Actual source code: sell.c
petsc-3.11.4 2019-09-28
2: /*
3: Defines the basic matrix operations for the SELL matrix storage format.
4: */
5: #include <../src/mat/impls/sell/seq/sell.h>
6: #include <petscblaslapack.h>
7: #include <petsc/private/kernels/blocktranspose.h>
8: #if defined(PETSC_HAVE_IMMINTRIN_H) && (defined(__AVX512F__) || (defined(__AVX2__) && defined(__FMA__)) || defined(__AVX__)) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
10: #include <immintrin.h>
12: #if !defined(_MM_SCALE_8)
13: #define _MM_SCALE_8 8
14: #endif
16: #if defined(__AVX512F__)
17: /* these do not work
18: vec_idx = _mm512_loadunpackhi_epi32(vec_idx,acolidx);
19: vec_vals = _mm512_loadunpackhi_pd(vec_vals,aval);
20: */
21: #define AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y) \
22: /* if the mask bit is set, copy from acolidx, otherwise from vec_idx */ \
23: vec_idx = _mm256_loadu_si256((__m256i const*)acolidx); \
24: vec_vals = _mm512_loadu_pd(aval); \
25: vec_x = _mm512_i32gather_pd(vec_idx,x,_MM_SCALE_8); \
26: vec_y = _mm512_fmadd_pd(vec_x,vec_vals,vec_y)
27: #elif defined(__AVX2__) && defined(__FMA__)
28: #define AVX2_Mult_Private(vec_idx,vec_x,vec_vals,vec_y) \
29: vec_vals = _mm256_loadu_pd(aval); \
30: vec_idx = _mm_loadu_si128((__m128i const*)acolidx); /* SSE2 */ \
31: vec_x = _mm256_i32gather_pd(x,vec_idx,_MM_SCALE_8); \
32: vec_y = _mm256_fmadd_pd(vec_x,vec_vals,vec_y)
33: #endif
34: #endif /* PETSC_HAVE_IMMINTRIN_H */
36: /*@C
37: MatSeqSELLSetPreallocation - For good matrix assembly performance
38: the user should preallocate the matrix storage by setting the parameter nz
39: (or the array nnz). By setting these parameters accurately, performance
40: during matrix assembly can be increased significantly.
42: Collective on MPI_Comm
44: Input Parameters:
45: + B - The matrix
46: . nz - number of nonzeros per row (same for all rows)
47: - nnz - array containing the number of nonzeros in the various rows
48: (possibly different for each row) or NULL
50: Notes:
51: If nnz is given then nz is ignored.
53: Specify the preallocated storage with either nz or nnz (not both).
54: Set nz=PETSC_DEFAULT and nnz=NULL for PETSc to control dynamic memory
55: allocation. For large problems you MUST preallocate memory or you
56: will get TERRIBLE performance, see the users' manual chapter on matrices.
58: You can call MatGetInfo() to get information on how effective the preallocation was;
59: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
60: You can also run with the option -info and look for messages with the string
61: malloc in them to see if additional memory allocation was needed.
63: Developers: Use nz of MAT_SKIP_ALLOCATION to not allocate any space for the matrix
64: entries or columns indices.
66: The maximum number of nonzeos in any row should be as accurate as possible.
67: If it is underestimated, you will get bad performance due to reallocation
68: (MatSeqXSELLReallocateSELL).
70: Level: intermediate
72: .seealso: MatCreate(), MatCreateSELL(), MatSetValues(), MatGetInfo()
74: @*/
75: PetscErrorCode MatSeqSELLSetPreallocation(Mat B,PetscInt rlenmax,const PetscInt rlen[])
76: {
82: PetscTryMethod(B,"MatSeqSELLSetPreallocation_C",(Mat,PetscInt,const PetscInt[]),(B,rlenmax,rlen));
83: return(0);
84: }
86: PetscErrorCode MatSeqSELLSetPreallocation_SeqSELL(Mat B,PetscInt maxallocrow,const PetscInt rlen[])
87: {
88: Mat_SeqSELL *b;
89: PetscInt i,j,totalslices;
90: PetscBool skipallocation=PETSC_FALSE,realalloc=PETSC_FALSE;
94: if (maxallocrow >= 0 || rlen) realalloc = PETSC_TRUE;
95: if (maxallocrow == MAT_SKIP_ALLOCATION) {
96: skipallocation = PETSC_TRUE;
97: maxallocrow = 0;
98: }
100: PetscLayoutSetUp(B->rmap);
101: PetscLayoutSetUp(B->cmap);
103: /* FIXME: if one preallocates more space than needed, the matrix does not shrink automatically, but for best performance it should */
104: if (maxallocrow == PETSC_DEFAULT || maxallocrow == PETSC_DECIDE) maxallocrow = 5;
105: if (maxallocrow < 0) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"maxallocrow cannot be less than 0: value %D",maxallocrow);
106: if (rlen) {
107: for (i=0; i<B->rmap->n; i++) {
108: if (rlen[i] < 0) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"rlen cannot be less than 0: local row %D value %D",i,rlen[i]);
109: if (rlen[i] > B->cmap->n) SETERRQ3(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"rlen cannot be greater than row length: local row %D value %D rowlength %D",i,rlen[i],B->cmap->n);
110: }
111: }
113: B->preallocated = PETSC_TRUE;
115: b = (Mat_SeqSELL*)B->data;
117: totalslices = B->rmap->n/8+((B->rmap->n & 0x07)?1:0); /* ceil(n/8) */
118: b->totalslices = totalslices;
119: if (!skipallocation) {
120: if (B->rmap->n & 0x07) {PetscInfo1(B,"Padding rows to the SEQSELL matrix because the number of rows is not the multiple of 8 (value %D)\n",B->rmap->n);}
122: if (!b->sliidx) { /* sliidx gives the starting index of each slice, the last element is the total space allocated */
123: PetscMalloc1(totalslices+1,&b->sliidx);
124: PetscLogObjectMemory((PetscObject)B,(totalslices+1)*sizeof(PetscInt));
125: }
126: if (!rlen) { /* if rlen is not provided, allocate same space for all the slices */
127: if (maxallocrow == PETSC_DEFAULT || maxallocrow == PETSC_DECIDE) maxallocrow = 10;
128: else if (maxallocrow < 0) maxallocrow = 1;
129: for (i=0; i<=totalslices; i++) b->sliidx[i] = i*8*maxallocrow;
130: } else {
131: maxallocrow = 0;
132: b->sliidx[0] = 0;
133: for (i=1; i<totalslices; i++) {
134: b->sliidx[i] = 0;
135: for (j=0;j<8;j++) {
136: b->sliidx[i] = PetscMax(b->sliidx[i],rlen[8*(i-1)+j]);
137: }
138: maxallocrow = PetscMax(b->sliidx[i],maxallocrow);
139: b->sliidx[i] = b->sliidx[i-1] + 8*b->sliidx[i];
140: }
141: /* last slice */
142: b->sliidx[totalslices] = 0;
143: for (j=(totalslices-1)*8;j<B->rmap->n;j++) b->sliidx[totalslices] = PetscMax(b->sliidx[totalslices],rlen[j]);
144: maxallocrow = PetscMax(b->sliidx[totalslices],maxallocrow);
145: b->sliidx[totalslices] = b->sliidx[totalslices-1] + 8*b->sliidx[totalslices];
146: }
148: /* allocate space for val, colidx, rlen */
149: /* FIXME: should B's old memory be unlogged? */
150: MatSeqXSELLFreeSELL(B,&b->val,&b->colidx);
151: /* FIXME: assuming an element of the bit array takes 8 bits */
152: PetscMalloc2(b->sliidx[totalslices],&b->val,b->sliidx[totalslices],&b->colidx);
153: PetscLogObjectMemory((PetscObject)B,b->sliidx[totalslices]*(sizeof(PetscScalar)+sizeof(PetscInt)));
154: /* b->rlen will count nonzeros in each row so far. We dont copy rlen to b->rlen because the matrix has not been set. */
155: PetscCalloc1(8*totalslices,&b->rlen);
156: PetscLogObjectMemory((PetscObject)B,8*totalslices*sizeof(PetscInt));
158: b->singlemalloc = PETSC_TRUE;
159: b->free_val = PETSC_TRUE;
160: b->free_colidx = PETSC_TRUE;
161: } else {
162: b->free_val = PETSC_FALSE;
163: b->free_colidx = PETSC_FALSE;
164: }
166: b->nz = 0;
167: b->maxallocrow = maxallocrow;
168: b->rlenmax = maxallocrow;
169: b->maxallocmat = b->sliidx[totalslices];
170: B->info.nz_unneeded = (double)b->maxallocmat;
171: if (realalloc) {
172: MatSetOption(B,MAT_NEW_NONZERO_ALLOCATION_ERR,PETSC_TRUE);
173: }
174: return(0);
175: }
177: PetscErrorCode MatGetRow_SeqSELL(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
178: {
179: Mat_SeqSELL *a = (Mat_SeqSELL*)A->data;
180: PetscInt shift;
183: if (row < 0 || row >= A->rmap->n) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row %D out of range",row);
184: if (nz) *nz = a->rlen[row];
185: shift = a->sliidx[row>>3]+(row&0x07);
186: if (!a->getrowcols) {
189: PetscMalloc2(a->rlenmax,&a->getrowcols,a->rlenmax,&a->getrowvals);
190: }
191: if (idx) {
192: PetscInt j;
193: for (j=0; j<a->rlen[row]; j++) a->getrowcols[j] = a->colidx[shift+8*j];
194: *idx = a->getrowcols;
195: }
196: if (v) {
197: PetscInt j;
198: for (j=0; j<a->rlen[row]; j++) a->getrowvals[j] = a->val[shift+8*j];
199: *v = a->getrowvals;
200: }
201: return(0);
202: }
204: PetscErrorCode MatRestoreRow_SeqSELL(Mat A,PetscInt row,PetscInt *nz,PetscInt **idx,PetscScalar **v)
205: {
207: return(0);
208: }
210: PetscErrorCode MatConvert_SeqSELL_SeqAIJ(Mat A, MatType newtype,MatReuse reuse,Mat *newmat)
211: {
212: Mat B;
213: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
214: PetscInt i;
218: if (reuse == MAT_REUSE_MATRIX) {
219: B = *newmat;
220: MatZeroEntries(B);
221: } else {
222: MatCreate(PetscObjectComm((PetscObject)A),&B);
223: MatSetSizes(B,A->rmap->n,A->cmap->n,A->rmap->N,A->cmap->N);
224: MatSetType(B,MATSEQAIJ);
225: MatSeqAIJSetPreallocation(B,0,a->rlen);
226: }
228: for (i=0; i<A->rmap->n; i++) {
229: PetscInt nz = 0,*cols = NULL;
230: PetscScalar *vals = NULL;
232: MatGetRow_SeqSELL(A,i,&nz,&cols,&vals);
233: MatSetValues(B,1,&i,nz,cols,vals,INSERT_VALUES);
234: MatRestoreRow_SeqSELL(A,i,&nz,&cols,&vals);
235: }
237: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
238: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
239: B->rmap->bs = A->rmap->bs;
241: if (reuse == MAT_INPLACE_MATRIX) {
242: MatHeaderReplace(A,&B);
243: } else {
244: *newmat = B;
245: }
246: return(0);
247: }
249: #include <../src/mat/impls/aij/seq/aij.h>
251: PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat A,MatType newtype,MatReuse reuse,Mat *newmat)
252: {
253: Mat B;
254: Mat_SeqAIJ *a=(Mat_SeqAIJ*)A->data;
255: PetscInt *ai=a->i,m=A->rmap->N,n=A->cmap->N,i,*rowlengths,row,ncols;
256: const PetscInt *cols;
257: const PetscScalar *vals;
258: PetscErrorCode ierr;
262: if (reuse == MAT_REUSE_MATRIX) {
263: B = *newmat;
264: } else {
265: /* Can we just use ilen? */
266: PetscMalloc1(m,&rowlengths);
267: for (i=0; i<m; i++) {
268: rowlengths[i] = ai[i+1] - ai[i];
269: }
271: MatCreate(PetscObjectComm((PetscObject)A),&B);
272: MatSetSizes(B,m,n,m,n);
273: MatSetType(B,MATSEQSELL);
274: MatSeqSELLSetPreallocation(B,0,rowlengths);
275: PetscFree(rowlengths);
276: }
278: for (row=0; row<m; row++) {
279: MatGetRow(A,row,&ncols,&cols,&vals);
280: MatSetValues(B,1,&row,ncols,cols,vals,INSERT_VALUES);
281: MatRestoreRow(A,row,&ncols,&cols,&vals);
282: }
283: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
284: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
285: B->rmap->bs = A->rmap->bs;
287: if (reuse == MAT_INPLACE_MATRIX) {
288: MatHeaderReplace(A,&B);
289: } else {
290: *newmat = B;
291: }
292: return(0);
293: }
295: PetscErrorCode MatMult_SeqSELL(Mat A,Vec xx,Vec yy)
296: {
297: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
298: PetscScalar *y;
299: const PetscScalar *x;
300: const MatScalar *aval=a->val;
301: PetscInt totalslices=a->totalslices;
302: const PetscInt *acolidx=a->colidx;
303: PetscInt i,j;
304: PetscErrorCode ierr;
305: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
306: __m512d vec_x,vec_y,vec_vals;
307: __m256i vec_idx;
308: __mmask8 mask;
309: __m512d vec_x2,vec_y2,vec_vals2,vec_x3,vec_y3,vec_vals3,vec_x4,vec_y4,vec_vals4;
310: __m256i vec_idx2,vec_idx3,vec_idx4;
311: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
312: __m128i vec_idx;
313: __m256d vec_x,vec_y,vec_y2,vec_vals;
314: MatScalar yval;
315: PetscInt r,rows_left,row,nnz_in_row;
316: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
317: __m128d vec_x_tmp;
318: __m256d vec_x,vec_y,vec_y2,vec_vals;
319: MatScalar yval;
320: PetscInt r,rows_left,row,nnz_in_row;
321: #else
322: PetscScalar sum[8];
323: #endif
325: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
326: #pragma disjoint(*x,*y,*aval)
327: #endif
330: VecGetArrayRead(xx,&x);
331: VecGetArray(yy,&y);
332: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
333: for (i=0; i<totalslices; i++) { /* loop over slices */
334: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
335: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
337: vec_y = _mm512_setzero_pd();
338: vec_y2 = _mm512_setzero_pd();
339: vec_y3 = _mm512_setzero_pd();
340: vec_y4 = _mm512_setzero_pd();
342: j = a->sliidx[i]>>3; /* 8 bytes are read at each time, corresponding to a slice columnn */
343: switch ((a->sliidx[i+1]-a->sliidx[i])/8 & 3) {
344: case 3:
345: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
346: acolidx += 8; aval += 8;
347: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
348: acolidx += 8; aval += 8;
349: AVX512_Mult_Private(vec_idx3,vec_x3,vec_vals3,vec_y3);
350: acolidx += 8; aval += 8;
351: j += 3;
352: break;
353: case 2:
354: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
355: acolidx += 8; aval += 8;
356: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
357: acolidx += 8; aval += 8;
358: j += 2;
359: break;
360: case 1:
361: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
362: acolidx += 8; aval += 8;
363: j += 1;
364: break;
365: }
366: #pragma novector
367: for (; j<(a->sliidx[i+1]>>3); j+=4) {
368: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
369: acolidx += 8; aval += 8;
370: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
371: acolidx += 8; aval += 8;
372: AVX512_Mult_Private(vec_idx3,vec_x3,vec_vals3,vec_y3);
373: acolidx += 8; aval += 8;
374: AVX512_Mult_Private(vec_idx4,vec_x4,vec_vals4,vec_y4);
375: acolidx += 8; aval += 8;
376: }
378: vec_y = _mm512_add_pd(vec_y,vec_y2);
379: vec_y = _mm512_add_pd(vec_y,vec_y3);
380: vec_y = _mm512_add_pd(vec_y,vec_y4);
381: if (i == totalslices-1 && A->rmap->n & 0x07) { /* if last slice has padding rows */
382: mask = (__mmask8)(0xff >> (8-(A->rmap->n & 0x07)));
383: _mm512_mask_storeu_pd(&y[8*i],mask,vec_y);
384: } else {
385: _mm512_storeu_pd(&y[8*i],vec_y);
386: }
387: }
388: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
389: for (i=0; i<totalslices; i++) { /* loop over full slices */
390: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
391: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
393: /* last slice may have padding rows. Don't use vectorization. */
394: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
395: rows_left = A->rmap->n - 8*i;
396: for (r=0; r<rows_left; ++r) {
397: yval = (MatScalar)0;
398: row = 8*i + r;
399: nnz_in_row = a->rlen[row];
400: for (j=0; j<nnz_in_row; ++j) yval += aval[8*j+r] * x[acolidx[8*j+r]];
401: y[row] = yval;
402: }
403: break;
404: }
406: vec_y = _mm256_setzero_pd();
407: vec_y2 = _mm256_setzero_pd();
409: /* Process slice of height 8 (512 bits) via two subslices of height 4 (256 bits) via AVX */
410: #pragma novector
411: #pragma unroll(2)
412: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
413: AVX2_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
414: aval += 4; acolidx += 4;
415: AVX2_Mult_Private(vec_idx,vec_x,vec_vals,vec_y2);
416: aval += 4; acolidx += 4;
417: }
419: _mm256_storeu_pd(y+i*8,vec_y);
420: _mm256_storeu_pd(y+i*8+4,vec_y2);
421: }
422: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
423: for (i=0; i<totalslices; i++) { /* loop over full slices */
424: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
425: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
427: vec_y = _mm256_setzero_pd();
428: vec_y2 = _mm256_setzero_pd();
430: /* last slice may have padding rows. Don't use vectorization. */
431: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
432: rows_left = A->rmap->n - 8*i;
433: for (r=0; r<rows_left; ++r) {
434: yval = (MatScalar)0;
435: row = 8*i + r;
436: nnz_in_row = a->rlen[row];
437: for (j=0; j<nnz_in_row; ++j) yval += aval[8*j + r] * x[acolidx[8*j + r]];
438: y[row] = yval;
439: }
440: break;
441: }
443: /* Process slice of height 8 (512 bits) via two subslices of height 4 (256 bits) via AVX */
444: #pragma novector
445: #pragma unroll(2)
446: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
447: vec_vals = _mm256_loadu_pd(aval);
448: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
449: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
450: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,0);
451: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
452: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
453: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,1);
454: vec_y = _mm256_add_pd(_mm256_mul_pd(vec_x,vec_vals),vec_y);
455: aval += 4;
457: vec_vals = _mm256_loadu_pd(aval);
458: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
459: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
460: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,0);
461: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
462: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
463: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,1);
464: vec_y2 = _mm256_add_pd(_mm256_mul_pd(vec_x,vec_vals),vec_y2);
465: aval += 4;
466: }
468: _mm256_storeu_pd(y + i*8, vec_y);
469: _mm256_storeu_pd(y + i*8 + 4, vec_y2);
470: }
471: #else
472: for (i=0; i<totalslices; i++) { /* loop over slices */
473: for (j=0; j<8; j++) sum[j] = 0.0;
474: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
475: sum[0] += aval[j] * x[acolidx[j]];
476: sum[1] += aval[j+1] * x[acolidx[j+1]];
477: sum[2] += aval[j+2] * x[acolidx[j+2]];
478: sum[3] += aval[j+3] * x[acolidx[j+3]];
479: sum[4] += aval[j+4] * x[acolidx[j+4]];
480: sum[5] += aval[j+5] * x[acolidx[j+5]];
481: sum[6] += aval[j+6] * x[acolidx[j+6]];
482: sum[7] += aval[j+7] * x[acolidx[j+7]];
483: }
484: if (i == totalslices-1 && (A->rmap->n & 0x07)) { /* if last slice has padding rows */
485: for(j=0; j<(A->rmap->n & 0x07); j++) y[8*i+j] = sum[j];
486: } else {
487: for(j=0; j<8; j++) y[8*i+j] = sum[j];
488: }
489: }
490: #endif
492: PetscLogFlops(2.0*a->nz-a->nonzerorowcnt); /* theoretical minimal FLOPs */
493: VecRestoreArrayRead(xx,&x);
494: VecRestoreArray(yy,&y);
495: return(0);
496: }
498: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
499: PetscErrorCode MatMultAdd_SeqSELL(Mat A,Vec xx,Vec yy,Vec zz)
500: {
501: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
502: PetscScalar *y,*z;
503: const PetscScalar *x;
504: const MatScalar *aval=a->val;
505: PetscInt totalslices=a->totalslices;
506: const PetscInt *acolidx=a->colidx;
507: PetscInt i,j;
508: PetscErrorCode ierr;
509: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
510: __m512d vec_x,vec_y,vec_vals;
511: __m256i vec_idx;
512: __mmask8 mask;
513: __m512d vec_x2,vec_y2,vec_vals2,vec_x3,vec_y3,vec_vals3,vec_x4,vec_y4,vec_vals4;
514: __m256i vec_idx2,vec_idx3,vec_idx4;
515: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
516: __m128d vec_x_tmp;
517: __m256d vec_x,vec_y,vec_y2,vec_vals;
518: MatScalar yval;
519: PetscInt r,row,nnz_in_row;
520: #else
521: PetscScalar sum[8];
522: #endif
524: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
525: #pragma disjoint(*x,*y,*aval)
526: #endif
529: VecGetArrayRead(xx,&x);
530: VecGetArrayPair(yy,zz,&y,&z);
531: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
532: for (i=0; i<totalslices; i++) { /* loop over slices */
533: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
534: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
536: if (i == totalslices-1 && A->rmap->n & 0x07) { /* if last slice has padding rows */
537: mask = (__mmask8)(0xff >> (8-(A->rmap->n & 0x07)));
538: vec_y = _mm512_mask_loadu_pd(vec_y,mask,&y[8*i]);
539: } else {
540: vec_y = _mm512_loadu_pd(&y[8*i]);
541: }
542: vec_y2 = _mm512_setzero_pd();
543: vec_y3 = _mm512_setzero_pd();
544: vec_y4 = _mm512_setzero_pd();
546: j = a->sliidx[i]>>3; /* 8 bytes are read at each time, corresponding to a slice columnn */
547: switch ((a->sliidx[i+1]-a->sliidx[i])/8 & 3) {
548: case 3:
549: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
550: acolidx += 8; aval += 8;
551: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
552: acolidx += 8; aval += 8;
553: AVX512_Mult_Private(vec_idx3,vec_x3,vec_vals3,vec_y3);
554: acolidx += 8; aval += 8;
555: j += 3;
556: break;
557: case 2:
558: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
559: acolidx += 8; aval += 8;
560: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
561: acolidx += 8; aval += 8;
562: j += 2;
563: break;
564: case 1:
565: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
566: acolidx += 8; aval += 8;
567: j += 1;
568: break;
569: }
570: #pragma novector
571: for (; j<(a->sliidx[i+1]>>3); j+=4) {
572: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
573: acolidx += 8; aval += 8;
574: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
575: acolidx += 8; aval += 8;
576: AVX512_Mult_Private(vec_idx3,vec_x3,vec_vals3,vec_y3);
577: acolidx += 8; aval += 8;
578: AVX512_Mult_Private(vec_idx4,vec_x4,vec_vals4,vec_y4);
579: acolidx += 8; aval += 8;
580: }
582: vec_y = _mm512_add_pd(vec_y,vec_y2);
583: vec_y = _mm512_add_pd(vec_y,vec_y3);
584: vec_y = _mm512_add_pd(vec_y,vec_y4);
585: if (i == totalslices-1 && A->rmap->n & 0x07) { /* if last slice has padding rows */
586: _mm512_mask_storeu_pd(&z[8*i],mask,vec_y);
587: } else {
588: _mm512_storeu_pd(&z[8*i],vec_y);
589: }
590: }
591: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
592: for (i=0; i<totalslices; i++) { /* loop over full slices */
593: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
594: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
596: /* last slice may have padding rows. Don't use vectorization. */
597: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
598: for (r=0; r<(A->rmap->n & 0x07); ++r) {
599: row = 8*i + r;
600: yval = (MatScalar)0.0;
601: nnz_in_row = a->rlen[row];
602: for (j=0; j<nnz_in_row; ++j) yval += aval[8*j+r] * x[acolidx[8*j+r]];
603: z[row] = y[row] + yval;
604: }
605: break;
606: }
608: vec_y = _mm256_loadu_pd(y+8*i);
609: vec_y2 = _mm256_loadu_pd(y+8*i+4);
611: /* Process slice of height 8 (512 bits) via two subslices of height 4 (256 bits) via AVX */
612: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
613: vec_vals = _mm256_loadu_pd(aval);
614: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
615: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
616: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,0);
617: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
618: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
619: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,1);
620: vec_y = _mm256_add_pd(_mm256_mul_pd(vec_x,vec_vals),vec_y);
621: aval += 4;
623: vec_vals = _mm256_loadu_pd(aval);
624: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
625: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
626: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,0);
627: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
628: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
629: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,1);
630: vec_y2 = _mm256_add_pd(_mm256_mul_pd(vec_x,vec_vals),vec_y2);
631: aval += 4;
632: }
634: _mm256_storeu_pd(z+i*8,vec_y);
635: _mm256_storeu_pd(z+i*8+4,vec_y2);
636: }
637: #else
638: for (i=0; i<totalslices; i++) { /* loop over slices */
639: for (j=0; j<8; j++) sum[j] = 0.0;
640: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
641: sum[0] += aval[j] * x[acolidx[j]];
642: sum[1] += aval[j+1] * x[acolidx[j+1]];
643: sum[2] += aval[j+2] * x[acolidx[j+2]];
644: sum[3] += aval[j+3] * x[acolidx[j+3]];
645: sum[4] += aval[j+4] * x[acolidx[j+4]];
646: sum[5] += aval[j+5] * x[acolidx[j+5]];
647: sum[6] += aval[j+6] * x[acolidx[j+6]];
648: sum[7] += aval[j+7] * x[acolidx[j+7]];
649: }
650: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
651: for (j=0; j<(A->rmap->n & 0x07); j++) z[8*i+j] = y[8*i+j] + sum[j];
652: } else {
653: for (j=0; j<8; j++) z[8*i+j] = y[8*i+j] + sum[j];
654: }
655: }
656: #endif
658: PetscLogFlops(2.0*a->nz);
659: VecRestoreArrayRead(xx,&x);
660: VecRestoreArrayPair(yy,zz,&y,&z);
661: return(0);
662: }
664: PetscErrorCode MatMultTransposeAdd_SeqSELL(Mat A,Vec xx,Vec zz,Vec yy)
665: {
666: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
667: PetscScalar *y;
668: const PetscScalar *x;
669: const MatScalar *aval=a->val;
670: const PetscInt *acolidx=a->colidx;
671: PetscInt i,j,r,row,nnz_in_row,totalslices=a->totalslices;
672: PetscErrorCode ierr;
674: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
675: #pragma disjoint(*x,*y,*aval)
676: #endif
679: if (A->symmetric) {
680: MatMultAdd_SeqSELL(A,xx,zz,yy);
681: return(0);
682: }
683: if (zz != yy) { VecCopy(zz,yy); }
684: VecGetArrayRead(xx,&x);
685: VecGetArray(yy,&y);
686: for (i=0; i<a->totalslices; i++) { /* loop over slices */
687: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
688: for (r=0; r<(A->rmap->n & 0x07); ++r) {
689: row = 8*i + r;
690: nnz_in_row = a->rlen[row];
691: for (j=0; j<nnz_in_row; ++j) y[acolidx[8*j+r]] += aval[8*j+r] * x[row];
692: }
693: break;
694: }
695: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
696: y[acolidx[j]] += aval[j] * x[8*i];
697: y[acolidx[j+1]] += aval[j+1] * x[8*i+1];
698: y[acolidx[j+2]] += aval[j+2] * x[8*i+2];
699: y[acolidx[j+3]] += aval[j+3] * x[8*i+3];
700: y[acolidx[j+4]] += aval[j+4] * x[8*i+4];
701: y[acolidx[j+5]] += aval[j+5] * x[8*i+5];
702: y[acolidx[j+6]] += aval[j+6] * x[8*i+6];
703: y[acolidx[j+7]] += aval[j+7] * x[8*i+7];
704: }
705: }
706: PetscLogFlops(2.0*a->sliidx[a->totalslices]);
707: VecRestoreArrayRead(xx,&x);
708: VecRestoreArray(yy,&y);
709: return(0);
710: }
712: PetscErrorCode MatMultTranspose_SeqSELL(Mat A,Vec xx,Vec yy)
713: {
717: if (A->symmetric) {
718: MatMult_SeqSELL(A,xx,yy);
719: } else {
720: VecSet(yy,0.0);
721: MatMultTransposeAdd_SeqSELL(A,xx,yy,yy);
722: }
723: return(0);
724: }
726: /*
727: Checks for missing diagonals
728: */
729: PetscErrorCode MatMissingDiagonal_SeqSELL(Mat A,PetscBool *missing,PetscInt *d)
730: {
731: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
732: PetscInt *diag,i;
736: *missing = PETSC_FALSE;
737: if (A->rmap->n > 0 && !(a->colidx)) {
738: *missing = PETSC_TRUE;
739: if (d) *d = 0;
740: PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
741: } else {
742: diag = a->diag;
743: for (i=0; i<A->rmap->n; i++) {
744: if (diag[i] == -1) {
745: *missing = PETSC_TRUE;
746: if (d) *d = i;
747: PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
748: break;
749: }
750: }
751: }
752: return(0);
753: }
755: PetscErrorCode MatMarkDiagonal_SeqSELL(Mat A)
756: {
757: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
758: PetscInt i,j,m=A->rmap->n,shift;
762: if (!a->diag) {
763: PetscMalloc1(m,&a->diag);
764: PetscLogObjectMemory((PetscObject)A,m*sizeof(PetscInt));
765: a->free_diag = PETSC_TRUE;
766: }
767: for (i=0; i<m; i++) { /* loop over rows */
768: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
769: a->diag[i] = -1;
770: for (j=0; j<a->rlen[i]; j++) {
771: if (a->colidx[shift+j*8] == i) {
772: a->diag[i] = shift+j*8;
773: break;
774: }
775: }
776: }
777: return(0);
778: }
780: /*
781: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
782: */
783: PetscErrorCode MatInvertDiagonal_SeqSELL(Mat A,PetscScalar omega,PetscScalar fshift)
784: {
785: Mat_SeqSELL *a=(Mat_SeqSELL*) A->data;
786: PetscInt i,*diag,m = A->rmap->n;
787: MatScalar *val = a->val;
788: PetscScalar *idiag,*mdiag;
792: if (a->idiagvalid) return(0);
793: MatMarkDiagonal_SeqSELL(A);
794: diag = a->diag;
795: if (!a->idiag) {
796: PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
797: PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
798: val = a->val;
799: }
800: mdiag = a->mdiag;
801: idiag = a->idiag;
803: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
804: for (i=0; i<m; i++) {
805: mdiag[i] = val[diag[i]];
806: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
807: if (PetscRealPart(fshift)) {
808: PetscInfo1(A,"Zero diagonal on row %D\n",i);
809: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
810: A->factorerror_zeropivot_value = 0.0;
811: A->factorerror_zeropivot_row = i;
812: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
813: }
814: idiag[i] = 1.0/val[diag[i]];
815: }
816: PetscLogFlops(m);
817: } else {
818: for (i=0; i<m; i++) {
819: mdiag[i] = val[diag[i]];
820: idiag[i] = omega/(fshift + val[diag[i]]);
821: }
822: PetscLogFlops(2.0*m);
823: }
824: a->idiagvalid = PETSC_TRUE;
825: return(0);
826: }
828: PetscErrorCode MatZeroEntries_SeqSELL(Mat A)
829: {
830: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
834: PetscMemzero(a->val,(a->sliidx[a->totalslices])*sizeof(PetscScalar));
835: MatSeqSELLInvalidateDiagonal(A);
836: return(0);
837: }
839: PetscErrorCode MatDestroy_SeqSELL(Mat A)
840: {
841: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
845: #if defined(PETSC_USE_LOG)
846: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
847: #endif
848: MatSeqXSELLFreeSELL(A,&a->val,&a->colidx);
849: ISDestroy(&a->row);
850: ISDestroy(&a->col);
851: PetscFree(a->diag);
852: PetscFree(a->rlen);
853: PetscFree(a->sliidx);
854: PetscFree3(a->idiag,a->mdiag,a->ssor_work);
855: PetscFree(a->solve_work);
856: ISDestroy(&a->icol);
857: PetscFree(a->saved_values);
858: PetscFree2(a->getrowcols,a->getrowvals);
860: PetscFree(A->data);
862: PetscObjectChangeTypeName((PetscObject)A,0);
863: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
864: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
865: #if defined(PETSC_HAVE_ELEMENTAL)
866: #endif
867: PetscObjectComposeFunction((PetscObject)A,"MatSeqSELLSetPreallocation_C",NULL);
868: return(0);
869: }
871: PetscErrorCode MatSetOption_SeqSELL(Mat A,MatOption op,PetscBool flg)
872: {
873: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
877: switch (op) {
878: case MAT_ROW_ORIENTED:
879: a->roworiented = flg;
880: break;
881: case MAT_KEEP_NONZERO_PATTERN:
882: a->keepnonzeropattern = flg;
883: break;
884: case MAT_NEW_NONZERO_LOCATIONS:
885: a->nonew = (flg ? 0 : 1);
886: break;
887: case MAT_NEW_NONZERO_LOCATION_ERR:
888: a->nonew = (flg ? -1 : 0);
889: break;
890: case MAT_NEW_NONZERO_ALLOCATION_ERR:
891: a->nonew = (flg ? -2 : 0);
892: break;
893: case MAT_UNUSED_NONZERO_LOCATION_ERR:
894: a->nounused = (flg ? -1 : 0);
895: break;
896: case MAT_NEW_DIAGONALS:
897: case MAT_IGNORE_OFF_PROC_ENTRIES:
898: case MAT_USE_HASH_TABLE:
899: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
900: break;
901: case MAT_SPD:
902: case MAT_SYMMETRIC:
903: case MAT_STRUCTURALLY_SYMMETRIC:
904: case MAT_HERMITIAN:
905: case MAT_SYMMETRY_ETERNAL:
906: /* These options are handled directly by MatSetOption() */
907: break;
908: default:
909: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
910: }
911: return(0);
912: }
914: PetscErrorCode MatGetDiagonal_SeqSELL(Mat A,Vec v)
915: {
916: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
917: PetscInt i,j,n,shift;
918: PetscScalar *x,zero=0.0;
922: VecGetLocalSize(v,&n);
923: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
925: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
926: PetscInt *diag=a->diag;
927: VecGetArray(v,&x);
928: for (i=0; i<n; i++) x[i] = 1.0/a->val[diag[i]];
929: VecRestoreArray(v,&x);
930: return(0);
931: }
933: VecSet(v,zero);
934: VecGetArray(v,&x);
935: for (i=0; i<n; i++) { /* loop over rows */
936: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
937: x[i] = 0;
938: for (j=0; j<a->rlen[i]; j++) {
939: if (a->colidx[shift+j*8] == i) {
940: x[i] = a->val[shift+j*8];
941: break;
942: }
943: }
944: }
945: VecRestoreArray(v,&x);
946: return(0);
947: }
949: PetscErrorCode MatDiagonalScale_SeqSELL(Mat A,Vec ll,Vec rr)
950: {
951: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
952: const PetscScalar *l,*r;
953: PetscInt i,j,m,n,row;
954: PetscErrorCode ierr;
957: if (ll) {
958: /* The local size is used so that VecMPI can be passed to this routine
959: by MatDiagonalScale_MPISELL */
960: VecGetLocalSize(ll,&m);
961: if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
962: VecGetArrayRead(ll,&l);
963: for (i=0; i<a->totalslices; i++) { /* loop over slices */
964: for (j=a->sliidx[i],row=0; j<a->sliidx[i+1]; j++,row=((row+1)&0x07)) {
965: a->val[j] *= l[8*i+row];
966: }
967: }
968: VecRestoreArrayRead(ll,&l);
969: PetscLogFlops(a->nz);
970: }
971: if (rr) {
972: VecGetLocalSize(rr,&n);
973: if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
974: VecGetArrayRead(rr,&r);
975: for (i=0; i<a->totalslices; i++) { /* loop over slices */
976: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j++) {
977: a->val[j] *= r[a->colidx[j]];
978: }
979: }
980: VecRestoreArrayRead(rr,&r);
981: PetscLogFlops(a->nz);
982: }
983: MatSeqSELLInvalidateDiagonal(A);
984: return(0);
985: }
987: extern PetscErrorCode MatSetValues_SeqSELL(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
989: PetscErrorCode MatGetValues_SeqSELL(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
990: {
991: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
992: PetscInt *cp,i,k,low,high,t,row,col,l;
993: PetscInt shift;
994: MatScalar *vp;
997: for (k=0; k<m; k++) { /* loop over requested rows */
998: row = im[k];
999: if (row<0) continue;
1000: #if defined(PETSC_USE_DEBUG)
1001: if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
1002: #endif
1003: shift = a->sliidx[row>>3]+(row&0x07); /* starting index of the row */
1004: cp = a->colidx+shift; /* pointer to the row */
1005: vp = a->val+shift; /* pointer to the row */
1006: for (l=0; l<n; l++) { /* loop over requested columns */
1007: col = in[l];
1008: if (col<0) continue;
1009: #if defined(PETSC_USE_DEBUG)
1010: if (col >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Column too large: row %D max %D",col,A->cmap->n-1);
1011: #endif
1012: high = a->rlen[row]; low = 0; /* assume unsorted */
1013: while (high-low > 5) {
1014: t = (low+high)/2;
1015: if (*(cp+t*8) > col) high = t;
1016: else low = t;
1017: }
1018: for (i=low; i<high; i++) {
1019: if (*(cp+8*i) > col) break;
1020: if (*(cp+8*i) == col) {
1021: *v++ = *(vp+8*i);
1022: goto finished;
1023: }
1024: }
1025: *v++ = 0.0;
1026: finished:;
1027: }
1028: }
1029: return(0);
1030: }
1032: PetscErrorCode MatView_SeqSELL_ASCII(Mat A,PetscViewer viewer)
1033: {
1034: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1035: PetscInt i,j,m=A->rmap->n,shift;
1036: const char *name;
1037: PetscViewerFormat format;
1038: PetscErrorCode ierr;
1041: PetscViewerGetFormat(viewer,&format);
1042: if (format == PETSC_VIEWER_ASCII_MATLAB) {
1043: PetscInt nofinalvalue = 0;
1044: /*
1045: if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
1046: nofinalvalue = 1;
1047: }
1048: */
1049: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1050: PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
1051: PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
1052: #if defined(PETSC_USE_COMPLEX)
1053: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
1054: #else
1055: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
1056: #endif
1057: PetscViewerASCIIPrintf(viewer,"zzz = [\n");
1059: for (i=0; i<m; i++) {
1060: shift = a->sliidx[i>>3]+(i&0x07);
1061: for (j=0; j<a->rlen[i]; j++) {
1062: #if defined(PETSC_USE_COMPLEX)
1063: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",i+1,a->colidx[shift+8*j]+1,(double)PetscRealPart(a->val[shift+8*j]),(double)PetscImaginaryPart(a->val[shift+8*j]));
1064: #else
1065: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->colidx[shift+8*j]+1,(double)a->val[shift+8*j]);
1066: #endif
1067: }
1068: }
1069: /*
1070: if (nofinalvalue) {
1071: #if defined(PETSC_USE_COMPLEX)
1072: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
1073: #else
1074: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->cmap->n,0.0);
1075: #endif
1076: }
1077: */
1078: PetscObjectGetName((PetscObject)A,&name);
1079: PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
1080: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1081: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
1082: return(0);
1083: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1084: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1085: for (i=0; i<m; i++) {
1086: PetscViewerASCIIPrintf(viewer,"row %D:",i);
1087: shift = a->sliidx[i>>3]+(i&0x07);
1088: for (j=0; j<a->rlen[i]; j++) {
1089: #if defined(PETSC_USE_COMPLEX)
1090: if (PetscImaginaryPart(a->val[shift+8*j]) > 0.0 && PetscRealPart(a->val[shift+8*j]) != 0.0) {
1091: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->colidx[shift+8*j],(double)PetscRealPart(a->val[shift+8*j]),(double)PetscImaginaryPart(a->val[shift+8*j]));
1092: } else if (PetscImaginaryPart(a->val[shift+8*j]) < 0.0 && PetscRealPart(a->val[shift+8*j]) != 0.0) {
1093: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->colidx[shift+8*j],(double)PetscRealPart(a->val[shift+8*j]),(double)-PetscImaginaryPart(a->val[shift+8*j]));
1094: } else if (PetscRealPart(a->val[shift+8*j]) != 0.0) {
1095: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[shift+8*j],(double)PetscRealPart(a->val[shift+8*j]));
1096: }
1097: #else
1098: if (a->val[shift+8*j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[shift+8*j],(double)a->val[shift+8*j]);}
1099: #endif
1100: }
1101: PetscViewerASCIIPrintf(viewer,"\n");
1102: }
1103: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1104: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
1105: PetscInt cnt=0,jcnt;
1106: PetscScalar value;
1107: #if defined(PETSC_USE_COMPLEX)
1108: PetscBool realonly=PETSC_TRUE;
1109: for (i=0; i<a->sliidx[a->totalslices]; i++) {
1110: if (PetscImaginaryPart(a->val[i]) != 0.0) {
1111: realonly = PETSC_FALSE;
1112: break;
1113: }
1114: }
1115: #endif
1117: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1118: for (i=0; i<m; i++) {
1119: jcnt = 0;
1120: shift = a->sliidx[i>>3]+(i&0x07);
1121: for (j=0; j<A->cmap->n; j++) {
1122: if (jcnt < a->rlen[i] && j == a->colidx[shift+8*j]) {
1123: value = a->val[cnt++];
1124: jcnt++;
1125: } else {
1126: value = 0.0;
1127: }
1128: #if defined(PETSC_USE_COMPLEX)
1129: if (realonly) {
1130: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
1131: } else {
1132: PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
1133: }
1134: #else
1135: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
1136: #endif
1137: }
1138: PetscViewerASCIIPrintf(viewer,"\n");
1139: }
1140: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1141: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
1142: PetscInt fshift=1;
1143: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1144: #if defined(PETSC_USE_COMPLEX)
1145: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
1146: #else
1147: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
1148: #endif
1149: PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
1150: for (i=0; i<m; i++) {
1151: shift = a->sliidx[i>>3]+(i&0x07);
1152: for (j=0; j<a->rlen[i]; j++) {
1153: #if defined(PETSC_USE_COMPLEX)
1154: PetscViewerASCIIPrintf(viewer,"%D %D %g %g\n",i+fshift,a->colidx[shift+8*j]+fshift,(double)PetscRealPart(a->val[shift+8*j]),(double)PetscImaginaryPart(a->val[shift+8*j]));
1155: #else
1156: PetscViewerASCIIPrintf(viewer,"%D %D %g\n",i+fshift,a->colidx[shift+8*j]+fshift,(double)a->val[shift+8*j]);
1157: #endif
1158: }
1159: }
1160: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1161: } else if (format == PETSC_VIEWER_NATIVE) {
1162: for (i=0; i<a->totalslices; i++) { /* loop over slices */
1163: PetscInt row;
1164: PetscViewerASCIIPrintf(viewer,"slice %D: %D %D\n",i,a->sliidx[i],a->sliidx[i+1]);
1165: for (j=a->sliidx[i],row=0; j<a->sliidx[i+1]; j++,row=((row+1)&0x07)) {
1166: #if defined(PETSC_USE_COMPLEX)
1167: if (PetscImaginaryPart(a->val[j]) > 0.0) {
1168: PetscViewerASCIIPrintf(viewer," %D %D %g + %g i\n",8*i+row,a->colidx[j],(double)PetscRealPart(a->val[j]),(double)PetscImaginaryPart(a->val[j]));
1169: } else if (PetscImaginaryPart(a->val[j]) < 0.0) {
1170: PetscViewerASCIIPrintf(viewer," %D %D %g - %g i\n",8*i+row,a->colidx[j],(double)PetscRealPart(a->val[j]),-(double)PetscImaginaryPart(a->val[j]));
1171: } else {
1172: PetscViewerASCIIPrintf(viewer," %D %D %g\n",8*i+row,a->colidx[j],(double)PetscRealPart(a->val[j]));
1173: }
1174: #else
1175: PetscViewerASCIIPrintf(viewer," %D %D %g\n",8*i+row,a->colidx[j],(double)a->val[j]);
1176: #endif
1177: }
1178: }
1179: } else {
1180: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1181: if (A->factortype) {
1182: for (i=0; i<m; i++) {
1183: shift = a->sliidx[i>>3]+(i&0x07);
1184: PetscViewerASCIIPrintf(viewer,"row %D:",i);
1185: /* L part */
1186: for (j=shift; j<a->diag[i]; j+=8) {
1187: #if defined(PETSC_USE_COMPLEX)
1188: if (PetscImaginaryPart(a->val[shift+8*j]) > 0.0) {
1189: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->colidx[j],(double)PetscRealPart(a->val[j]),(double)PetscImaginaryPart(a->val[j]));
1190: } else if (PetscImaginaryPart(a->val[shift+8*j]) < 0.0) {
1191: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->colidx[j],(double)PetscRealPart(a->val[j]),(double)(-PetscImaginaryPart(a->val[j])));
1192: } else {
1193: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)PetscRealPart(a->val[j]));
1194: }
1195: #else
1196: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)a->val[j]);
1197: #endif
1198: }
1199: /* diagonal */
1200: j = a->diag[i];
1201: #if defined(PETSC_USE_COMPLEX)
1202: if (PetscImaginaryPart(a->val[j]) > 0.0) {
1203: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->colidx[j],(double)PetscRealPart(1.0/a->val[j]),(double)PetscImaginaryPart(1.0/a->val[j]));
1204: } else if (PetscImaginaryPart(a->val[j]) < 0.0) {
1205: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->colidx[j],(double)PetscRealPart(1.0/a->val[j]),(double)(-PetscImaginaryPart(1.0/a->val[j])));
1206: } else {
1207: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)PetscRealPart(1.0/a->val[j]));
1208: }
1209: #else
1210: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)(1.0/a->val[j]));
1211: #endif
1213: /* U part */
1214: for (j=a->diag[i]+1; j<shift+8*a->rlen[i]; j+=8) {
1215: #if defined(PETSC_USE_COMPLEX)
1216: if (PetscImaginaryPart(a->val[j]) > 0.0) {
1217: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->colidx[j],(double)PetscRealPart(a->val[j]),(double)PetscImaginaryPart(a->val[j]));
1218: } else if (PetscImaginaryPart(a->val[j]) < 0.0) {
1219: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->colidx[j],(double)PetscRealPart(a->val[j]),(double)(-PetscImaginaryPart(a->val[j])));
1220: } else {
1221: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)PetscRealPart(a->val[j]));
1222: }
1223: #else
1224: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)a->val[j]);
1225: #endif
1226: }
1227: PetscViewerASCIIPrintf(viewer,"\n");
1228: }
1229: } else {
1230: for (i=0; i<m; i++) {
1231: shift = a->sliidx[i>>3]+(i&0x07);
1232: PetscViewerASCIIPrintf(viewer,"row %D:",i);
1233: for (j=0; j<a->rlen[i]; j++) {
1234: #if defined(PETSC_USE_COMPLEX)
1235: if (PetscImaginaryPart(a->val[j]) > 0.0) {
1236: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->colidx[shift+8*j],(double)PetscRealPart(a->val[shift+8*j]),(double)PetscImaginaryPart(a->val[shift+8*j]));
1237: } else if (PetscImaginaryPart(a->val[j]) < 0.0) {
1238: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->colidx[shift+8*j],(double)PetscRealPart(a->val[shift+8*j]),(double)-PetscImaginaryPart(a->val[shift+8*j]));
1239: } else {
1240: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[shift+8*j],(double)PetscRealPart(a->val[shift+8*j]));
1241: }
1242: #else
1243: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[shift+8*j],(double)a->val[shift+8*j]);
1244: #endif
1245: }
1246: PetscViewerASCIIPrintf(viewer,"\n");
1247: }
1248: }
1249: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1250: }
1251: PetscViewerFlush(viewer);
1252: return(0);
1253: }
1255: #include <petscdraw.h>
1256: PetscErrorCode MatView_SeqSELL_Draw_Zoom(PetscDraw draw,void *Aa)
1257: {
1258: Mat A=(Mat)Aa;
1259: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1260: PetscInt i,j,m=A->rmap->n,shift;
1261: int color;
1262: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1263: PetscViewer viewer;
1264: PetscViewerFormat format;
1265: PetscErrorCode ierr;
1268: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1269: PetscViewerGetFormat(viewer,&format);
1270: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
1272: /* loop over matrix elements drawing boxes */
1274: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1275: PetscDrawCollectiveBegin(draw);
1276: /* Blue for negative, Cyan for zero and Red for positive */
1277: color = PETSC_DRAW_BLUE;
1278: for (i=0; i<m; i++) {
1279: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1280: y_l = m - i - 1.0; y_r = y_l + 1.0;
1281: for (j=0; j<a->rlen[i]; j++) {
1282: x_l = a->colidx[shift+j*8]; x_r = x_l + 1.0;
1283: if (PetscRealPart(a->val[shift+8*j]) >= 0.) continue;
1284: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1285: }
1286: }
1287: color = PETSC_DRAW_CYAN;
1288: for (i=0; i<m; i++) {
1289: shift = a->sliidx[i>>3]+(i&0x07);
1290: y_l = m - i - 1.0; y_r = y_l + 1.0;
1291: for (j=0; j<a->rlen[i]; j++) {
1292: x_l = a->colidx[shift+j*8]; x_r = x_l + 1.0;
1293: if (a->val[shift+8*j] != 0.) continue;
1294: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1295: }
1296: }
1297: color = PETSC_DRAW_RED;
1298: for (i=0; i<m; i++) {
1299: shift = a->sliidx[i>>3]+(i&0x07);
1300: y_l = m - i - 1.0; y_r = y_l + 1.0;
1301: for (j=0; j<a->rlen[i]; j++) {
1302: x_l = a->colidx[shift+j*8]; x_r = x_l + 1.0;
1303: if (PetscRealPart(a->val[shift+8*j]) <= 0.) continue;
1304: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1305: }
1306: }
1307: PetscDrawCollectiveEnd(draw);
1308: } else {
1309: /* use contour shading to indicate magnitude of values */
1310: /* first determine max of all nonzero values */
1311: PetscReal minv=0.0,maxv=0.0;
1312: PetscInt count=0;
1313: PetscDraw popup;
1314: for (i=0; i<a->sliidx[a->totalslices]; i++) {
1315: if (PetscAbsScalar(a->val[i]) > maxv) maxv = PetscAbsScalar(a->val[i]);
1316: }
1317: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1318: PetscDrawGetPopup(draw,&popup);
1319: PetscDrawScalePopup(popup,minv,maxv);
1321: PetscDrawCollectiveBegin(draw);
1322: for (i=0; i<m; i++) {
1323: shift = a->sliidx[i>>3]+(i&0x07);
1324: y_l = m - i - 1.0;
1325: y_r = y_l + 1.0;
1326: for (j=0; j<a->rlen[i]; j++) {
1327: x_l = a->colidx[shift+j*8];
1328: x_r = x_l + 1.0;
1329: color = PetscDrawRealToColor(PetscAbsScalar(a->val[count]),minv,maxv);
1330: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1331: count++;
1332: }
1333: }
1334: PetscDrawCollectiveEnd(draw);
1335: }
1336: return(0);
1337: }
1339: #include <petscdraw.h>
1340: PetscErrorCode MatView_SeqSELL_Draw(Mat A,PetscViewer viewer)
1341: {
1342: PetscDraw draw;
1343: PetscReal xr,yr,xl,yl,h,w;
1344: PetscBool isnull;
1348: PetscViewerDrawGetDraw(viewer,0,&draw);
1349: PetscDrawIsNull(draw,&isnull);
1350: if (isnull) return(0);
1352: xr = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
1353: xr += w; yr += h; xl = -w; yl = -h;
1354: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1355: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1356: PetscDrawZoom(draw,MatView_SeqSELL_Draw_Zoom,A);
1357: PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
1358: PetscDrawSave(draw);
1359: return(0);
1360: }
1362: PetscErrorCode MatView_SeqSELL(Mat A,PetscViewer viewer)
1363: {
1364: PetscBool iascii,isbinary,isdraw;
1368: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1369: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1370: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1371: if (iascii) {
1372: MatView_SeqSELL_ASCII(A,viewer);
1373: } else if (isbinary) {
1374: /* MatView_SeqSELL_Binary(A,viewer); */
1375: } else if (isdraw) {
1376: MatView_SeqSELL_Draw(A,viewer);
1377: }
1378: return(0);
1379: }
1381: PetscErrorCode MatAssemblyEnd_SeqSELL(Mat A,MatAssemblyType mode)
1382: {
1383: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1384: PetscInt i,shift,row_in_slice,row,nrow,*cp,lastcol,j,k;
1385: MatScalar *vp;
1389: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1390: /* To do: compress out the unused elements */
1391: MatMarkDiagonal_SeqSELL(A);
1392: PetscInfo6(A,"Matrix size: %D X %D; storage space: %D allocated %D used (%D nonzeros+%D paddedzeros)\n",A->rmap->n,A->cmap->n,a->maxallocmat,a->sliidx[a->totalslices],a->nz,a->sliidx[a->totalslices]-a->nz);
1393: PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1394: PetscInfo1(A,"Maximum nonzeros in any row is %D\n",a->rlenmax);
1395: /* Set unused slots for column indices to last valid column index. Set unused slots for values to zero. This allows for a use of unmasked intrinsics -> higher performance */
1396: for (i=0; i<a->totalslices; ++i) {
1397: shift = a->sliidx[i]; /* starting index of the slice */
1398: cp = a->colidx+shift; /* pointer to the column indices of the slice */
1399: vp = a->val+shift; /* pointer to the nonzero values of the slice */
1400: for (row_in_slice=0; row_in_slice<8; ++row_in_slice) { /* loop over rows in the slice */
1401: row = 8*i + row_in_slice;
1402: nrow = a->rlen[row]; /* number of nonzeros in row */
1403: /*
1404: Search for the nearest nonzero. Normally setting the index to zero may cause extra communication.
1405: But if the entire slice are empty, it is fine to use 0 since the index will not be loaded.
1406: */
1407: lastcol = 0;
1408: if (nrow>0) { /* nonempty row */
1409: lastcol = cp[8*(nrow-1)+row_in_slice]; /* use the index from the last nonzero at current row */
1410: } else if (!row_in_slice) { /* first row of the currect slice is empty */
1411: for (j=1;j<8;j++) {
1412: if (a->rlen[8*i+j]) {
1413: lastcol = cp[j];
1414: break;
1415: }
1416: }
1417: } else {
1418: if (a->sliidx[i+1] != shift) lastcol = cp[row_in_slice-1]; /* use the index from the previous row */
1419: }
1421: for (k=nrow; k<(a->sliidx[i+1]-shift)/8; ++k) {
1422: cp[8*k+row_in_slice] = lastcol;
1423: vp[8*k+row_in_slice] = (MatScalar)0;
1424: }
1425: }
1426: }
1428: A->info.mallocs += a->reallocs;
1429: a->reallocs = 0;
1431: MatSeqSELLInvalidateDiagonal(A);
1432: return(0);
1433: }
1435: PetscErrorCode MatGetInfo_SeqSELL(Mat A,MatInfoType flag,MatInfo *info)
1436: {
1437: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1440: info->block_size = 1.0;
1441: info->nz_allocated = (double)a->maxallocmat;
1442: info->nz_used = (double)a->sliidx[a->totalslices]; /* include padding zeros */
1443: info->nz_unneeded = (double)(a->maxallocmat-a->sliidx[a->totalslices]);
1444: info->assemblies = (double)A->num_ass;
1445: info->mallocs = (double)A->info.mallocs;
1446: info->memory = ((PetscObject)A)->mem;
1447: if (A->factortype) {
1448: info->fill_ratio_given = A->info.fill_ratio_given;
1449: info->fill_ratio_needed = A->info.fill_ratio_needed;
1450: info->factor_mallocs = A->info.factor_mallocs;
1451: } else {
1452: info->fill_ratio_given = 0;
1453: info->fill_ratio_needed = 0;
1454: info->factor_mallocs = 0;
1455: }
1456: return(0);
1457: }
1459: PetscErrorCode MatSetValues_SeqSELL(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
1460: {
1461: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1462: PetscInt shift,i,k,l,low,high,t,ii,row,col,nrow;
1463: PetscInt *cp,nonew=a->nonew,lastcol=-1;
1464: MatScalar *vp,value;
1468: for (k=0; k<m; k++) { /* loop over added rows */
1469: row = im[k];
1470: if (row < 0) continue;
1471: #if defined(PETSC_USE_DEBUG)
1472: if (row >= A->rmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Row too large: row %D max %D",row,A->rmap->n-1);
1473: #endif
1474: shift = a->sliidx[row>>3]+(row&0x07); /* starting index of the row */
1475: cp = a->colidx+shift; /* pointer to the row */
1476: vp = a->val+shift; /* pointer to the row */
1477: nrow = a->rlen[row];
1478: low = 0;
1479: high = nrow;
1481: for (l=0; l<n; l++) { /* loop over added columns */
1482: col = in[l];
1483: if (col<0) continue;
1484: #if defined(PETSC_USE_DEBUG)
1485: if (col >= A->cmap->n) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Col too large: row %D max %D",col,A->cmap->n-1);
1486: #endif
1487: if (a->roworiented) {
1488: value = v[l+k*n];
1489: } else {
1490: value = v[k+l*m];
1491: }
1492: if ((value == 0.0 && a->ignorezeroentries) && (is == ADD_VALUES)) continue;
1494: /* search in this row for the specified colmun, i indicates the column to be set */
1495: if (col <= lastcol) low = 0;
1496: else high = nrow;
1497: lastcol = col;
1498: while (high-low > 5) {
1499: t = (low+high)/2;
1500: if (*(cp+t*8) > col) high = t;
1501: else low = t;
1502: }
1503: for (i=low; i<high; i++) {
1504: if (*(cp+i*8) > col) break;
1505: if (*(cp+i*8) == col) {
1506: if (is == ADD_VALUES) *(vp+i*8) += value;
1507: else *(vp+i*8) = value;
1508: low = i + 1;
1509: goto noinsert;
1510: }
1511: }
1512: if (value == 0.0 && a->ignorezeroentries) goto noinsert;
1513: if (nonew == 1) goto noinsert;
1514: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1515: /* If the current row length exceeds the slice width (e.g. nrow==slice_width), allocate a new space, otherwise do nothing */
1516: MatSeqXSELLReallocateSELL(A,A->rmap->n,1,nrow,a->sliidx,row/8,row,col,a->colidx,a->val,cp,vp,nonew,MatScalar);
1517: /* add the new nonzero to the high position, shift the remaining elements in current row to the right by one slot */
1518: for (ii=nrow-1; ii>=i; ii--) {
1519: *(cp+(ii+1)*8) = *(cp+ii*8);
1520: *(vp+(ii+1)*8) = *(vp+ii*8);
1521: }
1522: a->rlen[row]++;
1523: *(cp+i*8) = col;
1524: *(vp+i*8) = value;
1525: a->nz++;
1526: A->nonzerostate++;
1527: low = i+1; high++; nrow++;
1528: noinsert:;
1529: }
1530: a->rlen[row] = nrow;
1531: }
1532: return(0);
1533: }
1535: PetscErrorCode MatCopy_SeqSELL(Mat A,Mat B,MatStructure str)
1536: {
1540: /* If the two matrices have the same copy implementation, use fast copy. */
1541: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1542: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1543: Mat_SeqSELL *b=(Mat_SeqSELL*)B->data;
1545: if (a->sliidx[a->totalslices] != b->sliidx[b->totalslices]) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Number of nonzeros in two matrices are different");
1546: PetscMemcpy(b->val,a->val,a->sliidx[a->totalslices]*sizeof(PetscScalar));
1547: } else {
1548: MatCopy_Basic(A,B,str);
1549: }
1550: return(0);
1551: }
1553: PetscErrorCode MatSetUp_SeqSELL(Mat A)
1554: {
1558: MatSeqSELLSetPreallocation(A,PETSC_DEFAULT,0);
1559: return(0);
1560: }
1562: PetscErrorCode MatSeqSELLGetArray_SeqSELL(Mat A,PetscScalar *array[])
1563: {
1564: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1567: *array = a->val;
1568: return(0);
1569: }
1571: PetscErrorCode MatSeqSELLRestoreArray_SeqSELL(Mat A,PetscScalar *array[])
1572: {
1574: return(0);
1575: }
1577: PetscErrorCode MatRealPart_SeqSELL(Mat A)
1578: {
1579: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1580: PetscInt i;
1581: MatScalar *aval=a->val;
1584: for (i=0; i<a->sliidx[a->totalslices]; i++) aval[i]=PetscRealPart(aval[i]);
1585: return(0);
1586: }
1588: PetscErrorCode MatImaginaryPart_SeqSELL(Mat A)
1589: {
1590: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1591: PetscInt i;
1592: MatScalar *aval=a->val;
1596: for (i=0; i<a->sliidx[a->totalslices]; i++) aval[i] = PetscImaginaryPart(aval[i]);
1597: MatSeqSELLInvalidateDiagonal(A);
1598: return(0);
1599: }
1601: PetscErrorCode MatScale_SeqSELL(Mat inA,PetscScalar alpha)
1602: {
1603: Mat_SeqSELL *a=(Mat_SeqSELL*)inA->data;
1604: MatScalar *aval=a->val;
1605: PetscScalar oalpha=alpha;
1606: PetscBLASInt one=1,size;
1610: PetscBLASIntCast(a->sliidx[a->totalslices],&size);
1611: PetscStackCallBLAS("BLASscal",BLASscal_(&size,&oalpha,aval,&one));
1612: PetscLogFlops(a->nz);
1613: MatSeqSELLInvalidateDiagonal(inA);
1614: return(0);
1615: }
1617: PetscErrorCode MatShift_SeqSELL(Mat Y,PetscScalar a)
1618: {
1619: Mat_SeqSELL *y=(Mat_SeqSELL*)Y->data;
1623: if (!Y->preallocated || !y->nz) {
1624: MatSeqSELLSetPreallocation(Y,1,NULL);
1625: }
1626: MatShift_Basic(Y,a);
1627: return(0);
1628: }
1630: PetscErrorCode MatSOR_SeqSELL(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1631: {
1632: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1633: PetscScalar *x,sum,*t;
1634: const MatScalar *idiag=0,*mdiag;
1635: const PetscScalar *b,*xb;
1636: PetscInt n,m=A->rmap->n,i,j,shift;
1637: const PetscInt *diag;
1638: PetscErrorCode ierr;
1641: its = its*lits;
1643: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1644: if (!a->idiagvalid) {MatInvertDiagonal_SeqSELL(A,omega,fshift);}
1645: a->fshift = fshift;
1646: a->omega = omega;
1648: diag = a->diag;
1649: t = a->ssor_work;
1650: idiag = a->idiag;
1651: mdiag = a->mdiag;
1653: VecGetArray(xx,&x);
1654: VecGetArrayRead(bb,&b);
1655: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1656: if (flag == SOR_APPLY_UPPER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_UPPER is not implemented");
1657: if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1658: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
1660: if (flag & SOR_ZERO_INITIAL_GUESS) {
1661: if ((flag & SOR_FORWARD_SWEEP) || (flag & SOR_LOCAL_FORWARD_SWEEP)) {
1662: for (i=0; i<m; i++) {
1663: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1664: sum = b[i];
1665: n = (diag[i]-shift)/8;
1666: for (j=0; j<n; j++) sum -= a->val[shift+j*8]*x[a->colidx[shift+j*8]];
1667: t[i] = sum;
1668: x[i] = sum*idiag[i];
1669: }
1670: xb = t;
1671: PetscLogFlops(a->nz);
1672: } else xb = b;
1673: if ((flag & SOR_BACKWARD_SWEEP) || (flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1674: for (i=m-1; i>=0; i--) {
1675: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1676: sum = xb[i];
1677: n = a->rlen[i]-(diag[i]-shift)/8-1;
1678: for (j=1; j<=n; j++) sum -= a->val[diag[i]+j*8]*x[a->colidx[diag[i]+j*8]];
1679: if (xb == b) {
1680: x[i] = sum*idiag[i];
1681: } else {
1682: x[i] = (1.-omega)*x[i]+sum*idiag[i]; /* omega in idiag */
1683: }
1684: }
1685: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1686: }
1687: its--;
1688: }
1689: while (its--) {
1690: if ((flag & SOR_FORWARD_SWEEP) || (flag & SOR_LOCAL_FORWARD_SWEEP)) {
1691: for (i=0; i<m; i++) {
1692: /* lower */
1693: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1694: sum = b[i];
1695: n = (diag[i]-shift)/8;
1696: for (j=0; j<n; j++) sum -= a->val[shift+j*8]*x[a->colidx[shift+j*8]];
1697: t[i] = sum; /* save application of the lower-triangular part */
1698: /* upper */
1699: n = a->rlen[i]-(diag[i]-shift)/8-1;
1700: for (j=1; j<=n; j++) sum -= a->val[diag[i]+j*8]*x[a->colidx[diag[i]+j*8]];
1701: x[i] = (1.-omega)*x[i]+sum*idiag[i]; /* omega in idiag */
1702: }
1703: xb = t;
1704: PetscLogFlops(2.0*a->nz);
1705: } else xb = b;
1706: if ((flag & SOR_BACKWARD_SWEEP) || (flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1707: for (i=m-1; i>=0; i--) {
1708: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1709: sum = xb[i];
1710: if (xb == b) {
1711: /* whole matrix (no checkpointing available) */
1712: n = a->rlen[i];
1713: for (j=0; j<n; j++) sum -= a->val[shift+j*8]*x[a->colidx[shift+j*8]];
1714: x[i] = (1.-omega)*x[i]+(sum+mdiag[i]*x[i])*idiag[i];
1715: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
1716: n = a->rlen[i]-(diag[i]-shift)/8-1;
1717: for (j=1; j<=n; j++) sum -= a->val[diag[i]+j*8]*x[a->colidx[diag[i]+j*8]];
1718: x[i] = (1.-omega)*x[i]+sum*idiag[i]; /* omega in idiag */
1719: }
1720: }
1721: if (xb == b) {
1722: PetscLogFlops(2.0*a->nz);
1723: } else {
1724: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1725: }
1726: }
1727: }
1728: VecRestoreArray(xx,&x);
1729: VecRestoreArrayRead(bb,&b);
1730: return(0);
1731: }
1733: /* -------------------------------------------------------------------*/
1734: static struct _MatOps MatOps_Values = {MatSetValues_SeqSELL,
1735: MatGetRow_SeqSELL,
1736: MatRestoreRow_SeqSELL,
1737: MatMult_SeqSELL,
1738: /* 4*/ MatMultAdd_SeqSELL,
1739: MatMultTranspose_SeqSELL,
1740: MatMultTransposeAdd_SeqSELL,
1741: 0,
1742: 0,
1743: 0,
1744: /* 10*/ 0,
1745: 0,
1746: 0,
1747: MatSOR_SeqSELL,
1748: 0,
1749: /* 15*/ MatGetInfo_SeqSELL,
1750: MatEqual_SeqSELL,
1751: MatGetDiagonal_SeqSELL,
1752: MatDiagonalScale_SeqSELL,
1753: 0,
1754: /* 20*/ 0,
1755: MatAssemblyEnd_SeqSELL,
1756: MatSetOption_SeqSELL,
1757: MatZeroEntries_SeqSELL,
1758: /* 24*/ 0,
1759: 0,
1760: 0,
1761: 0,
1762: 0,
1763: /* 29*/ MatSetUp_SeqSELL,
1764: 0,
1765: 0,
1766: 0,
1767: 0,
1768: /* 34*/ MatDuplicate_SeqSELL,
1769: 0,
1770: 0,
1771: 0,
1772: 0,
1773: /* 39*/ 0,
1774: 0,
1775: 0,
1776: MatGetValues_SeqSELL,
1777: MatCopy_SeqSELL,
1778: /* 44*/ 0,
1779: MatScale_SeqSELL,
1780: MatShift_SeqSELL,
1781: 0,
1782: 0,
1783: /* 49*/ 0,
1784: 0,
1785: 0,
1786: 0,
1787: 0,
1788: /* 54*/ MatFDColoringCreate_SeqXAIJ,
1789: 0,
1790: 0,
1791: 0,
1792: 0,
1793: /* 59*/ 0,
1794: MatDestroy_SeqSELL,
1795: MatView_SeqSELL,
1796: 0,
1797: 0,
1798: /* 64*/ 0,
1799: 0,
1800: 0,
1801: 0,
1802: 0,
1803: /* 69*/ 0,
1804: 0,
1805: 0,
1806: 0,
1807: 0,
1808: /* 74*/ 0,
1809: MatFDColoringApply_AIJ, /* reuse the FDColoring function for AIJ */
1810: 0,
1811: 0,
1812: 0,
1813: /* 79*/ 0,
1814: 0,
1815: 0,
1816: 0,
1817: 0,
1818: /* 84*/ 0,
1819: 0,
1820: 0,
1821: 0,
1822: 0,
1823: /* 89*/ 0,
1824: 0,
1825: 0,
1826: 0,
1827: 0,
1828: /* 94*/ 0,
1829: 0,
1830: 0,
1831: 0,
1832: 0,
1833: /* 99*/ 0,
1834: 0,
1835: 0,
1836: MatConjugate_SeqSELL,
1837: 0,
1838: /*104*/ 0,
1839: 0,
1840: 0,
1841: 0,
1842: 0,
1843: /*109*/ 0,
1844: 0,
1845: 0,
1846: 0,
1847: MatMissingDiagonal_SeqSELL,
1848: /*114*/ 0,
1849: 0,
1850: 0,
1851: 0,
1852: 0,
1853: /*119*/ 0,
1854: 0,
1855: 0,
1856: 0,
1857: 0,
1858: /*124*/ 0,
1859: 0,
1860: 0,
1861: 0,
1862: 0,
1863: /*129*/ 0,
1864: 0,
1865: 0,
1866: 0,
1867: 0,
1868: /*134*/ 0,
1869: 0,
1870: 0,
1871: 0,
1872: 0,
1873: /*139*/ 0,
1874: 0,
1875: 0,
1876: MatFDColoringSetUp_SeqXAIJ,
1877: 0,
1878: /*144*/0
1879: };
1881: PetscErrorCode MatStoreValues_SeqSELL(Mat mat)
1882: {
1883: Mat_SeqSELL *a=(Mat_SeqSELL*)mat->data;
1887: if (!a->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
1889: /* allocate space for values if not already there */
1890: if (!a->saved_values) {
1891: PetscMalloc1(a->sliidx[a->totalslices]+1,&a->saved_values);
1892: PetscLogObjectMemory((PetscObject)mat,(a->sliidx[a->totalslices]+1)*sizeof(PetscScalar));
1893: }
1895: /* copy values over */
1896: PetscMemcpy(a->saved_values,a->val,a->sliidx[a->totalslices]*sizeof(PetscScalar));
1897: return(0);
1898: }
1900: PetscErrorCode MatRetrieveValues_SeqSELL(Mat mat)
1901: {
1902: Mat_SeqSELL *a=(Mat_SeqSELL*)mat->data;
1906: if (!a->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
1907: if (!a->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
1908: /* copy values over */
1909: PetscMemcpy(a->val,a->saved_values,a->sliidx[a->totalslices]*sizeof(PetscScalar));
1910: return(0);
1911: }
1913: /*@C
1914: MatSeqSELLRestoreArray - returns access to the array where the data for a MATSEQSELL matrix is stored obtained by MatSeqSELLGetArray()
1916: Not Collective
1918: Input Parameters:
1919: . mat - a MATSEQSELL matrix
1920: . array - pointer to the data
1922: Level: intermediate
1924: .seealso: MatSeqSELLGetArray(), MatSeqSELLRestoreArrayF90()
1925: @*/
1926: PetscErrorCode MatSeqSELLRestoreArray(Mat A,PetscScalar **array)
1927: {
1931: PetscUseMethod(A,"MatSeqSELLRestoreArray_C",(Mat,PetscScalar**),(A,array));
1932: return(0);
1933: }
1935: PETSC_EXTERN PetscErrorCode MatCreate_SeqSELL(Mat B)
1936: {
1937: Mat_SeqSELL *b;
1938: PetscMPIInt size;
1942: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
1943: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
1945: PetscNewLog(B,&b);
1947: B->data = (void*)b;
1949: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
1951: b->row = 0;
1952: b->col = 0;
1953: b->icol = 0;
1954: b->reallocs = 0;
1955: b->ignorezeroentries = PETSC_FALSE;
1956: b->roworiented = PETSC_TRUE;
1957: b->nonew = 0;
1958: b->diag = 0;
1959: b->solve_work = 0;
1960: B->spptr = 0;
1961: b->saved_values = 0;
1962: b->idiag = 0;
1963: b->mdiag = 0;
1964: b->ssor_work = 0;
1965: b->omega = 1.0;
1966: b->fshift = 0.0;
1967: b->idiagvalid = PETSC_FALSE;
1968: b->keepnonzeropattern = PETSC_FALSE;
1970: PetscObjectChangeTypeName((PetscObject)B,MATSEQSELL);
1971: PetscObjectComposeFunction((PetscObject)B,"MatSeqSELLGetArray_C",MatSeqSELLGetArray_SeqSELL);
1972: PetscObjectComposeFunction((PetscObject)B,"MatSeqSELLRestoreArray_C",MatSeqSELLRestoreArray_SeqSELL);
1973: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqSELL);
1974: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqSELL);
1975: PetscObjectComposeFunction((PetscObject)B,"MatSeqSELLSetPreallocation_C",MatSeqSELLSetPreallocation_SeqSELL);
1976: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqsell_seqaij_C",MatConvert_SeqSELL_SeqAIJ);
1977: return(0);
1978: }
1980: /*
1981: Given a matrix generated with MatGetFactor() duplicates all the information in A into B
1982: */
1983: PetscErrorCode MatDuplicateNoCreate_SeqSELL(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
1984: {
1985: Mat_SeqSELL *c,*a=(Mat_SeqSELL*)A->data;
1986: PetscInt i,m=A->rmap->n;
1987: PetscInt totalslices=a->totalslices;
1991: c = (Mat_SeqSELL*)C->data;
1993: C->factortype = A->factortype;
1994: c->row = 0;
1995: c->col = 0;
1996: c->icol = 0;
1997: c->reallocs = 0;
1999: C->assembled = PETSC_TRUE;
2001: PetscLayoutReference(A->rmap,&C->rmap);
2002: PetscLayoutReference(A->cmap,&C->cmap);
2004: PetscMalloc1(8*totalslices,&c->rlen);
2005: PetscLogObjectMemory((PetscObject)C,m*sizeof(PetscInt));
2006: PetscMalloc1(totalslices+1,&c->sliidx);
2007: PetscLogObjectMemory((PetscObject)C, (totalslices+1)*sizeof(PetscInt));
2009: for (i=0; i<m; i++) c->rlen[i] = a->rlen[i];
2010: for (i=0; i<totalslices+1; i++) c->sliidx[i] = a->sliidx[i];
2012: /* allocate the matrix space */
2013: if (mallocmatspace) {
2014: PetscMalloc2(a->maxallocmat,&c->val,a->maxallocmat,&c->colidx);
2015: PetscLogObjectMemory((PetscObject)C,a->maxallocmat*(sizeof(PetscScalar)+sizeof(PetscInt)));
2017: c->singlemalloc = PETSC_TRUE;
2019: if (m > 0) {
2020: PetscMemcpy(c->colidx,a->colidx,(a->maxallocmat)*sizeof(PetscInt));
2021: if (cpvalues == MAT_COPY_VALUES) {
2022: PetscMemcpy(c->val,a->val,a->maxallocmat*sizeof(PetscScalar));
2023: } else {
2024: PetscMemzero(c->val,a->maxallocmat*sizeof(PetscScalar));
2025: }
2026: }
2027: }
2029: c->ignorezeroentries = a->ignorezeroentries;
2030: c->roworiented = a->roworiented;
2031: c->nonew = a->nonew;
2032: if (a->diag) {
2033: PetscMalloc1(m,&c->diag);
2034: PetscLogObjectMemory((PetscObject)C,m*sizeof(PetscInt));
2035: for (i=0; i<m; i++) {
2036: c->diag[i] = a->diag[i];
2037: }
2038: } else c->diag = 0;
2040: c->solve_work = 0;
2041: c->saved_values = 0;
2042: c->idiag = 0;
2043: c->ssor_work = 0;
2044: c->keepnonzeropattern = a->keepnonzeropattern;
2045: c->free_val = PETSC_TRUE;
2046: c->free_colidx = PETSC_TRUE;
2048: c->maxallocmat = a->maxallocmat;
2049: c->maxallocrow = a->maxallocrow;
2050: c->rlenmax = a->rlenmax;
2051: c->nz = a->nz;
2052: C->preallocated = PETSC_TRUE;
2054: c->nonzerorowcnt = a->nonzerorowcnt;
2055: C->nonzerostate = A->nonzerostate;
2057: PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
2058: return(0);
2059: }
2061: PetscErrorCode MatDuplicate_SeqSELL(Mat A,MatDuplicateOption cpvalues,Mat *B)
2062: {
2066: MatCreate(PetscObjectComm((PetscObject)A),B);
2067: MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
2068: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
2069: MatSetBlockSizesFromMats(*B,A,A);
2070: }
2071: MatSetType(*B,((PetscObject)A)->type_name);
2072: MatDuplicateNoCreate_SeqSELL(*B,A,cpvalues,PETSC_TRUE);
2073: return(0);
2074: }
2076: /*@C
2077: MatCreateSeqSELL - Creates a sparse matrix in SELL format.
2079: Collective on MPI_Comm
2081: Input Parameters:
2082: + comm - MPI communicator, set to PETSC_COMM_SELF
2083: . m - number of rows
2084: . n - number of columns
2085: . rlenmax - maximum number of nonzeros in a row
2086: - rlen - array containing the number of nonzeros in the various rows
2087: (possibly different for each row) or NULL
2089: Output Parameter:
2090: . A - the matrix
2092: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
2093: MatXXXXSetPreallocation() paradigm instead of this routine directly.
2094: [MatXXXXSetPreallocation() is, for example, MatSeqSELLSetPreallocation]
2096: Notes:
2097: If nnz is given then nz is ignored
2099: Specify the preallocated storage with either rlenmax or rlen (not both).
2100: Set rlenmax=PETSC_DEFAULT and rlen=NULL for PETSc to control dynamic memory
2101: allocation. For large problems you MUST preallocate memory or you
2102: will get TERRIBLE performance, see the users' manual chapter on matrices.
2104: Level: intermediate
2106: .seealso: MatCreate(), MatCreateSELL(), MatSetValues(), MatCreateSeqSELLWithArrays()
2108: @*/
2109: PetscErrorCode MatCreateSeqSELL(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt maxallocrow,const PetscInt rlen[],Mat *A)
2110: {
2114: MatCreate(comm,A);
2115: MatSetSizes(*A,m,n,m,n);
2116: MatSetType(*A,MATSEQSELL);
2117: MatSeqSELLSetPreallocation_SeqSELL(*A,maxallocrow,rlen);
2118: return(0);
2119: }
2121: PetscErrorCode MatEqual_SeqSELL(Mat A,Mat B,PetscBool * flg)
2122: {
2123: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data,*b=(Mat_SeqSELL*)B->data;
2124: PetscInt totalslices=a->totalslices;
2128: /* If the matrix dimensions are not equal,or no of nonzeros */
2129: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz) || (a->rlenmax != b->rlenmax)) {
2130: *flg = PETSC_FALSE;
2131: return(0);
2132: }
2133: /* if the a->colidx are the same */
2134: PetscMemcmp(a->colidx,b->colidx,a->sliidx[totalslices]*sizeof(PetscInt),flg);
2135: if (!*flg) return(0);
2136: /* if a->val are the same */
2137: PetscMemcmp(a->val,b->val,a->sliidx[totalslices]*sizeof(PetscScalar),flg);
2138: return(0);
2139: }
2141: PetscErrorCode MatSeqSELLInvalidateDiagonal(Mat A)
2142: {
2143: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
2146: a->idiagvalid = PETSC_FALSE;
2147: return(0);
2148: }
2150: PetscErrorCode MatConjugate_SeqSELL(Mat A)
2151: {
2152: #if defined(PETSC_USE_COMPLEX)
2153: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
2154: PetscInt i;
2155: PetscScalar *val = a->val;
2158: for (i=0; i<a->sliidx[a->totalslices]; i++) {
2159: val[i] = PetscConj(val[i]);
2160: }
2161: #else
2163: #endif
2164: return(0);
2165: }