscObject)A, MATSEQAIJ));
109: /* Note that I don't call MatSetType(). I believe this is because that
110: * is only to be called when *building* a matrix. I could be wrong, but
111: * that is how things work for the SuperLU matrix class. */
112: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
113: PetscCall(MatDestroy_SeqAIJ(A));
114: PetscFunctionReturn(PETSC_SUCCESS);
115: }
117: static PetscErrorCode MatDuplicate_SeqAIJPERM(Mat A, MatDuplicateOption op, Mat *M)
118: {
119: Mat_SeqAIJPERM *aijperm = (Mat_SeqAIJPERM *)A->spptr;
120: Mat_SeqAIJPERM *aijperm_dest;
121: PetscBool perm;
123: PetscFunctionBegin;
124: PetscCall(MatDuplicate_SeqAIJ(A, op, M));
125: PetscCall(PetscObjectTypeCompare((PetscObject)*M, MATSEQAIJPERM, &perm));
126: if (perm) {
127: aijperm_dest = (Mat_SeqAIJPERM *)(*M)->spptr;
128: PetscCall(PetscFree(aijperm_dest->xgroup));
129: PetscCall(PetscFree(aijperm_dest->nzgroup));
130: PetscCall(PetscFree(aijperm_dest->iperm));
131: } else {
132: PetscCall(PetscNew(&aijperm_dest));
133: (*M)->spptr = (void *)aijperm_dest;
134: PetscCall(PetscObjectChangeTypeName((PetscObject)*M, MATSEQAIJPERM));
135: PetscCall(PetscObjectComposeFunction((PetscObject)*M, "MatConvert_seqaijperm_seqaij_C", MatConvert_SeqAIJPERM_SeqAIJ));
136: }
137: PetscCall(PetscArraycpy(aijperm_dest, aijperm, 1));
138: /* Allocate space for, and copy the grouping and permutation info.
139: * I note that when the groups are initially determined in
140: * MatSeqAIJPERM_create_perm, xgroup and nzgroup may be sized larger than
141: * necessary. But at this point, we know how large they need to be, and
142: * allocate only the necessary amount of memory. So the duplicated matrix
143: * may actually use slightly less storage than the original! */
144: PetscCall(PetscMalloc1(A->rmap->n, &aijperm_dest->iperm));
145: PetscCall(PetscMalloc1(aijperm->ngroup + 1, &aijperm_dest->xgroup));
146: PetscCall(PetscMalloc1(aijperm->ngroup, &aijperm_dest->nzgroup));
147: PetscCall(PetscArraycpy(aijperm_dest->iperm, aijperm->iperm, A->rmap->n));
148: PetscCall(PetscArraycpy(aijperm_dest->xgroup, aijperm->xgroup, aijperm->ngroup + 1));
149: PetscCall(PetscArraycpy(aijperm_dest->nzgroup, aijperm->nzgroup, aijperm->ngroup));
150: PetscFunctionReturn(PETSC_SUCCESS);
151: }
153: static PetscErrorCode MatSeqAIJPERM_create_perm(Mat A)
154: {
155: Mat_SeqAIJ *a = (Mat_SeqAIJ *)(A)->data;
156: Mat_SeqAIJPERM *aijperm = (Mat_SeqAIJPERM *)A->spptr;
157: PetscInt m; /* Number of rows in the matrix. */
158: PetscInt *ia; /* From the CSR representation; points to the beginning of each row. */
159: PetscInt maxnz; /* Maximum number of nonzeros in any row. */
160: PetscInt *rows_in_bucket;
161: /* To construct the permutation, we sort each row into one of maxnz
162: * buckets based on how many nonzeros are in the row. */
163: PetscInt nz;
164: PetscInt *nz_in_row; /* the number of nonzero elements in row k. */
165: PetscInt *ipnz;
166: /* When constructing the iperm permutation vector,
167: * ipnz[nz] is used to point to the next place in the permutation vector
168: * that a row with nz nonzero elements should be placed.*/
169: PetscInt i, ngroup, istart, ipos;
171: PetscFunctionBegin;
172: if (aijperm->nonzerostate == A->nonzerostate) PetscFunctionReturn(PETSC_SUCCESS); /* permutation exists and matches current nonzero structure */
173: aijperm->nonzerostate = A->nonzerostate;
174: /* Free anything previously put in the Mat_SeqAIJPERM data structure. */
175: PetscCall(PetscFree(aijperm->xgroup));
176: PetscCall(PetscFree(aijperm->nzgroup));
177: PetscCall(PetscFree(aijperm->iperm));
179: m = A->rmap->n;
180: ia = a->i;
182: /* Allocate the arrays that will hold the permutation vector. */
183: PetscCall(PetscMalloc1(m, &aijperm->iperm));
185: /* Allocate some temporary work arrays that will be used in
186: * calculating the permutation vector and groupings. */
187: PetscCall(PetscMalloc1(m, &nz_in_row));
189: /* Now actually figure out the permutation and grouping. */
191: /* First pass: Determine number of nonzeros in each row, maximum
192: * number of nonzeros in any row, and how many rows fall into each
193: * "bucket" of rows with same number of nonzeros. */
194: maxnz = 0;
195: for (i = 0; i < m; i++) {
196: nz_in_row[i] = ia[i + 1] - ia[i];
197: if (nz_in_row[i] > maxnz) maxnz = nz_in_row[i];
198: }
199: PetscCall(PetscMalloc1(PetscMax(maxnz, m) + 1, &rows_in_bucket));
200: PetscCall(PetscMalloc1(PetscMax(maxnz, m) + 1, &ipnz));
202: for (i = 0; i <= maxnz; i++) rows_in_bucket[i] = 0;
203: for (i = 0; i < m; i++) {
204: nz = nz_in_row[i];
205: rows_in_bucket[nz]++;
206: }
208: /* Allocate space for the grouping info. There will be at most (maxnz + 1)
209: * groups. (It is maxnz + 1 instead of simply maxnz because there may be
210: * rows with no nonzero elements.) If there are (maxnz + 1) groups,
211: * then xgroup[] must consist of (maxnz + 2) elements, since the last
212: * element of xgroup will tell us where the (maxnz + 1)th group ends.
213: * We allocate space for the maximum number of groups;
214: * that is potentially a little wasteful, but not too much so.
215: * Perhaps I should fix it later. */
216: PetscCall(PetscMalloc1(maxnz + 2, &aijperm->xgroup));
217: PetscCall(PetscMalloc1(maxnz + 1, &aijperm->nzgroup));
219: /* Second pass. Look at what is in the buckets and create the groupings.
220: * Note that it is OK to have a group of rows with no non-zero values. */
221: ngroup = 0;
222: istart = 0;
223: for (i = 0; i <= maxnz; i++) {
224: if (rows_in_bucket[i] > 0) {
225: aijperm->nzgroup[ngroup] = i;
226: aijperm->xgroup[ngroup] = istart;
227: ngroup++;
228: istart += rows_in_bucket[i];
229: }
230: }
232: aijperm->xgroup[ngroup] = istart;
233: aijperm->ngroup = ngroup;
235: /* Now fill in the permutation vector iperm. */
236: ipnz[0] = 0;
237: for (i = 0; i < maxnz; i++) ipnz[i + 1] = ipnz[i] + rows_in_bucket[i];
239: for (i = 0; i < m; i++) {
240: nz = nz_in_row[i];
241: ipos = ipnz[nz];
242: aijperm->iperm[ipos] = i;
243: ipnz[nz]++;
244: }
246: /* Clean up temporary work arrays. */
247: PetscCall(PetscFree(rows_in_bucket));
248: PetscCall(PetscFree(ipnz));
249: PetscCall(PetscFree(nz_in_row));
250: PetscFunctionReturn(PETSC_SUCCESS);
251: }
253: static PetscErrorCode MatAssemblyEnd_SeqAIJPERM(Mat A, MatAssemblyType mode)
254: {
255: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
257: PetscFunctionBegin;
258: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
260: /* Since a MATSEQAIJPERM matrix is really just a MATSEQAIJ with some
261: * extra information, call the AssemblyEnd routine for a MATSEQAIJ.
262: * I'm not sure if this is the best way to do this, but it avoids
263: * a lot of code duplication.
264: * I also note that currently MATSEQAIJPERM doesn't know anything about
265: * the Mat_CompressedRow data structure that SeqAIJ now uses when there
266: * are many zero rows. If the SeqAIJ assembly end routine decides to use
267: * this, this may break things. (Don't know... haven't looked at it.) */
268: a->inode.use = PETSC_FALSE;
269: PetscCall(MatAssemblyEnd_SeqAIJ(A, mode));
271: /* Now calculate the permutation and grouping information. */
272: PetscCall(MatSeqAIJPERM_create_perm(A));
273: PetscFunctionReturn(PETSC_SUCCESS);
274: }
276: static PetscErrorCode MatMult_SeqAIJPERM(Mat A, Vec xx, Vec yy)
277: {
278: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
279: const PetscScalar *x;
280: PetscScalar *y;
281: const MatScalar *aa;
282: const PetscInt *aj, *ai;
283: PetscInt i, j;
284: #if defined(PETSC_USE_AVX512_KERNELS) && defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
285: __m512d vec_x, vec_y, vec_vals;
286: __m256i vec_idx, vec_ipos, vec_j;
287: __mmask8 mask;
288: #endif
290: /* Variables that don't appear in MatMult_SeqAIJ. */
291: Mat_SeqAIJPERM *aijperm = (Mat_SeqAIJPERM *)A->spptr;
292: PetscInt *iperm; /* Points to the permutation vector. */
293: PetscInt *xgroup;
294: /* Denotes where groups of rows with same number of nonzeros
295: * begin and end in iperm. */
296: PetscInt *nzgroup;
297: PetscInt ngroup;
298: PetscInt igroup;
299: PetscInt jstart, jend;
300: /* jstart is used in loops to denote the position in iperm where a
301: * group starts; jend denotes the position where it ends.
302: * (jend + 1 is where the next group starts.) */
303: PetscInt iold, nz;
304: PetscInt istart, iend, isize;
305: PetscInt ipos;
306: PetscScalar yp[NDIM];
307: PetscInt ip[NDIM]; /* yp[] and ip[] are treated as vector "registers" for performing the mat-vec. */
309: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
310: #pragma disjoint(*x, *y, *aa)
311: #endif
313: PetscFunctionBegin;
314: PetscCall(VecGetArrayRead(xx, &x));
315: PetscCall(VecGetArray(yy, &y));
316: aj = a->j; /* aj[k] gives column index for element aa[k]. */
317: aa = a->a; /* Nonzero elements stored row-by-row. */
318: ai = a->i; /* ai[k] is the position in aa and aj where row k starts. */
320: /* Get the info we need about the permutations and groupings. */
321: iperm = aijperm->iperm;
322: ngroup = aijperm->ngroup;
323: xgroup = aijperm->xgroup;
324: nzgroup = aijperm->nzgroup;
326: for (igroup = 0; igroup < ngroup; igroup++) {
327: jstart = xgroup[igroup];
328: jend = xgroup[igroup + 1] - 1;
329: nz = nzgroup[igroup];
331: /* Handle the special cases where the number of nonzeros per row
332: * in the group is either 0 or 1. */
333: if (nz == 0) {
334: for (i = jstart; i <= jend; i++) y[iperm[i]] = 0.0;
335: } else if (nz == 1) {
336: for (i = jstart; i <= jend; i++) {
337: iold = iperm[i];
338: ipos = ai[iold];
339: y[iold] = aa[ipos] * x[aj[ipos]];
340: }
341: } else {
342: /* We work our way through the current group in chunks of NDIM rows
343: * at a time. */
345: for (istart = jstart; istart <= jend; istart += NDIM) {
346: /* Figure out where the chunk of 'isize' rows ends in iperm.
347: * 'isize may of course be less than NDIM for the last chunk. */
348: iend = istart + (NDIM - 1);
350: if (iend > jend) iend = jend;
352: isize = iend - istart + 1;
354: /* Initialize the yp[] array that will be used to hold part of
355: * the permuted results vector, and figure out where in aa each
356: * row of the chunk will begin. */
357: for (i = 0; i < isize; i++) {
358: iold = iperm[istart + i];
359: /* iold is a row number from the matrix A *before* reordering. */
360: ip[i] = ai[iold];
361: /* ip[i] tells us where the ith row of the chunk begins in aa. */
362: yp[i] = (PetscScalar)0.0;
363: }
365: /* If the number of zeros per row exceeds the number of rows in
366: * the chunk, we should vectorize along nz, that is, perform the
367: * mat-vec one row at a time as in the usual CSR case. */
368: if (nz > isize) {
369: #if defined(PETSC_HAVE_CRAY_VECTOR)
370: #pragma _CRI preferstream
371: #endif
372: for (i = 0; i < isize; i++) {
373: #if defined(PETSC_HAVE_CRAY_VECTOR)
374: #pragma _CRI prefervector
375: #endif
377: #if defined(PETSC_USE_AVX512_KERNELS) && defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
378: vec_y = _mm512_setzero_pd();
379: ipos = ip[i];
380: for (j = 0; j < (nz >> 3); j++) {
381: vec_idx = _mm256_loadu_si256((__m256i const *)&aj[ipos]);
382: vec_vals = _mm512_loadu_pd(&aa[ipos]);
383: vec_x = _mm512_i32gather_pd(vec_idx, x, _MM_SCALE_8);
384: vec_y = _mm512_fmadd_pd(vec_x, vec_vals, vec_y);
385: ipos += 8;
386: }
387: if ((nz & 0x07) > 2) {
388: mask = (__mmask8)(0xff >> (8 - (nz & 0x07)));
389: vec_idx = _mm256_loadu_si256((__m256i const *)&aj[ipos]);
390: vec_vals = _mm512_loadu_pd(&aa[ipos]);
391: vec_x = _mm512_mask_i32gather_pd(vec_x, mask, vec_idx, x, _MM_SCALE_8);
392: vec_y = _mm512_mask3_fmadd_pd(vec_x, vec_vals, vec_y, mask);
393: } else if ((nz & 0x07) == 2) {
394: yp[i] += aa[ipos] * x[aj[ipos]];
395: yp[i] += aa[ipos + 1] * x[aj[ipos + 1]];
396: } else if ((nz & 0x07) == 1) {
397: yp[i] += aa[ipos] * x[aj[ipos]];
398: }
399: yp[i] += _mm512_reduce_add_pd(vec_y);
400: #else
401: for (j = 0; j < nz; j++) {
402: ipos = ip[i] + j;
403: yp[i] += aa[ipos] * x[aj[ipos]];
404: }
405: #endif
406: }
407: } else {
408: /* Otherwise, there are enough rows in the chunk to make it
409: * worthwhile to vectorize across the rows, that is, to do the
410: * matvec by operating with "columns" of the chunk. */
411: for (j = 0; j < nz; j++) {
412: #if defined(PETSC_USE_AVX512_KERNELS) && defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
413: vec_j = _mm256_set1_epi32(j);
414: for (i = 0; i < ((isize >> 3) << 3); i += 8) {
415: vec_y = _mm512_loadu_pd(&yp[i]);
416: vec_ipos = _mm256_loadu_si256((__m256i const *)&ip[i]);
417: vec_ipos = _mm256_add_epi32(vec_ipos, vec_j);
418: vec_idx = _mm256_i32gather_epi32(aj, vec_ipos, _MM_SCALE_4);
419: vec_vals = _mm512_i32gather_pd(vec_ipos, aa, _MM_SCALE_8);
420: vec_x = _mm512_i32gather_pd(vec_idx, x, _MM_SCALE_8);
421: vec_y = _mm512_fmadd_pd(vec_x, vec_vals, vec_y);
422: _mm512_storeu_pd(&yp[i], vec_y);
423: }
424: for (i = isize - (isize & 0x07); i < isize; i++) {
425: ipos = ip[i] + j;
426: yp[i] += aa[ipos] * x[aj[ipos]];
427: }
428: #else
429: for (i = 0; i < isize; i++) {
430: ipos = ip[i] + j;
431: yp[i] += aa[ipos] * x[aj[ipos]];
432: }
433: #endif
434: }
435: }
437: #if defined(PETSC_HAVE_CRAY_VECTOR)
438: #pragma _CRI ivdep
439: #endif
440: /* Put results from yp[] into non-permuted result vector y. */
441: for (i = 0; i < isize; i++) y[iperm[istart + i]] = yp[i];
442: } /* End processing chunk of isize rows of a group. */
443: } /* End handling matvec for chunk with nz > 1. */
444: } /* End loop over igroup. */
445: PetscCall(PetscLogFlops(PetscMax(2.0 * a->nz - A->rmap->n, 0)));
446: PetscCall(VecRestoreArrayRead(xx, &x));
447: PetscCall(VecRestoreArray(yy, &y));
448: PetscFunctionReturn(PETSC_SUCCESS);
449: }
451: /* MatMultAdd_SeqAIJPERM() calculates yy = ww + A * xx.
452: * Note that the names I used to designate the vectors differs from that
453: * used in MatMultAdd_SeqAIJ(). I did this to keep my notation consistent
454: * with the MatMult_SeqAIJPERM() routine, which is very similar to this one. */
455: /*
456: I hate having virtually identical code for the mult and the multadd!!!
457: */
458: static PetscErrorCode MatMultAdd_SeqAIJPERM(Mat A, Vec xx, Vec ww, Vec yy)
459: {
460: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
461: const PetscScalar *x;
462: PetscScalar *y, *w;
463: const MatScalar *aa;
464: const PetscInt *aj, *ai;
465: PetscInt i, j;
467: /* Variables that don't appear in MatMultAdd_SeqAIJ. */
468: Mat_SeqAIJPERM *aijperm;
469: PetscInt *iperm; /* Points to the permutation vector. */
470: PetscInt *xgroup;
471: /* Denotes where groups of rows with same number of nonzeros
472: * begin and end in iperm. */
473: PetscInt *nzgroup;
474: PetscInt ngroup;
475: PetscInt igroup;
476: PetscInt jstart, jend;
477: /* jstart is used in loops to denote the position in iperm where a
478: * group starts; jend denotes the position where it ends.
479: * (jend + 1 is where the next group starts.) */
480: PetscInt iold, nz;
481: PetscInt istart, iend, isize;
482: PetscInt ipos;
483: PetscScalar yp[NDIM];
484: PetscInt ip[NDIM];
485: /* yp[] and ip[] are treated as vector "registers" for performing
486: * the mat-vec. */
488: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
489: #pragma disjoint(*x, *y, *aa)
490: #endif
492: PetscFunctionBegin;
493: PetscCall(VecGetArrayRead(xx, &x));
494: PetscCall(VecGetArrayPair(yy, ww, &y, &w));
496: aj = a->j; /* aj[k] gives column index for element aa[k]. */
497: aa = a->a; /* Nonzero elements stored row-by-row. */
498: ai = a->i; /* ai[k] is the position in aa and aj where row k starts. */
500: /* Get the info we need about the permutations and groupings. */
501: aijperm = (Mat_SeqAIJPERM *)A->spptr;
502: iperm = aijperm->iperm;
503: ngroup = aijperm->ngroup;
504: xgroup = aijperm->xgroup;
505: nzgroup = aijperm->nzgroup;
507: for (igroup = 0; igroup < ngroup; igroup++) {
508: jstart = xgroup[igroup];
509: jend = xgroup[igroup + 1] - 1;
511: nz = nzgroup[igroup];
513: /* Handle the special cases where the number of nonzeros per row
514: * in the group is either 0 or 1. */
515: if (nz == 0) {
516: for (i = jstart; i <= jend; i++) {
517: iold = iperm[i];
518: y[iold] = w[iold];
519: }
520: } else if (nz == 1) {
521: for (i = jstart; i <= jend; i++) {
522: iold = iperm[i];
523: ipos = ai[iold];
524: y[iold] = w[iold] + aa[ipos] * x[aj[ipos]];
525: }
526: }
527: /* For the general case: */
528: else {
529: /* We work our way through the current group in chunks of NDIM rows
530: * at a time. */
532: for (istart = jstart; istart <= jend; istart += NDIM) {
533: /* Figure out where the chunk of 'isize' rows ends in iperm.
534: * 'isize may of course be less than NDIM for the last chunk. */
535: iend = istart + (NDIM - 1);
536: if (iend > jend) iend = jend;
537: isize = iend - istart + 1;
539: /* Initialize the yp[] array that will be used to hold part of
540: * the permuted results vector, and figure out where in aa each
541: * row of the chunk will begin. */
542: for (i = 0; i < isize; i++) {
543: iold = iperm[istart + i];
544: /* iold is a row number from the matrix A *before* reordering. */
545: ip[i] = ai[iold];
546: /* ip[i] tells us where the ith row of the chunk begins in aa. */
547: yp[i] = w[iold];
548: }
550: /* If the number of zeros per row exceeds the number of rows in
551: * the chunk, we should vectorize along nz, that is, perform the
552: * mat-vec one row at a time as in the usual CSR case. */
553: if (nz > isize) {
554: #if defined(PETSC_HAVE_CRAY_VECTOR)
555: #pragma _CRI preferstream
556: #endif
557: for (i = 0; i < isize; i++) {
558: #if defined(PETSC_HAVE_CRAY_VECTOR)
559: #pragma _CRI prefervector
560: #endif
561: for (j = 0; j < nz; j++) {
562: ipos = ip[i] + j;
563: yp[i] += aa[ipos] * x[aj[ipos]];
564: }
565: }
566: }
567: /* Otherwise, there are enough rows in the chunk to make it
568: * worthwhile to vectorize across the rows, that is, to do the
569: * matvec by operating with "columns" of the chunk. */
570: else {
571: for (j = 0; j < nz; j++) {
572: for (i = 0; i < isize; i++) {
573: ipos = ip[i] + j;
574: yp[i] += aa[ipos] * x[aj[ipos]];
575: }
576: }
577: }
579: #if defined(PETSC_HAVE_CRAY_VECTOR)
580: #pragma _CRI ivdep
581: #endif
582: /* Put results from yp[] into non-permuted result vector y. */
583: for (i = 0; i < isize; i++) y[iperm[istart + i]] = yp[i];
584: } /* End processing chunk of isize rows of a group. */
586: } /* End handling matvec for chunk with nz > 1. */
587: } /* End loop over igroup. */
589: PetscCall(PetscLogFlops(2.0 * a->nz));
590: PetscCall(VecRestoreArrayRead(xx, &x));
591: PetscCall(VecRestoreArrayPair(yy, ww, &y, &w));
592: PetscFunctionReturn(PETSC_SUCCESS);
593: }
595: /* MatConvert_SeqAIJ_SeqAIJPERM converts a SeqAIJ matrix into a
596: * SeqAIJPERM matrix. This routine is called by the MatCreate_SeqAIJPERM()
597: * routine, but can also be used to convert an assembled SeqAIJ matrix
598: * into a SeqAIJPERM one. */
599: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJPERM(Mat A, MatType type, MatReuse reuse, Mat *newmat)
600: {
601: Mat B = *newmat;
602: Mat_SeqAIJPERM *aijperm;
603: PetscBool sametype;
605: PetscFunctionBegin;
606: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatDuplicate(A, MAT_COPY_VALUES, &B));
607: PetscCall(PetscObjectTypeCompare((PetscObject)A, type, &sametype));
608: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
610: PetscCall(PetscNew(&aijperm));
611: B->spptr = (void *)aijperm;
613: /* Set function pointers for methods that we inherit from AIJ but override. */
614: B->ops->duplicate = MatDuplicate_SeqAIJPERM;
615: B->ops->assemblyend = MatAssemblyEnd_SeqAIJPERM;
616: B->ops->destroy = MatDestroy_SeqAIJPERM;
617: B->ops->mult = MatMult_SeqAIJPERM;
618: B->ops->multadd = MatMultAdd_SeqAIJPERM;
620: aijperm->nonzerostate = -1; /* this will trigger the generation of the permutation information the first time through MatAssembly()*/
621: /* If A has already been assembled, compute the permutation. */
622: if (A->assembled) PetscCall(MatSeqAIJPERM_create_perm(B));
624: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaijperm_seqaij_C", MatConvert_SeqAIJPERM_SeqAIJ));
626: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJPERM));
627: *newmat = B;
628: PetscFunctionReturn(PETSC_SUCCESS);
629: }
631: /*@C
632: MatCreateSeqAIJPERM - Creates a sparse matrix of type `MATSEQAIJPERM`.
634: Collective
636: Input Parameters:
637: + comm - MPI communicator, set to `PETSC_COMM_SELF`
638: . m - number of rows
639: . n - number of columns
640: . nz - number of nonzeros per row (same for all rows), ignored if `nnz` is given
641: - nnz - array containing the number of nonzeros in the various rows (possibly different for each row) or `NULL`
643: Output Parameter:
644: . A - the matrix
646: Level: intermediate
648: Notes:
649: This type inherits from `MATSEQAIJ`, but calculates some additional permutation information
650: that is used to allow better vectorization of some operations. At the cost of increased
651: storage, the `MATSEQAIJ` formatted matrix can be copied to a format in which pieces of the
652: matrix are stored in ELLPACK format, allowing the vectorized matrix multiply routine to use
653: stride-1 memory accesses.
655: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateMPIAIJPERM()`, `MatSetValues()`
656: @*/
657: PetscErrorCode MatCreateSeqAIJPERM(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
658: {
659: PetscFunctionBegin;
660: PetscCall(MatCreate(comm, A));
661: PetscCall(MatSetSizes(*A, m, n, m, n));
662: PetscCall(MatSetType(*A, MATSEQAIJPERM));
663: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
664: PetscFunctionReturn(PETSC_SUCCESS);
665: }
667: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJPERM(Mat A)
668: {
669: PetscFunctionBegin;
670: PetscCall(MatSetType(A, MATSEQAIJ));
671: PetscCall(MatConvert_SeqAIJ_SeqAIJPERM(A, MATSEQAIJPERM, MAT_INPLACE_MATRIX, &A));
672: PetscFunctionReturn(PETSC_SUCCESS);
673: }