Actual source code: sell.c
petsc-3.10.5 2019-03-28
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      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() paradgm 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: }