Actual source code: sell.c
petsc-3.9.1 2018-04-29
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 accuate as possible.
67: If it is underesitmated, 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,*cols;
230: PetscScalar *vals;
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;
261: if (A->rmap->bs > 1) {
262: MatConvert_Basic(A,newtype,reuse,newmat);
263: return(0);
264: }
266: if (reuse == MAT_REUSE_MATRIX) {
267: B = *newmat;
268: } else {
269: /* Can we just use ilen? */
270: PetscMalloc1(m,&rowlengths);
271: for (i=0; i<m; i++) {
272: rowlengths[i] = ai[i+1] - ai[i];
273: }
275: MatCreate(PetscObjectComm((PetscObject)A),&B);
276: MatSetSizes(B,m,n,m,n);
277: MatSetType(B,MATSEQSELL);
278: MatSeqSELLSetPreallocation(B,0,rowlengths);
279: PetscFree(rowlengths);
280: }
282: for (row=0; row<m; row++) {
283: MatGetRow(A,row,&ncols,&cols,&vals);
284: MatSetValues(B,1,&row,ncols,cols,vals,INSERT_VALUES);
285: MatRestoreRow(A,row,&ncols,&cols,&vals);
286: }
287: MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY);
288: MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY);
289: B->rmap->bs = A->rmap->bs;
291: if (reuse == MAT_INPLACE_MATRIX) {
292: MatHeaderReplace(A,&B);
293: } else {
294: *newmat = B;
295: }
296: return(0);
297: }
299: PetscErrorCode MatMult_SeqSELL(Mat A,Vec xx,Vec yy)
300: {
301: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
302: PetscScalar *y;
303: const PetscScalar *x;
304: const MatScalar *aval=a->val;
305: PetscInt totalslices=a->totalslices;
306: const PetscInt *acolidx=a->colidx;
307: PetscInt i,j;
308: PetscErrorCode ierr;
309: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
310: __m512d vec_x,vec_y,vec_vals;
311: __m256i vec_idx;
312: __mmask8 mask;
313: __m512d vec_x2,vec_y2,vec_vals2,vec_x3,vec_y3,vec_vals3,vec_x4,vec_y4,vec_vals4;
314: __m256i vec_idx2,vec_idx3,vec_idx4;
315: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
316: __m128i vec_idx;
317: __m256d vec_x,vec_y,vec_y2,vec_vals;
318: MatScalar yval;
319: PetscInt r,rows_left,row,nnz_in_row;
320: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
321: __m128d vec_x_tmp;
322: __m256d vec_x,vec_y,vec_y2,vec_vals;
323: MatScalar yval;
324: PetscInt r,rows_left,row,nnz_in_row;
325: #else
326: PetscScalar sum[8];
327: #endif
329: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
330: #pragma disjoint(*x,*y,*aval)
331: #endif
334: VecGetArrayRead(xx,&x);
335: VecGetArray(yy,&y);
336: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
337: for (i=0; i<totalslices; i++) { /* loop over slices */
338: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
339: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
341: vec_y = _mm512_setzero_pd();
342: vec_y2 = _mm512_setzero_pd();
343: vec_y3 = _mm512_setzero_pd();
344: vec_y4 = _mm512_setzero_pd();
346: j = a->sliidx[i]>>3; /* 8 bytes are read at each time, corresponding to a slice columnn */
347: switch ((a->sliidx[i+1]-a->sliidx[i])/8 & 3) {
348: case 3:
349: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
350: acolidx += 8; aval += 8;
351: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
352: acolidx += 8; aval += 8;
353: AVX512_Mult_Private(vec_idx3,vec_x3,vec_vals3,vec_y3);
354: acolidx += 8; aval += 8;
355: j += 3;
356: break;
357: case 2:
358: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
359: acolidx += 8; aval += 8;
360: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
361: acolidx += 8; aval += 8;
362: j += 2;
363: break;
364: case 1:
365: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
366: acolidx += 8; aval += 8;
367: j += 1;
368: break;
369: }
370: #pragma novector
371: for (; j<(a->sliidx[i+1]>>3); j+=4) {
372: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
373: acolidx += 8; aval += 8;
374: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
375: acolidx += 8; aval += 8;
376: AVX512_Mult_Private(vec_idx3,vec_x3,vec_vals3,vec_y3);
377: acolidx += 8; aval += 8;
378: AVX512_Mult_Private(vec_idx4,vec_x4,vec_vals4,vec_y4);
379: acolidx += 8; aval += 8;
380: }
382: vec_y = _mm512_add_pd(vec_y,vec_y2);
383: vec_y = _mm512_add_pd(vec_y,vec_y3);
384: vec_y = _mm512_add_pd(vec_y,vec_y4);
385: if (i == totalslices-1 && A->rmap->n & 0x07) { /* if last slice has padding rows */
386: mask = (__mmask8)(0xff >> (8-(A->rmap->n & 0x07)));
387: _mm512_mask_storeu_pd(&y[8*i],mask,vec_y);
388: } else {
389: _mm512_storeu_pd(&y[8*i],vec_y);
390: }
391: }
392: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX2__) && defined(__FMA__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
393: for (i=0; i<totalslices; i++) { /* loop over full slices */
394: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
395: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
397: /* last slice may have padding rows. Don't use vectorization. */
398: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
399: rows_left = A->rmap->n - 8*i;
400: for (r=0; r<rows_left; ++r) {
401: yval = (MatScalar)0;
402: row = 8*i + r;
403: nnz_in_row = a->rlen[row];
404: for (j=0; j<nnz_in_row; ++j) yval += aval[8*j+r] * x[acolidx[8*j+r]];
405: y[row] = yval;
406: }
407: break;
408: }
410: vec_y = _mm256_setzero_pd();
411: vec_y2 = _mm256_setzero_pd();
413: /* Process slice of height 8 (512 bits) via two subslices of height 4 (256 bits) via AVX */
414: #pragma novector
415: #pragma unroll(2)
416: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
417: AVX2_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
418: aval += 4; acolidx += 4;
419: AVX2_Mult_Private(vec_idx,vec_x,vec_vals,vec_y2);
420: aval += 4; acolidx += 4;
421: }
423: _mm256_storeu_pd(y+i*8,vec_y);
424: _mm256_storeu_pd(y+i*8+4,vec_y2);
425: }
426: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
427: for (i=0; i<totalslices; i++) { /* loop over full slices */
428: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
429: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
431: vec_y = _mm256_setzero_pd();
432: vec_y2 = _mm256_setzero_pd();
434: /* last slice may have padding rows. Don't use vectorization. */
435: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
436: rows_left = A->rmap->n - 8*i;
437: for (r=0; r<rows_left; ++r) {
438: yval = (MatScalar)0;
439: row = 8*i + r;
440: nnz_in_row = a->rlen[row];
441: for (j=0; j<nnz_in_row; ++j) yval += aval[8*j + r] * x[acolidx[8*j + r]];
442: y[row] = yval;
443: }
444: break;
445: }
447: /* Process slice of height 8 (512 bits) via two subslices of height 4 (256 bits) via AVX */
448: #pragma novector
449: #pragma unroll(2)
450: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
451: vec_vals = _mm256_loadu_pd(aval);
452: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
453: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
454: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,0);
455: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
456: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
457: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,1);
458: vec_y = _mm256_add_pd(_mm256_mul_pd(vec_x,vec_vals),vec_y);
459: aval += 4;
461: vec_vals = _mm256_loadu_pd(aval);
462: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
463: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
464: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,0);
465: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
466: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
467: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,1);
468: vec_y2 = _mm256_add_pd(_mm256_mul_pd(vec_x,vec_vals),vec_y2);
469: aval += 4;
470: }
472: _mm256_storeu_pd(y + i*8, vec_y);
473: _mm256_storeu_pd(y + i*8 + 4, vec_y2);
474: }
475: #else
476: for (i=0; i<totalslices; i++) { /* loop over slices */
477: for (j=0; j<8; j++) sum[j] = 0.0;
478: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
479: sum[0] += aval[j] * x[acolidx[j]];
480: sum[1] += aval[j+1] * x[acolidx[j+1]];
481: sum[2] += aval[j+2] * x[acolidx[j+2]];
482: sum[3] += aval[j+3] * x[acolidx[j+3]];
483: sum[4] += aval[j+4] * x[acolidx[j+4]];
484: sum[5] += aval[j+5] * x[acolidx[j+5]];
485: sum[6] += aval[j+6] * x[acolidx[j+6]];
486: sum[7] += aval[j+7] * x[acolidx[j+7]];
487: }
488: if (i == totalslices-1 && (A->rmap->n & 0x07)) { /* if last slice has padding rows */
489: for(j=0; j<(A->rmap->n & 0x07); j++) y[8*i+j] = sum[j];
490: } else {
491: for(j=0; j<8; j++) y[8*i+j] = sum[j];
492: }
493: }
494: #endif
496: PetscLogFlops(2.0*a->nz-a->nonzerorowcnt); /* theoretical minimal FLOPs */
497: VecRestoreArrayRead(xx,&x);
498: VecRestoreArray(yy,&y);
499: return(0);
500: }
502: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
503: PetscErrorCode MatMultAdd_SeqSELL(Mat A,Vec xx,Vec yy,Vec zz)
504: {
505: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
506: PetscScalar *y,*z;
507: const PetscScalar *x;
508: const MatScalar *aval=a->val;
509: PetscInt totalslices=a->totalslices;
510: const PetscInt *acolidx=a->colidx;
511: PetscInt i,j;
512: PetscErrorCode ierr;
513: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
514: __m512d vec_x,vec_y,vec_vals;
515: __m256i vec_idx;
516: __mmask8 mask;
517: __m512d vec_x2,vec_y2,vec_vals2,vec_x3,vec_y3,vec_vals3,vec_x4,vec_y4,vec_vals4;
518: __m256i vec_idx2,vec_idx3,vec_idx4;
519: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
520: __m128d vec_x_tmp;
521: __m256d vec_x,vec_y,vec_y2,vec_vals;
522: MatScalar yval;
523: PetscInt r,row,nnz_in_row;
524: #else
525: PetscScalar sum[8];
526: #endif
528: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
529: #pragma disjoint(*x,*y,*aval)
530: #endif
533: VecGetArrayRead(xx,&x);
534: VecGetArrayPair(yy,zz,&y,&z);
535: #if defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
536: for (i=0; i<totalslices; i++) { /* loop over slices */
537: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
538: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
540: if (i == totalslices-1 && A->rmap->n & 0x07) { /* if last slice has padding rows */
541: mask = (__mmask8)(0xff >> (8-(A->rmap->n & 0x07)));
542: vec_y = _mm512_mask_loadu_pd(vec_y,mask,&y[8*i]);
543: } else {
544: vec_y = _mm512_loadu_pd(&y[8*i]);
545: }
546: vec_y2 = _mm512_setzero_pd();
547: vec_y3 = _mm512_setzero_pd();
548: vec_y4 = _mm512_setzero_pd();
550: j = a->sliidx[i]>>3; /* 8 bytes are read at each time, corresponding to a slice columnn */
551: switch ((a->sliidx[i+1]-a->sliidx[i])/8 & 3) {
552: case 3:
553: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
554: acolidx += 8; aval += 8;
555: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
556: acolidx += 8; aval += 8;
557: AVX512_Mult_Private(vec_idx3,vec_x3,vec_vals3,vec_y3);
558: acolidx += 8; aval += 8;
559: j += 3;
560: break;
561: case 2:
562: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
563: acolidx += 8; aval += 8;
564: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
565: acolidx += 8; aval += 8;
566: j += 2;
567: break;
568: case 1:
569: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
570: acolidx += 8; aval += 8;
571: j += 1;
572: break;
573: }
574: #pragma novector
575: for (; j<(a->sliidx[i+1]>>3); j+=4) {
576: AVX512_Mult_Private(vec_idx,vec_x,vec_vals,vec_y);
577: acolidx += 8; aval += 8;
578: AVX512_Mult_Private(vec_idx2,vec_x2,vec_vals2,vec_y2);
579: acolidx += 8; aval += 8;
580: AVX512_Mult_Private(vec_idx3,vec_x3,vec_vals3,vec_y3);
581: acolidx += 8; aval += 8;
582: AVX512_Mult_Private(vec_idx4,vec_x4,vec_vals4,vec_y4);
583: acolidx += 8; aval += 8;
584: }
586: vec_y = _mm512_add_pd(vec_y,vec_y2);
587: vec_y = _mm512_add_pd(vec_y,vec_y3);
588: vec_y = _mm512_add_pd(vec_y,vec_y4);
589: if (i == totalslices-1 && A->rmap->n & 0x07) { /* if last slice has padding rows */
590: _mm512_mask_storeu_pd(&z[8*i],mask,vec_y);
591: } else {
592: _mm512_storeu_pd(&z[8*i],vec_y);
593: }
594: }
595: #elif defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES)
596: for (i=0; i<totalslices; i++) { /* loop over full slices */
597: PetscPrefetchBlock(acolidx,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
598: PetscPrefetchBlock(aval,a->sliidx[i+1]-a->sliidx[i],0,PETSC_PREFETCH_HINT_T0);
600: /* last slice may have padding rows. Don't use vectorization. */
601: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
602: for (r=0; r<(A->rmap->n & 0x07); ++r) {
603: row = 8*i + r;
604: yval = (MatScalar)0.0;
605: nnz_in_row = a->rlen[row];
606: for (j=0; j<nnz_in_row; ++j) yval += aval[8*j+r] * x[acolidx[8*j+r]];
607: z[row] = y[row] + yval;
608: }
609: break;
610: }
612: vec_y = _mm256_loadu_pd(y+8*i);
613: vec_y2 = _mm256_loadu_pd(y+8*i+4);
615: /* Process slice of height 8 (512 bits) via two subslices of height 4 (256 bits) via AVX */
616: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
617: vec_vals = _mm256_loadu_pd(aval);
618: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
619: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
620: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,0);
621: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
622: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
623: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,1);
624: vec_y = _mm256_add_pd(_mm256_mul_pd(vec_x,vec_vals),vec_y);
625: aval += 4;
627: vec_vals = _mm256_loadu_pd(aval);
628: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
629: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
630: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,0);
631: vec_x_tmp = _mm_loadl_pd(vec_x_tmp, x + *acolidx++);
632: vec_x_tmp = _mm_loadh_pd(vec_x_tmp, x + *acolidx++);
633: vec_x = _mm256_insertf128_pd(vec_x,vec_x_tmp,1);
634: vec_y2 = _mm256_add_pd(_mm256_mul_pd(vec_x,vec_vals),vec_y2);
635: aval += 4;
636: }
638: _mm256_storeu_pd(z+i*8,vec_y);
639: _mm256_storeu_pd(z+i*8+4,vec_y2);
640: }
641: #else
642: for (i=0; i<totalslices; i++) { /* loop over slices */
643: for (j=0; j<8; j++) sum[j] = 0.0;
644: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
645: sum[0] += aval[j] * x[acolidx[j]];
646: sum[1] += aval[j+1] * x[acolidx[j+1]];
647: sum[2] += aval[j+2] * x[acolidx[j+2]];
648: sum[3] += aval[j+3] * x[acolidx[j+3]];
649: sum[4] += aval[j+4] * x[acolidx[j+4]];
650: sum[5] += aval[j+5] * x[acolidx[j+5]];
651: sum[6] += aval[j+6] * x[acolidx[j+6]];
652: sum[7] += aval[j+7] * x[acolidx[j+7]];
653: }
654: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
655: for (j=0; j<(A->rmap->n & 0x07); j++) z[8*i+j] = y[8*i+j] + sum[j];
656: } else {
657: for (j=0; j<8; j++) z[8*i+j] = y[8*i+j] + sum[j];
658: }
659: }
660: #endif
662: PetscLogFlops(2.0*a->nz);
663: VecRestoreArrayRead(xx,&x);
664: VecRestoreArrayPair(yy,zz,&y,&z);
665: return(0);
666: }
668: PetscErrorCode MatMultTransposeAdd_SeqSELL(Mat A,Vec xx,Vec zz,Vec yy)
669: {
670: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
671: PetscScalar *y;
672: const PetscScalar *x;
673: const MatScalar *aval=a->val;
674: const PetscInt *acolidx=a->colidx;
675: PetscInt i,j,r,row,nnz_in_row,totalslices=a->totalslices;
676: PetscErrorCode ierr;
678: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
679: #pragma disjoint(*x,*y,*aval)
680: #endif
683: if (A->symmetric) {
684: MatMultAdd_SeqSELL(A,xx,zz,yy);
685: return(0);
686: }
687: if (zz != yy) { VecCopy(zz,yy); }
688: VecGetArrayRead(xx,&x);
689: VecGetArray(yy,&y);
690: for (i=0; i<a->totalslices; i++) { /* loop over slices */
691: if (i == totalslices-1 && (A->rmap->n & 0x07)) {
692: for (r=0; r<(A->rmap->n & 0x07); ++r) {
693: row = 8*i + r;
694: nnz_in_row = a->rlen[row];
695: for (j=0; j<nnz_in_row; ++j) y[acolidx[8*j+r]] += aval[8*j+r] * x[row];
696: }
697: break;
698: }
699: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j+=8) {
700: y[acolidx[j]] += aval[j] * x[8*i];
701: y[acolidx[j+1]] += aval[j+1] * x[8*i+1];
702: y[acolidx[j+2]] += aval[j+2] * x[8*i+2];
703: y[acolidx[j+3]] += aval[j+3] * x[8*i+3];
704: y[acolidx[j+4]] += aval[j+4] * x[8*i+4];
705: y[acolidx[j+5]] += aval[j+5] * x[8*i+5];
706: y[acolidx[j+6]] += aval[j+6] * x[8*i+6];
707: y[acolidx[j+7]] += aval[j+7] * x[8*i+7];
708: }
709: }
710: PetscLogFlops(2.0*a->sliidx[a->totalslices]);
711: VecRestoreArrayRead(xx,&x);
712: VecRestoreArray(yy,&y);
713: return(0);
714: }
716: PetscErrorCode MatMultTranspose_SeqSELL(Mat A,Vec xx,Vec yy)
717: {
721: if (A->symmetric) {
722: MatMult_SeqSELL(A,xx,yy);
723: } else {
724: VecSet(yy,0.0);
725: MatMultTransposeAdd_SeqSELL(A,xx,yy,yy);
726: }
727: return(0);
728: }
730: /*
731: Checks for missing diagonals
732: */
733: PetscErrorCode MatMissingDiagonal_SeqSELL(Mat A,PetscBool *missing,PetscInt *d)
734: {
735: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
736: PetscInt *diag,i;
739: *missing = PETSC_FALSE;
740: if (A->rmap->n > 0 && !(a->colidx)) {
741: *missing = PETSC_TRUE;
742: if (d) *d = 0;
743: PetscInfo(A,"Matrix has no entries therefore is missing diagonal\n");
744: } else {
745: diag = a->diag;
746: for (i=0; i<A->rmap->n; i++) {
747: if (diag[i] == -1) {
748: *missing = PETSC_TRUE;
749: if (d) *d = i;
750: PetscInfo1(A,"Matrix is missing diagonal number %D\n",i);
751: break;
752: }
753: }
754: }
755: return(0);
756: }
758: PetscErrorCode MatMarkDiagonal_SeqSELL(Mat A)
759: {
760: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
761: PetscInt i,j,m=A->rmap->n,shift;
765: if (!a->diag) {
766: PetscMalloc1(m,&a->diag);
767: PetscLogObjectMemory((PetscObject)A,m*sizeof(PetscInt));
768: a->free_diag = PETSC_TRUE;
769: }
770: for (i=0; i<m; i++) { /* loop over rows */
771: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
772: a->diag[i] = -1;
773: for (j=0; j<a->rlen[i]; j++) {
774: if (a->colidx[shift+j*8] == i) {
775: a->diag[i] = shift+j*8;
776: break;
777: }
778: }
779: }
780: return(0);
781: }
783: /*
784: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
785: */
786: PetscErrorCode MatInvertDiagonal_SeqSELL(Mat A,PetscScalar omega,PetscScalar fshift)
787: {
788: Mat_SeqSELL *a=(Mat_SeqSELL*) A->data;
789: PetscInt i,*diag,m = A->rmap->n;
790: MatScalar *val = a->val;
791: PetscScalar *idiag,*mdiag;
795: if (a->idiagvalid) return(0);
796: MatMarkDiagonal_SeqSELL(A);
797: diag = a->diag;
798: if (!a->idiag) {
799: PetscMalloc3(m,&a->idiag,m,&a->mdiag,m,&a->ssor_work);
800: PetscLogObjectMemory((PetscObject)A, 3*m*sizeof(PetscScalar));
801: val = a->val;
802: }
803: mdiag = a->mdiag;
804: idiag = a->idiag;
806: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
807: for (i=0; i<m; i++) {
808: mdiag[i] = val[diag[i]];
809: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
810: if (PetscRealPart(fshift)) {
811: PetscInfo1(A,"Zero diagonal on row %D\n",i);
812: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
813: A->factorerror_zeropivot_value = 0.0;
814: A->factorerror_zeropivot_row = i;
815: } else SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_ARG_INCOMP,"Zero diagonal on row %D",i);
816: }
817: idiag[i] = 1.0/val[diag[i]];
818: }
819: PetscLogFlops(m);
820: } else {
821: for (i=0; i<m; i++) {
822: mdiag[i] = val[diag[i]];
823: idiag[i] = omega/(fshift + val[diag[i]]);
824: }
825: PetscLogFlops(2.0*m);
826: }
827: a->idiagvalid = PETSC_TRUE;
828: return(0);
829: }
831: PetscErrorCode MatZeroEntries_SeqSELL(Mat A)
832: {
833: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
837: PetscMemzero(a->val,(a->sliidx[a->totalslices])*sizeof(PetscScalar));
838: MatSeqSELLInvalidateDiagonal(A);
839: return(0);
840: }
842: PetscErrorCode MatDestroy_SeqSELL(Mat A)
843: {
844: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
848: #if defined(PETSC_USE_LOG)
849: PetscLogObjectState((PetscObject)A,"Rows=%D, Cols=%D, NZ=%D",A->rmap->n,A->cmap->n,a->nz);
850: #endif
851: MatSeqXSELLFreeSELL(A,&a->val,&a->colidx);
852: ISDestroy(&a->row);
853: ISDestroy(&a->col);
854: PetscFree(a->diag);
855: PetscFree(a->rlen);
856: PetscFree(a->sliidx);
857: PetscFree3(a->idiag,a->mdiag,a->ssor_work);
858: PetscFree(a->solve_work);
859: ISDestroy(&a->icol);
860: PetscFree(a->saved_values);
861: PetscFree2(a->getrowcols,a->getrowvals);
863: PetscFree(A->data);
865: PetscObjectChangeTypeName((PetscObject)A,0);
866: PetscObjectComposeFunction((PetscObject)A,"MatStoreValues_C",NULL);
867: PetscObjectComposeFunction((PetscObject)A,"MatRetrieveValues_C",NULL);
868: #if defined(PETSC_HAVE_ELEMENTAL)
869: #endif
870: PetscObjectComposeFunction((PetscObject)A,"MatSeqSELLSetPreallocation_C",NULL);
871: return(0);
872: }
874: PetscErrorCode MatSetOption_SeqSELL(Mat A,MatOption op,PetscBool flg)
875: {
876: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
880: switch (op) {
881: case MAT_ROW_ORIENTED:
882: a->roworiented = flg;
883: break;
884: case MAT_KEEP_NONZERO_PATTERN:
885: a->keepnonzeropattern = flg;
886: break;
887: case MAT_NEW_NONZERO_LOCATIONS:
888: a->nonew = (flg ? 0 : 1);
889: break;
890: case MAT_NEW_NONZERO_LOCATION_ERR:
891: a->nonew = (flg ? -1 : 0);
892: break;
893: case MAT_NEW_NONZERO_ALLOCATION_ERR:
894: a->nonew = (flg ? -2 : 0);
895: break;
896: case MAT_UNUSED_NONZERO_LOCATION_ERR:
897: a->nounused = (flg ? -1 : 0);
898: break;
899: case MAT_NEW_DIAGONALS:
900: case MAT_IGNORE_OFF_PROC_ENTRIES:
901: case MAT_USE_HASH_TABLE:
902: PetscInfo1(A,"Option %s ignored\n",MatOptions[op]);
903: break;
904: case MAT_SPD:
905: case MAT_SYMMETRIC:
906: case MAT_STRUCTURALLY_SYMMETRIC:
907: case MAT_HERMITIAN:
908: case MAT_SYMMETRY_ETERNAL:
909: /* These options are handled directly by MatSetOption() */
910: break;
911: default:
912: SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SUP,"unknown option %d",op);
913: }
914: return(0);
915: }
917: PetscErrorCode MatGetDiagonal_SeqSELL(Mat A,Vec v)
918: {
919: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
920: PetscInt i,j,n,shift;
921: PetscScalar *x,zero=0.0;
925: VecGetLocalSize(v,&n);
926: if (n != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Nonconforming matrix and vector");
928: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
929: PetscInt *diag=a->diag;
930: VecGetArray(v,&x);
931: for (i=0; i<n; i++) x[i] = 1.0/a->val[diag[i]];
932: VecRestoreArray(v,&x);
933: return(0);
934: }
936: VecSet(v,zero);
937: VecGetArray(v,&x);
938: for (i=0; i<n; i++) { /* loop over rows */
939: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
940: x[i] = 0;
941: for (j=0; j<a->rlen[i]; j++) {
942: if (a->colidx[shift+j*8] == i) {
943: x[i] = a->val[shift+j*8];
944: break;
945: }
946: }
947: }
948: VecRestoreArray(v,&x);
949: return(0);
950: }
952: PetscErrorCode MatDiagonalScale_SeqSELL(Mat A,Vec ll,Vec rr)
953: {
954: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
955: const PetscScalar *l,*r;
956: PetscInt i,j,m,n,row;
957: PetscErrorCode ierr;
960: if (ll) {
961: /* The local size is used so that VecMPI can be passed to this routine
962: by MatDiagonalScale_MPISELL */
963: VecGetLocalSize(ll,&m);
964: if (m != A->rmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Left scaling vector wrong length");
965: VecGetArrayRead(ll,&l);
966: for (i=0; i<a->totalslices; i++) { /* loop over slices */
967: for (j=a->sliidx[i],row=0; j<a->sliidx[i+1]; j++,row=((row+1)&0x07)) {
968: a->val[j] *= l[8*i+row];
969: }
970: }
971: VecRestoreArrayRead(ll,&l);
972: PetscLogFlops(a->nz);
973: }
974: if (rr) {
975: VecGetLocalSize(rr,&n);
976: if (n != A->cmap->n) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Right scaling vector wrong length");
977: VecGetArrayRead(rr,&r);
978: for (i=0; i<a->totalslices; i++) { /* loop over slices */
979: for (j=a->sliidx[i]; j<a->sliidx[i+1]; j++) {
980: a->val[j] *= r[a->colidx[j]];
981: }
982: }
983: VecRestoreArrayRead(rr,&r);
984: PetscLogFlops(a->nz);
985: }
986: MatSeqSELLInvalidateDiagonal(A);
987: return(0);
988: }
990: extern PetscErrorCode MatSetValues_SeqSELL(Mat,PetscInt,const PetscInt[],PetscInt,const PetscInt[],const PetscScalar[],InsertMode);
992: PetscErrorCode MatGetValues_SeqSELL(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],PetscScalar v[])
993: {
994: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
995: PetscInt *cp,i,k,low,high,t,row,col,l;
996: PetscInt shift;
997: MatScalar *vp;
1000: for (k=0; k<m; k++) { /* loop over requested rows */
1001: row = im[k];
1002: if (row<0) continue;
1003: #if defined(PETSC_USE_DEBUG)
1004: 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);
1005: #endif
1006: shift = a->sliidx[row>>3]+(row&0x07); /* starting index of the row */
1007: cp = a->colidx+shift; /* pointer to the row */
1008: vp = a->val+shift; /* pointer to the row */
1009: for (l=0; l<n; l++) { /* loop over requested columns */
1010: col = in[l];
1011: if (col<0) continue;
1012: #if defined(PETSC_USE_DEBUG)
1013: 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);
1014: #endif
1015: high = a->rlen[row]; low = 0; /* assume unsorted */
1016: while (high-low > 5) {
1017: t = (low+high)/2;
1018: if (*(cp+t*8) > col) high = t;
1019: else low = t;
1020: }
1021: for (i=low; i<high; i++) {
1022: if (*(cp+8*i) > col) break;
1023: if (*(cp+8*i) == col) {
1024: *v++ = *(vp+8*i);
1025: goto finished;
1026: }
1027: }
1028: *v++ = 0.0;
1029: finished:;
1030: }
1031: }
1032: return(0);
1033: }
1035: PetscErrorCode MatView_SeqSELL_ASCII(Mat A,PetscViewer viewer)
1036: {
1037: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1038: PetscInt i,j,m=A->rmap->n,shift;
1039: const char *name;
1040: PetscViewerFormat format;
1041: PetscErrorCode ierr;
1044: PetscViewerGetFormat(viewer,&format);
1045: if (format == PETSC_VIEWER_ASCII_MATLAB) {
1046: PetscInt nofinalvalue = 0;
1047: /*
1048: if (m && ((a->i[m] == a->i[m-1]) || (a->j[a->nz-1] != A->cmap->n-1))) {
1049: nofinalvalue = 1;
1050: }
1051: */
1052: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1053: PetscViewerASCIIPrintf(viewer,"%% Size = %D %D \n",m,A->cmap->n);
1054: PetscViewerASCIIPrintf(viewer,"%% Nonzeros = %D \n",a->nz);
1055: #if defined(PETSC_USE_COMPLEX)
1056: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,4);\n",a->nz+nofinalvalue);
1057: #else
1058: PetscViewerASCIIPrintf(viewer,"zzz = zeros(%D,3);\n",a->nz+nofinalvalue);
1059: #endif
1060: PetscViewerASCIIPrintf(viewer,"zzz = [\n");
1062: for (i=0; i<m; i++) {
1063: shift = a->sliidx[i>>3]+(i&0x07);
1064: for (j=0; j<a->rlen[i]; j++) {
1065: #if defined(PETSC_USE_COMPLEX)
1066: 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]));
1067: #else
1068: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",i+1,a->colidx[shift+8*j]+1,(double)a->val[shift+8*j]);
1069: #endif
1070: }
1071: }
1072: /*
1073: if (nofinalvalue) {
1074: #if defined(PETSC_USE_COMPLEX)
1075: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e %18.16e\n",m,A->cmap->n,0.,0.);
1076: #else
1077: PetscViewerASCIIPrintf(viewer,"%D %D %18.16e\n",m,A->cmap->n,0.0);
1078: #endif
1079: }
1080: */
1081: PetscObjectGetName((PetscObject)A,&name);
1082: PetscViewerASCIIPrintf(viewer,"];\n %s = spconvert(zzz);\n",name);
1083: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1084: } else if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO) {
1085: return(0);
1086: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
1087: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1088: for (i=0; i<m; i++) {
1089: PetscViewerASCIIPrintf(viewer,"row %D:",i);
1090: shift = a->sliidx[i>>3]+(i&0x07);
1091: for (j=0; j<a->rlen[i]; j++) {
1092: #if defined(PETSC_USE_COMPLEX)
1093: if (PetscImaginaryPart(a->val[shift+8*j]) > 0.0 && PetscRealPart(a->val[shift+8*j]) != 0.0) {
1094: 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]));
1095: } else if (PetscImaginaryPart(a->val[shift+8*j]) < 0.0 && PetscRealPart(a->val[shift+8*j]) != 0.0) {
1096: 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]));
1097: } else if (PetscRealPart(a->val[shift+8*j]) != 0.0) {
1098: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[shift+8*j],(double)PetscRealPart(a->val[shift+8*j]));
1099: }
1100: #else
1101: if (a->val[shift+8*j] != 0.0) {PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[shift+8*j],(double)a->val[shift+8*j]);}
1102: #endif
1103: }
1104: PetscViewerASCIIPrintf(viewer,"\n");
1105: }
1106: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1107: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
1108: PetscInt cnt=0,jcnt;
1109: PetscScalar value;
1110: #if defined(PETSC_USE_COMPLEX)
1111: PetscBool realonly=PETSC_TRUE;
1112: for (i=0; i<a->sliidx[a->totalslices]; i++) {
1113: if (PetscImaginaryPart(a->val[i]) != 0.0) {
1114: realonly = PETSC_FALSE;
1115: break;
1116: }
1117: }
1118: #endif
1120: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1121: for (i=0; i<m; i++) {
1122: jcnt = 0;
1123: shift = a->sliidx[i>>3]+(i&0x07);
1124: for (j=0; j<A->cmap->n; j++) {
1125: if (jcnt < a->rlen[i] && j == a->colidx[shift+8*j]) {
1126: value = a->val[cnt++];
1127: jcnt++;
1128: } else {
1129: value = 0.0;
1130: }
1131: #if defined(PETSC_USE_COMPLEX)
1132: if (realonly) {
1133: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)PetscRealPart(value));
1134: } else {
1135: PetscViewerASCIIPrintf(viewer," %7.5e+%7.5e i ",(double)PetscRealPart(value),(double)PetscImaginaryPart(value));
1136: }
1137: #else
1138: PetscViewerASCIIPrintf(viewer," %7.5e ",(double)value);
1139: #endif
1140: }
1141: PetscViewerASCIIPrintf(viewer,"\n");
1142: }
1143: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1144: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
1145: PetscInt fshift=1;
1146: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1147: #if defined(PETSC_USE_COMPLEX)
1148: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate complex general\n");
1149: #else
1150: PetscViewerASCIIPrintf(viewer,"%%%%MatrixMarket matrix coordinate real general\n");
1151: #endif
1152: PetscViewerASCIIPrintf(viewer,"%D %D %D\n", m, A->cmap->n, a->nz);
1153: for (i=0; i<m; i++) {
1154: shift = a->sliidx[i>>3]+(i&0x07);
1155: for (j=0; j<a->rlen[i]; j++) {
1156: #if defined(PETSC_USE_COMPLEX)
1157: 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]));
1158: #else
1159: PetscViewerASCIIPrintf(viewer,"%D %D %g\n",i+fshift,a->colidx[shift+8*j]+fshift,(double)a->val[shift+8*j]);
1160: #endif
1161: }
1162: }
1163: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1164: } else if (format == PETSC_VIEWER_NATIVE) {
1165: for (i=0; i<a->totalslices; i++) { /* loop over slices */
1166: PetscInt row;
1167: PetscViewerASCIIPrintf(viewer,"slice %D: %D %D\n",i,a->sliidx[i],a->sliidx[i+1]);
1168: for (j=a->sliidx[i],row=0; j<a->sliidx[i+1]; j++,row=((row+1)&0x07)) {
1169: #if defined(PETSC_USE_COMPLEX)
1170: if (PetscImaginaryPart(a->val[j]) > 0.0) {
1171: PetscViewerASCIIPrintf(viewer," %D %D %g + %g i\n",8*i+row,a->colidx[j],(double)PetscRealPart(a->val[j]),(double)PetscImaginaryPart(a->val[j]));
1172: } else if (PetscImaginaryPart(a->val[j]) < 0.0) {
1173: PetscViewerASCIIPrintf(viewer," %D %D %g - %g i\n",8*i+row,a->colidx[j],(double)PetscRealPart(a->val[j]),-(double)PetscImaginaryPart(a->val[j]));
1174: } else {
1175: PetscViewerASCIIPrintf(viewer," %D %D %g\n",8*i+row,a->colidx[j],(double)PetscRealPart(a->val[j]));
1176: }
1177: #else
1178: PetscViewerASCIIPrintf(viewer," %D %D %g\n",8*i+row,a->colidx[j],(double)a->val[j]);
1179: #endif
1180: }
1181: }
1182: } else {
1183: PetscViewerASCIIUseTabs(viewer,PETSC_FALSE);
1184: if (A->factortype) {
1185: for (i=0; i<m; i++) {
1186: shift = a->sliidx[i>>3]+(i&0x07);
1187: PetscViewerASCIIPrintf(viewer,"row %D:",i);
1188: /* L part */
1189: for (j=shift; j<a->diag[i]; j+=8) {
1190: #if defined(PETSC_USE_COMPLEX)
1191: if (PetscImaginaryPart(a->val[shift+8*j]) > 0.0) {
1192: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->colidx[j],(double)PetscRealPart(a->val[j]),(double)PetscImaginaryPart(a->val[j]));
1193: } else if (PetscImaginaryPart(a->val[shift+8*j]) < 0.0) {
1194: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->colidx[j],(double)PetscRealPart(a->val[j]),(double)(-PetscImaginaryPart(a->val[j])));
1195: } else {
1196: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)PetscRealPart(a->val[j]));
1197: }
1198: #else
1199: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)a->val[j]);
1200: #endif
1201: }
1202: /* diagonal */
1203: j = a->diag[i];
1204: #if defined(PETSC_USE_COMPLEX)
1205: if (PetscImaginaryPart(a->val[j]) > 0.0) {
1206: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->colidx[j],(double)PetscRealPart(1.0/a->val[j]),(double)PetscImaginaryPart(1.0/a->val[j]));
1207: } else if (PetscImaginaryPart(a->val[j]) < 0.0) {
1208: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->colidx[j],(double)PetscRealPart(1.0/a->val[j]),(double)(-PetscImaginaryPart(1.0/a->val[j])));
1209: } else {
1210: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)PetscRealPart(1.0/a->val[j]));
1211: }
1212: #else
1213: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)(1.0/a->val[j]));
1214: #endif
1216: /* U part */
1217: for (j=a->diag[i]+1; j<shift+8*a->rlen[i]; j+=8) {
1218: #if defined(PETSC_USE_COMPLEX)
1219: if (PetscImaginaryPart(a->val[j]) > 0.0) {
1220: PetscViewerASCIIPrintf(viewer," (%D, %g + %g i)",a->colidx[j],(double)PetscRealPart(a->val[j]),(double)PetscImaginaryPart(a->val[j]));
1221: } else if (PetscImaginaryPart(a->val[j]) < 0.0) {
1222: PetscViewerASCIIPrintf(viewer," (%D, %g - %g i)",a->colidx[j],(double)PetscRealPart(a->val[j]),(double)(-PetscImaginaryPart(a->val[j])));
1223: } else {
1224: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)PetscRealPart(a->val[j]));
1225: }
1226: #else
1227: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[j],(double)a->val[j]);
1228: #endif
1229: }
1230: PetscViewerASCIIPrintf(viewer,"\n");
1231: }
1232: } else {
1233: for (i=0; i<m; i++) {
1234: shift = a->sliidx[i>>3]+(i&0x07);
1235: PetscViewerASCIIPrintf(viewer,"row %D:",i);
1236: for (j=0; j<a->rlen[i]; j++) {
1237: #if defined(PETSC_USE_COMPLEX)
1238: if (PetscImaginaryPart(a->val[j]) > 0.0) {
1239: 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]));
1240: } else if (PetscImaginaryPart(a->val[j]) < 0.0) {
1241: 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]));
1242: } else {
1243: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[shift+8*j],(double)PetscRealPart(a->val[shift+8*j]));
1244: }
1245: #else
1246: PetscViewerASCIIPrintf(viewer," (%D, %g) ",a->colidx[shift+8*j],(double)a->val[shift+8*j]);
1247: #endif
1248: }
1249: PetscViewerASCIIPrintf(viewer,"\n");
1250: }
1251: }
1252: PetscViewerASCIIUseTabs(viewer,PETSC_TRUE);
1253: }
1254: PetscViewerFlush(viewer);
1255: return(0);
1256: }
1258: #include <petscdraw.h>
1259: PetscErrorCode MatView_SeqSELL_Draw_Zoom(PetscDraw draw,void *Aa)
1260: {
1261: Mat A=(Mat)Aa;
1262: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1263: PetscInt i,j,m=A->rmap->n,shift;
1264: int color;
1265: PetscReal xl,yl,xr,yr,x_l,x_r,y_l,y_r;
1266: PetscViewer viewer;
1267: PetscViewerFormat format;
1268: PetscErrorCode ierr;
1271: PetscObjectQuery((PetscObject)A,"Zoomviewer",(PetscObject*)&viewer);
1272: PetscViewerGetFormat(viewer,&format);
1273: PetscDrawGetCoordinates(draw,&xl,&yl,&xr,&yr);
1275: /* loop over matrix elements drawing boxes */
1277: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
1278: PetscDrawCollectiveBegin(draw);
1279: /* Blue for negative, Cyan for zero and Red for positive */
1280: color = PETSC_DRAW_BLUE;
1281: for (i=0; i<m; i++) {
1282: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1283: y_l = m - i - 1.0; y_r = y_l + 1.0;
1284: for (j=0; j<a->rlen[i]; j++) {
1285: x_l = a->colidx[shift+j*8]; x_r = x_l + 1.0;
1286: if (PetscRealPart(a->val[shift+8*j]) >= 0.) continue;
1287: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1288: }
1289: }
1290: color = PETSC_DRAW_CYAN;
1291: for (i=0; i<m; i++) {
1292: shift = a->sliidx[i>>3]+(i&0x07);
1293: y_l = m - i - 1.0; y_r = y_l + 1.0;
1294: for (j=0; j<a->rlen[i]; j++) {
1295: x_l = a->colidx[shift+j*8]; x_r = x_l + 1.0;
1296: if (a->val[shift+8*j] != 0.) continue;
1297: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1298: }
1299: }
1300: color = PETSC_DRAW_RED;
1301: for (i=0; i<m; i++) {
1302: shift = a->sliidx[i>>3]+(i&0x07);
1303: y_l = m - i - 1.0; y_r = y_l + 1.0;
1304: for (j=0; j<a->rlen[i]; j++) {
1305: x_l = a->colidx[shift+j*8]; x_r = x_l + 1.0;
1306: if (PetscRealPart(a->val[shift+8*j]) <= 0.) continue;
1307: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1308: }
1309: }
1310: PetscDrawCollectiveEnd(draw);
1311: } else {
1312: /* use contour shading to indicate magnitude of values */
1313: /* first determine max of all nonzero values */
1314: PetscReal minv=0.0,maxv=0.0;
1315: PetscInt count=0;
1316: PetscDraw popup;
1317: for (i=0; i<a->sliidx[a->totalslices]; i++) {
1318: if (PetscAbsScalar(a->val[i]) > maxv) maxv = PetscAbsScalar(a->val[i]);
1319: }
1320: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1321: PetscDrawGetPopup(draw,&popup);
1322: PetscDrawScalePopup(popup,minv,maxv);
1324: PetscDrawCollectiveBegin(draw);
1325: for (i=0; i<m; i++) {
1326: shift = a->sliidx[i>>3]+(i&0x07);
1327: y_l = m - i - 1.0;
1328: y_r = y_l + 1.0;
1329: for (j=0; j<a->rlen[i]; j++) {
1330: x_l = a->colidx[shift+j*8];
1331: x_r = x_l + 1.0;
1332: color = PetscDrawRealToColor(PetscAbsScalar(a->val[count]),minv,maxv);
1333: PetscDrawRectangle(draw,x_l,y_l,x_r,y_r,color,color,color,color);
1334: count++;
1335: }
1336: }
1337: PetscDrawCollectiveEnd(draw);
1338: }
1339: return(0);
1340: }
1342: #include <petscdraw.h>
1343: PetscErrorCode MatView_SeqSELL_Draw(Mat A,PetscViewer viewer)
1344: {
1345: PetscDraw draw;
1346: PetscReal xr,yr,xl,yl,h,w;
1347: PetscBool isnull;
1351: PetscViewerDrawGetDraw(viewer,0,&draw);
1352: PetscDrawIsNull(draw,&isnull);
1353: if (isnull) return(0);
1355: xr = A->cmap->n; yr = A->rmap->n; h = yr/10.0; w = xr/10.0;
1356: xr += w; yr += h; xl = -w; yl = -h;
1357: PetscDrawSetCoordinates(draw,xl,yl,xr,yr);
1358: PetscObjectCompose((PetscObject)A,"Zoomviewer",(PetscObject)viewer);
1359: PetscDrawZoom(draw,MatView_SeqSELL_Draw_Zoom,A);
1360: PetscObjectCompose((PetscObject)A,"Zoomviewer",NULL);
1361: PetscDrawSave(draw);
1362: return(0);
1363: }
1365: PetscErrorCode MatView_SeqSELL(Mat A,PetscViewer viewer)
1366: {
1367: PetscBool iascii,isbinary,isdraw;
1371: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERASCII,&iascii);
1372: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERBINARY,&isbinary);
1373: PetscObjectTypeCompare((PetscObject)viewer,PETSCVIEWERDRAW,&isdraw);
1374: if (iascii) {
1375: MatView_SeqSELL_ASCII(A,viewer);
1376: } else if (isbinary) {
1377: /* MatView_SeqSELL_Binary(A,viewer); */
1378: } else if (isdraw) {
1379: MatView_SeqSELL_Draw(A,viewer);
1380: }
1381: return(0);
1382: }
1384: PetscErrorCode MatAssemblyEnd_SeqSELL(Mat A,MatAssemblyType mode)
1385: {
1386: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1387: PetscInt i,shift,row_in_slice,row,nrow,*cp,lastcol,j,k;
1388: MatScalar *vp;
1392: if (mode == MAT_FLUSH_ASSEMBLY) return(0);
1393: /* To do: compress out the unused elements */
1394: MatMarkDiagonal_SeqSELL(A);
1395: 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);
1396: PetscInfo1(A,"Number of mallocs during MatSetValues() is %D\n",a->reallocs);
1397: PetscInfo1(A,"Maximum nonzeros in any row is %D\n",a->rlenmax);
1398: /* 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 */
1399: for (i=0; i<a->totalslices; ++i) {
1400: shift = a->sliidx[i]; /* starting index of the slice */
1401: cp = a->colidx+shift; /* pointer to the column indices of the slice */
1402: vp = a->val+shift; /* pointer to the nonzero values of the slice */
1403: for (row_in_slice=0; row_in_slice<8; ++row_in_slice) { /* loop over rows in the slice */
1404: row = 8*i + row_in_slice;
1405: nrow = a->rlen[row]; /* number of nonzeros in row */
1406: /*
1407: Search for the nearest nonzero. Normally setting the index to zero may cause extra communication.
1408: But if the entire slice are empty, it is fine to use 0 since the index will not be loaded.
1409: */
1410: lastcol = 0;
1411: if (nrow>0) { /* nonempty row */
1412: lastcol = cp[8*(nrow-1)+row_in_slice]; /* use the index from the last nonzero at current row */
1413: } else if (!row_in_slice) { /* first row of the currect slice is empty */
1414: for (j=1;j<8;j++) {
1415: if (a->rlen[8*i+j]) {
1416: lastcol = cp[j];
1417: break;
1418: }
1419: }
1420: } else {
1421: if (a->sliidx[i+1] != shift) lastcol = cp[row_in_slice-1]; /* use the index from the previous row */
1422: }
1424: for (k=nrow; k<(a->sliidx[i+1]-shift)/8; ++k) {
1425: cp[8*k+row_in_slice] = lastcol;
1426: vp[8*k+row_in_slice] = (MatScalar)0;
1427: }
1428: }
1429: }
1431: A->info.mallocs += a->reallocs;
1432: a->reallocs = 0;
1434: MatSeqSELLInvalidateDiagonal(A);
1435: return(0);
1436: }
1438: PetscErrorCode MatGetInfo_SeqSELL(Mat A,MatInfoType flag,MatInfo *info)
1439: {
1440: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1443: info->block_size = 1.0;
1444: info->nz_allocated = (double)a->maxallocmat;
1445: info->nz_used = (double)a->sliidx[a->totalslices]; /* include padding zeros */
1446: info->nz_unneeded = (double)(a->maxallocmat-a->sliidx[a->totalslices]);
1447: info->assemblies = (double)A->num_ass;
1448: info->mallocs = (double)A->info.mallocs;
1449: info->memory = ((PetscObject)A)->mem;
1450: if (A->factortype) {
1451: info->fill_ratio_given = A->info.fill_ratio_given;
1452: info->fill_ratio_needed = A->info.fill_ratio_needed;
1453: info->factor_mallocs = A->info.factor_mallocs;
1454: } else {
1455: info->fill_ratio_given = 0;
1456: info->fill_ratio_needed = 0;
1457: info->factor_mallocs = 0;
1458: }
1459: return(0);
1460: }
1462: PetscErrorCode MatSetValues_SeqSELL(Mat A,PetscInt m,const PetscInt im[],PetscInt n,const PetscInt in[],const PetscScalar v[],InsertMode is)
1463: {
1464: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1465: PetscInt shift,i,k,l,low,high,t,ii,row,col,nrow;
1466: PetscInt *cp,nonew=a->nonew,lastcol=-1;
1467: MatScalar *vp,value;
1471: for (k=0; k<m; k++) { /* loop over added rows */
1472: row = im[k];
1473: if (row < 0) continue;
1474: #if defined(PETSC_USE_DEBUG)
1475: 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);
1476: #endif
1477: shift = a->sliidx[row>>3]+(row&0x07); /* starting index of the row */
1478: cp = a->colidx+shift; /* pointer to the row */
1479: vp = a->val+shift; /* pointer to the row */
1480: nrow = a->rlen[row];
1481: low = 0;
1482: high = nrow;
1484: for (l=0; l<n; l++) { /* loop over added columns */
1485: col = in[l];
1486: if (col<0) continue;
1487: #if defined(PETSC_USE_DEBUG)
1488: 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);
1489: #endif
1490: if (a->roworiented) {
1491: value = v[l+k*n];
1492: } else {
1493: value = v[k+l*m];
1494: }
1495: if ((value == 0.0 && a->ignorezeroentries) && (is == ADD_VALUES)) continue;
1497: /* search in this row for the specified colmun, i indicates the column to be set */
1498: if (col <= lastcol) low = 0;
1499: else high = nrow;
1500: lastcol = col;
1501: while (high-low > 5) {
1502: t = (low+high)/2;
1503: if (*(cp+t*8) > col) high = t;
1504: else low = t;
1505: }
1506: for (i=low; i<high; i++) {
1507: if (*(cp+i*8) > col) break;
1508: if (*(cp+i*8) == col) {
1509: if (is == ADD_VALUES) *(vp+i*8) += value;
1510: else *(vp+i*8) = value;
1511: low = i + 1;
1512: goto noinsert;
1513: }
1514: }
1515: if (value == 0.0 && a->ignorezeroentries) goto noinsert;
1516: if (nonew == 1) goto noinsert;
1517: if (nonew == -1) SETERRQ2(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Inserting a new nonzero (%D, %D) in the matrix", row, col);
1518: /* If the current row length exceeds the slice width (e.g. nrow==slice_width), allocate a new space, otherwise do nothing */
1519: MatSeqXSELLReallocateSELL(A,A->rmap->n,1,nrow,a->sliidx,row/8,row,col,a->colidx,a->val,cp,vp,nonew,MatScalar);
1520: /* add the new nonzero to the high position, shift the remaining elements in current row to the right by one slot */
1521: for (ii=nrow-1; ii>=i; ii--) {
1522: *(cp+(ii+1)*8) = *(cp+ii*8);
1523: *(vp+(ii+1)*8) = *(vp+ii*8);
1524: }
1525: a->rlen[row]++;
1526: *(cp+i*8) = col;
1527: *(vp+i*8) = value;
1528: a->nz++;
1529: A->nonzerostate++;
1530: low = i+1; high++; nrow++;
1531: noinsert:;
1532: }
1533: a->rlen[row] = nrow;
1534: }
1535: return(0);
1536: }
1538: PetscErrorCode MatCopy_SeqSELL(Mat A,Mat B,MatStructure str)
1539: {
1543: /* If the two matrices have the same copy implementation, use fast copy. */
1544: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
1545: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1546: Mat_SeqSELL *b=(Mat_SeqSELL*)B->data;
1548: 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");
1549: PetscMemcpy(b->val,a->val,a->sliidx[a->totalslices]*sizeof(PetscScalar));
1550: } else {
1551: MatCopy_Basic(A,B,str);
1552: }
1553: return(0);
1554: }
1556: PetscErrorCode MatSetUp_SeqSELL(Mat A)
1557: {
1561: MatSeqSELLSetPreallocation(A,PETSC_DEFAULT,0);
1562: return(0);
1563: }
1565: PetscErrorCode MatSeqSELLGetArray_SeqSELL(Mat A,PetscScalar *array[])
1566: {
1567: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1570: *array = a->val;
1571: return(0);
1572: }
1574: PetscErrorCode MatSeqSELLRestoreArray_SeqSELL(Mat A,PetscScalar *array[])
1575: {
1577: return(0);
1578: }
1580: PetscErrorCode MatRealPart_SeqSELL(Mat A)
1581: {
1582: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1583: PetscInt i;
1584: MatScalar *aval=a->val;
1587: for (i=0; i<a->sliidx[a->totalslices]; i++) aval[i]=PetscRealPart(aval[i]);
1588: return(0);
1589: }
1591: PetscErrorCode MatImaginaryPart_SeqSELL(Mat A)
1592: {
1593: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1594: PetscInt i;
1595: MatScalar *aval=a->val;
1599: for (i=0; i<a->sliidx[a->totalslices]; i++) aval[i] = PetscImaginaryPart(aval[i]);
1600: MatSeqSELLInvalidateDiagonal(A);
1601: return(0);
1602: }
1604: PetscErrorCode MatScale_SeqSELL(Mat inA,PetscScalar alpha)
1605: {
1606: Mat_SeqSELL *a=(Mat_SeqSELL*)inA->data;
1607: MatScalar *aval=a->val;
1608: PetscScalar oalpha=alpha;
1609: PetscBLASInt one=1,size;
1613: PetscBLASIntCast(a->sliidx[a->totalslices],&size);
1614: PetscStackCallBLAS("BLASscal",BLASscal_(&size,&oalpha,aval,&one));
1615: PetscLogFlops(a->nz);
1616: MatSeqSELLInvalidateDiagonal(inA);
1617: return(0);
1618: }
1620: PetscErrorCode MatShift_SeqSELL(Mat Y,PetscScalar a)
1621: {
1622: Mat_SeqSELL *y=(Mat_SeqSELL*)Y->data;
1626: if (!Y->preallocated || !y->nz) {
1627: MatSeqSELLSetPreallocation(Y,1,NULL);
1628: }
1629: MatShift_Basic(Y,a);
1630: return(0);
1631: }
1633: PetscErrorCode MatSOR_SeqSELL(Mat A,Vec bb,PetscReal omega,MatSORType flag,PetscReal fshift,PetscInt its,PetscInt lits,Vec xx)
1634: {
1635: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
1636: PetscScalar *x,sum,*t;
1637: const MatScalar *idiag=0,*mdiag;
1638: const PetscScalar *b,*xb;
1639: PetscInt n,m=A->rmap->n,i,j,shift;
1640: const PetscInt *diag;
1641: PetscErrorCode ierr;
1644: its = its*lits;
1646: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1647: if (!a->idiagvalid) {MatInvertDiagonal_SeqSELL(A,omega,fshift);}
1648: a->fshift = fshift;
1649: a->omega = omega;
1651: diag = a->diag;
1652: t = a->ssor_work;
1653: idiag = a->idiag;
1654: mdiag = a->mdiag;
1656: VecGetArray(xx,&x);
1657: VecGetArrayRead(bb,&b);
1658: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1659: if (flag == SOR_APPLY_UPPER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_UPPER is not implemented");
1660: if (flag == SOR_APPLY_LOWER) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"SOR_APPLY_LOWER is not implemented");
1661: if (flag & SOR_EISENSTAT) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_SUP,"No support yet for Eisenstat");
1663: if (flag & SOR_ZERO_INITIAL_GUESS) {
1664: if ((flag & SOR_FORWARD_SWEEP) || (flag & SOR_LOCAL_FORWARD_SWEEP)) {
1665: for (i=0; i<m; i++) {
1666: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1667: sum = b[i];
1668: n = (diag[i]-shift)/8;
1669: for (j=0; j<n; j++) sum -= a->val[shift+j*8]*x[a->colidx[shift+j*8]];
1670: t[i] = sum;
1671: x[i] = sum*idiag[i];
1672: }
1673: xb = t;
1674: PetscLogFlops(a->nz);
1675: } else xb = b;
1676: if ((flag & SOR_BACKWARD_SWEEP) || (flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1677: for (i=m-1; i>=0; i--) {
1678: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1679: sum = xb[i];
1680: n = a->rlen[i]-(diag[i]-shift)/8-1;
1681: for (j=1; j<=n; j++) sum -= a->val[diag[i]+j*8]*x[a->colidx[diag[i]+j*8]];
1682: if (xb == b) {
1683: x[i] = sum*idiag[i];
1684: } else {
1685: x[i] = (1.-omega)*x[i]+sum*idiag[i]; /* omega in idiag */
1686: }
1687: }
1688: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1689: }
1690: its--;
1691: }
1692: while (its--) {
1693: if ((flag & SOR_FORWARD_SWEEP) || (flag & SOR_LOCAL_FORWARD_SWEEP)) {
1694: for (i=0; i<m; i++) {
1695: /* lower */
1696: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1697: sum = b[i];
1698: n = (diag[i]-shift)/8;
1699: for (j=0; j<n; j++) sum -= a->val[shift+j*8]*x[a->colidx[shift+j*8]];
1700: t[i] = sum; /* save application of the lower-triangular part */
1701: /* upper */
1702: n = a->rlen[i]-(diag[i]-shift)/8-1;
1703: for (j=1; j<=n; j++) sum -= a->val[diag[i]+j*8]*x[a->colidx[diag[i]+j*8]];
1704: x[i] = (1.-omega)*x[i]+sum*idiag[i]; /* omega in idiag */
1705: }
1706: xb = t;
1707: PetscLogFlops(2.0*a->nz);
1708: } else xb = b;
1709: if ((flag & SOR_BACKWARD_SWEEP) || (flag & SOR_LOCAL_BACKWARD_SWEEP)) {
1710: for (i=m-1; i>=0; i--) {
1711: shift = a->sliidx[i>>3]+(i&0x07); /* starting index of the row i */
1712: sum = xb[i];
1713: if (xb == b) {
1714: /* whole matrix (no checkpointing available) */
1715: n = a->rlen[i];
1716: for (j=0; j<n; j++) sum -= a->val[shift+j*8]*x[a->colidx[shift+j*8]];
1717: x[i] = (1.-omega)*x[i]+(sum+mdiag[i]*x[i])*idiag[i];
1718: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
1719: n = a->rlen[i]-(diag[i]-shift)/8-1;
1720: for (j=1; j<=n; j++) sum -= a->val[diag[i]+j*8]*x[a->colidx[diag[i]+j*8]];
1721: x[i] = (1.-omega)*x[i]+sum*idiag[i]; /* omega in idiag */
1722: }
1723: }
1724: if (xb == b) {
1725: PetscLogFlops(2.0*a->nz);
1726: } else {
1727: PetscLogFlops(a->nz); /* assumes 1/2 in upper */
1728: }
1729: }
1730: }
1731: VecRestoreArray(xx,&x);
1732: VecRestoreArrayRead(bb,&b);
1733: return(0);
1734: }
1736: /* -------------------------------------------------------------------*/
1737: static struct _MatOps MatOps_Values = {MatSetValues_SeqSELL,
1738: MatGetRow_SeqSELL,
1739: MatRestoreRow_SeqSELL,
1740: MatMult_SeqSELL,
1741: /* 4*/ MatMultAdd_SeqSELL,
1742: MatMultTranspose_SeqSELL,
1743: MatMultTransposeAdd_SeqSELL,
1744: 0,
1745: 0,
1746: 0,
1747: /* 10*/ 0,
1748: 0,
1749: 0,
1750: MatSOR_SeqSELL,
1751: 0,
1752: /* 15*/ MatGetInfo_SeqSELL,
1753: MatEqual_SeqSELL,
1754: MatGetDiagonal_SeqSELL,
1755: MatDiagonalScale_SeqSELL,
1756: 0,
1757: /* 20*/ 0,
1758: MatAssemblyEnd_SeqSELL,
1759: MatSetOption_SeqSELL,
1760: MatZeroEntries_SeqSELL,
1761: /* 24*/ 0,
1762: 0,
1763: 0,
1764: 0,
1765: 0,
1766: /* 29*/ MatSetUp_SeqSELL,
1767: 0,
1768: 0,
1769: 0,
1770: 0,
1771: /* 34*/ MatDuplicate_SeqSELL,
1772: 0,
1773: 0,
1774: 0,
1775: 0,
1776: /* 39*/ 0,
1777: 0,
1778: 0,
1779: MatGetValues_SeqSELL,
1780: MatCopy_SeqSELL,
1781: /* 44*/ 0,
1782: MatScale_SeqSELL,
1783: MatShift_SeqSELL,
1784: 0,
1785: 0,
1786: /* 49*/ 0,
1787: 0,
1788: 0,
1789: 0,
1790: 0,
1791: /* 54*/ MatFDColoringCreate_SeqXAIJ,
1792: 0,
1793: 0,
1794: 0,
1795: 0,
1796: /* 59*/ 0,
1797: MatDestroy_SeqSELL,
1798: MatView_SeqSELL,
1799: 0,
1800: 0,
1801: /* 64*/ 0,
1802: 0,
1803: 0,
1804: 0,
1805: 0,
1806: /* 69*/ 0,
1807: 0,
1808: 0,
1809: 0,
1810: 0,
1811: /* 74*/ 0,
1812: MatFDColoringApply_AIJ, /* reuse the FDColoring function for AIJ */
1813: 0,
1814: 0,
1815: 0,
1816: /* 79*/ 0,
1817: 0,
1818: 0,
1819: 0,
1820: 0,
1821: /* 84*/ 0,
1822: 0,
1823: 0,
1824: 0,
1825: 0,
1826: /* 89*/ 0,
1827: 0,
1828: 0,
1829: 0,
1830: 0,
1831: /* 94*/ 0,
1832: 0,
1833: 0,
1834: 0,
1835: 0,
1836: /* 99*/ 0,
1837: 0,
1838: 0,
1839: MatConjugate_SeqSELL,
1840: 0,
1841: /*104*/ 0,
1842: 0,
1843: 0,
1844: 0,
1845: 0,
1846: /*109*/ 0,
1847: 0,
1848: 0,
1849: 0,
1850: MatMissingDiagonal_SeqSELL,
1851: /*114*/ 0,
1852: 0,
1853: 0,
1854: 0,
1855: 0,
1856: /*119*/ 0,
1857: 0,
1858: 0,
1859: 0,
1860: 0,
1861: /*124*/ 0,
1862: 0,
1863: 0,
1864: 0,
1865: 0,
1866: /*129*/ 0,
1867: 0,
1868: 0,
1869: 0,
1870: 0,
1871: /*134*/ 0,
1872: 0,
1873: 0,
1874: 0,
1875: 0,
1876: /*139*/ 0,
1877: 0,
1878: 0,
1879: MatFDColoringSetUp_SeqXAIJ,
1880: 0,
1881: /*144*/0
1882: };
1884: PetscErrorCode MatStoreValues_SeqSELL(Mat mat)
1885: {
1886: Mat_SeqSELL *a=(Mat_SeqSELL*)mat->data;
1890: if (!a->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
1892: /* allocate space for values if not already there */
1893: if (!a->saved_values) {
1894: PetscMalloc1(a->sliidx[a->totalslices]+1,&a->saved_values);
1895: PetscLogObjectMemory((PetscObject)mat,(a->sliidx[a->totalslices]+1)*sizeof(PetscScalar));
1896: }
1898: /* copy values over */
1899: PetscMemcpy(a->saved_values,a->val,a->sliidx[a->totalslices]*sizeof(PetscScalar));
1900: return(0);
1901: }
1903: PetscErrorCode MatRetrieveValues_SeqSELL(Mat mat)
1904: {
1905: Mat_SeqSELL *a=(Mat_SeqSELL*)mat->data;
1909: if (!a->nonew) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
1910: if (!a->saved_values) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ORDER,"Must call MatStoreValues(A);first");
1911: /* copy values over */
1912: PetscMemcpy(a->val,a->saved_values,a->sliidx[a->totalslices]*sizeof(PetscScalar));
1913: return(0);
1914: }
1916: /*@C
1917: MatSeqSELLRestoreArray - returns access to the array where the data for a MATSEQSELL matrix is stored obtained by MatSeqSELLGetArray()
1919: Not Collective
1921: Input Parameters:
1922: . mat - a MATSEQSELL matrix
1923: . array - pointer to the data
1925: Level: intermediate
1927: .seealso: MatSeqSELLGetArray(), MatSeqSELLRestoreArrayF90()
1928: @*/
1929: PetscErrorCode MatSeqSELLRestoreArray(Mat A,PetscScalar **array)
1930: {
1934: PetscUseMethod(A,"MatSeqSELLRestoreArray_C",(Mat,PetscScalar**),(A,array));
1935: return(0);
1936: }
1938: PETSC_EXTERN PetscErrorCode MatCreate_SeqSELL(Mat B)
1939: {
1940: Mat_SeqSELL *b;
1941: PetscMPIInt size;
1945: MPI_Comm_size(PetscObjectComm((PetscObject)B),&size);
1946: if (size > 1) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_OUTOFRANGE,"Comm must be of size 1");
1948: PetscNewLog(B,&b);
1950: B->data = (void*)b;
1952: PetscMemcpy(B->ops,&MatOps_Values,sizeof(struct _MatOps));
1954: b->row = 0;
1955: b->col = 0;
1956: b->icol = 0;
1957: b->reallocs = 0;
1958: b->ignorezeroentries = PETSC_FALSE;
1959: b->roworiented = PETSC_TRUE;
1960: b->nonew = 0;
1961: b->diag = 0;
1962: b->solve_work = 0;
1963: B->spptr = 0;
1964: b->saved_values = 0;
1965: b->idiag = 0;
1966: b->mdiag = 0;
1967: b->ssor_work = 0;
1968: b->omega = 1.0;
1969: b->fshift = 0.0;
1970: b->idiagvalid = PETSC_FALSE;
1971: b->keepnonzeropattern = PETSC_FALSE;
1973: PetscObjectChangeTypeName((PetscObject)B,MATSEQSELL);
1974: PetscObjectComposeFunction((PetscObject)B,"MatSeqSELLGetArray_C",MatSeqSELLGetArray_SeqSELL);
1975: PetscObjectComposeFunction((PetscObject)B,"MatSeqSELLRestoreArray_C",MatSeqSELLRestoreArray_SeqSELL);
1976: PetscObjectComposeFunction((PetscObject)B,"MatStoreValues_C",MatStoreValues_SeqSELL);
1977: PetscObjectComposeFunction((PetscObject)B,"MatRetrieveValues_C",MatRetrieveValues_SeqSELL);
1978: PetscObjectComposeFunction((PetscObject)B,"MatSeqSELLSetPreallocation_C",MatSeqSELLSetPreallocation_SeqSELL);
1979: PetscObjectComposeFunction((PetscObject)B,"MatConvert_seqsell_seqaij_C",MatConvert_SeqSELL_SeqAIJ);
1980: return(0);
1981: }
1983: /*
1984: Given a matrix generated with MatGetFactor() duplicates all the information in A into B
1985: */
1986: PetscErrorCode MatDuplicateNoCreate_SeqSELL(Mat C,Mat A,MatDuplicateOption cpvalues,PetscBool mallocmatspace)
1987: {
1988: Mat_SeqSELL *c,*a=(Mat_SeqSELL*)A->data;
1989: PetscInt i,m=A->rmap->n;
1990: PetscInt totalslices=a->totalslices;
1994: c = (Mat_SeqSELL*)C->data;
1996: C->factortype = A->factortype;
1997: c->row = 0;
1998: c->col = 0;
1999: c->icol = 0;
2000: c->reallocs = 0;
2002: C->assembled = PETSC_TRUE;
2004: PetscLayoutReference(A->rmap,&C->rmap);
2005: PetscLayoutReference(A->cmap,&C->cmap);
2007: PetscMalloc1(8*totalslices,&c->rlen);
2008: PetscLogObjectMemory((PetscObject)C,m*sizeof(PetscInt));
2009: PetscMalloc1(totalslices+1,&c->sliidx);
2010: PetscLogObjectMemory((PetscObject)C, (totalslices+1)*sizeof(PetscInt));
2012: for (i=0; i<m; i++) c->rlen[i] = a->rlen[i];
2013: for (i=0; i<totalslices+1; i++) c->sliidx[i] = a->sliidx[i];
2015: /* allocate the matrix space */
2016: if (mallocmatspace) {
2017: PetscMalloc2(a->maxallocmat,&c->val,a->maxallocmat,&c->colidx);
2018: PetscLogObjectMemory((PetscObject)C,a->maxallocmat*(sizeof(PetscScalar)+sizeof(PetscInt)));
2020: c->singlemalloc = PETSC_TRUE;
2022: if (m > 0) {
2023: PetscMemcpy(c->colidx,a->colidx,(a->maxallocmat)*sizeof(PetscInt));
2024: if (cpvalues == MAT_COPY_VALUES) {
2025: PetscMemcpy(c->val,a->val,a->maxallocmat*sizeof(PetscScalar));
2026: } else {
2027: PetscMemzero(c->val,a->maxallocmat*sizeof(PetscScalar));
2028: }
2029: }
2030: }
2032: c->ignorezeroentries = a->ignorezeroentries;
2033: c->roworiented = a->roworiented;
2034: c->nonew = a->nonew;
2035: if (a->diag) {
2036: PetscMalloc1(m,&c->diag);
2037: PetscLogObjectMemory((PetscObject)C,m*sizeof(PetscInt));
2038: for (i=0; i<m; i++) {
2039: c->diag[i] = a->diag[i];
2040: }
2041: } else c->diag = 0;
2043: c->solve_work = 0;
2044: c->saved_values = 0;
2045: c->idiag = 0;
2046: c->ssor_work = 0;
2047: c->keepnonzeropattern = a->keepnonzeropattern;
2048: c->free_val = PETSC_TRUE;
2049: c->free_colidx = PETSC_TRUE;
2051: c->maxallocmat = a->maxallocmat;
2052: c->maxallocrow = a->maxallocrow;
2053: c->rlenmax = a->rlenmax;
2054: c->nz = a->nz;
2055: C->preallocated = PETSC_TRUE;
2057: c->nonzerorowcnt = a->nonzerorowcnt;
2058: C->nonzerostate = A->nonzerostate;
2060: PetscFunctionListDuplicate(((PetscObject)A)->qlist,&((PetscObject)C)->qlist);
2061: return(0);
2062: }
2064: PetscErrorCode MatDuplicate_SeqSELL(Mat A,MatDuplicateOption cpvalues,Mat *B)
2065: {
2069: MatCreate(PetscObjectComm((PetscObject)A),B);
2070: MatSetSizes(*B,A->rmap->n,A->cmap->n,A->rmap->n,A->cmap->n);
2071: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) {
2072: MatSetBlockSizesFromMats(*B,A,A);
2073: }
2074: MatSetType(*B,((PetscObject)A)->type_name);
2075: MatDuplicateNoCreate_SeqSELL(*B,A,cpvalues,PETSC_TRUE);
2076: return(0);
2077: }
2079: /*@C
2080: MatCreateSeqSELL - Creates a sparse matrix in SELL format.
2082: Collective on MPI_Comm
2084: Input Parameters:
2085: + comm - MPI communicator, set to PETSC_COMM_SELF
2086: . m - number of rows
2087: . n - number of columns
2088: . rlenmax - maximum number of nonzeros in a row
2089: - rlen - array containing the number of nonzeros in the various rows
2090: (possibly different for each row) or NULL
2092: Output Parameter:
2093: . A - the matrix
2095: It is recommended that one use the MatCreate(), MatSetType() and/or MatSetFromOptions(),
2096: MatXXXXSetPreallocation() paradgm instead of this routine directly.
2097: [MatXXXXSetPreallocation() is, for example, MatSeqSELLSetPreallocation]
2099: Notes:
2100: If nnz is given then nz is ignored
2102: Specify the preallocated storage with either rlenmax or rlen (not both).
2103: Set rlenmax=PETSC_DEFAULT and rlen=NULL for PETSc to control dynamic memory
2104: allocation. For large problems you MUST preallocate memory or you
2105: will get TERRIBLE performance, see the users' manual chapter on matrices.
2107: Level: intermediate
2109: .seealso: MatCreate(), MatCreateSELL(), MatSetValues(), MatCreateSeqSELLWithArrays()
2111: @*/
2112: PetscErrorCode MatCreateSeqSELL(MPI_Comm comm,PetscInt m,PetscInt n,PetscInt maxallocrow,const PetscInt rlen[],Mat *A)
2113: {
2117: MatCreate(comm,A);
2118: MatSetSizes(*A,m,n,m,n);
2119: MatSetType(*A,MATSEQSELL);
2120: MatSeqSELLSetPreallocation_SeqSELL(*A,maxallocrow,rlen);
2121: return(0);
2122: }
2124: PetscErrorCode MatEqual_SeqSELL(Mat A,Mat B,PetscBool * flg)
2125: {
2126: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data,*b=(Mat_SeqSELL*)B->data;
2127: PetscInt totalslices=a->totalslices;
2131: /* If the matrix dimensions are not equal,or no of nonzeros */
2132: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) ||(a->nz != b->nz) || (a->rlenmax != b->rlenmax)) {
2133: *flg = PETSC_FALSE;
2134: return(0);
2135: }
2136: /* if the a->colidx are the same */
2137: PetscMemcmp(a->colidx,b->colidx,a->sliidx[totalslices]*sizeof(PetscInt),flg);
2138: if (!*flg) return(0);
2139: /* if a->val are the same */
2140: PetscMemcmp(a->val,b->val,a->sliidx[totalslices]*sizeof(PetscScalar),flg);
2141: return(0);
2142: }
2144: PetscErrorCode MatSeqSELLInvalidateDiagonal(Mat A)
2145: {
2146: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
2149: a->idiagvalid = PETSC_FALSE;
2150: return(0);
2151: }
2153: PetscErrorCode MatConjugate_SeqSELL(Mat A)
2154: {
2155: #if defined(PETSC_USE_COMPLEX)
2156: Mat_SeqSELL *a=(Mat_SeqSELL*)A->data;
2157: PetscInt i;
2158: PetscScalar *val = a->val;
2161: for (i=0; i<a->sliidx[a->totalslices]; i++) {
2162: val[i] = PetscConj(val[i]);
2163: }
2164: #else
2166: #endif
2167: return(0);
2168: }