Actual source code: aij.h
1: #pragma once
3: #include <petsc/private/matimpl.h>
4: #include <petsc/private/hashmapi.h>
5: #include <petsc/private/hashmapijv.h>
7: /*
8: Used by MatCreateSubMatrices_MPIXAIJ_Local()
9: */
10: typedef struct { /* used by MatCreateSubMatrices_MPIAIJ_SingleIS_Local() and MatCreateSubMatrices_MPIAIJ_Local */
11: PetscInt id; /* index of submats, only submats[0] is responsible for deleting some arrays below */
12: PetscMPIInt nrqs, nrqr;
13: PetscInt **rbuf1, **rbuf2, **rbuf3, **sbuf1, **sbuf2;
14: PetscInt **ptr;
15: PetscInt *tmp;
16: PetscInt *ctr;
17: PetscMPIInt *pa; /* process array */
18: PetscInt *req_size;
19: PetscMPIInt *req_source1, *req_source2;
20: PetscBool allcolumns, allrows;
21: PetscBool singleis;
22: PetscMPIInt *row2proc; /* row to process (MPI rank) map */
23: PetscInt nstages;
24: #if defined(PETSC_USE_CTABLE)
25: PetscHMapI cmap, rmap;
26: PetscInt *cmap_loc, *rmap_loc;
27: #else
28: PetscInt *cmap, *rmap;
29: #endif
30: PetscErrorCode (*destroy)(Mat);
31: } Mat_SubSppt;
33: /* Operations provided by MATSEQAIJ and its subclasses */
34: typedef struct {
35: PetscErrorCode (*getarray)(Mat, PetscScalar **);
36: PetscErrorCode (*restorearray)(Mat, PetscScalar **);
37: PetscErrorCode (*getarrayread)(Mat, const PetscScalar **);
38: PetscErrorCode (*restorearrayread)(Mat, const PetscScalar **);
39: PetscErrorCode (*getarraywrite)(Mat, PetscScalar **);
40: PetscErrorCode (*restorearraywrite)(Mat, PetscScalar **);
41: PetscErrorCode (*getcsrandmemtype)(Mat, const PetscInt **, const PetscInt **, PetscScalar **, PetscMemType *);
42: } Mat_SeqAIJOps;
44: /*
45: Struct header shared by SeqAIJ, SeqBAIJ and SeqSBAIJ matrix formats
46: */
47: #define SEQAIJHEADER(datatype) \
48: PetscBool roworiented; /* if true, row-oriented input, default */ \
49: PetscInt nonew; /* 1 don't add new nonzeros, -1 generate error on new */ \
50: PetscInt nounused; /* -1 generate error on unused space */ \
51: PetscInt maxnz; /* allocated nonzeros */ \
52: PetscInt *imax; /* maximum space allocated for each row */ \
53: PetscInt *ilen; /* actual length of each row */ \
54: PetscInt *ipre; /* space preallocated for each row by user */ \
55: PetscBool free_imax_ilen; \
56: PetscInt reallocs; /* number of mallocs done during MatSetValues() \
57: as more values are set than were prealloced */ \
58: PetscInt rmax; /* max nonzeros in any row */ \
59: PetscBool keepnonzeropattern; /* keeps matrix structure same in calls to MatZeroRows()*/ \
60: PetscBool ignorezeroentries; \
61: PetscBool free_ij; /* free the column indices j and row offsets i when the matrix is destroyed */ \
62: PetscBool free_a; /* free the numerical values when matrix is destroy */ \
63: Mat_CompressedRow compressedrow; /* use compressed row format */ \
64: PetscInt nz; /* nonzeros */ \
65: PetscInt *i; /* pointer to beginning of each row */ \
66: PetscInt *j; /* column values: j + i[k] - 1 is start of row k */ \
67: PetscInt *diag; /* pointers to diagonal elements */ \
68: PetscInt nonzerorowcnt; /* how many rows have nonzero entries */ \
69: PetscBool free_diag; \
70: datatype *a; /* nonzero elements */ \
71: PetscScalar *solve_work; /* work space used in MatSolve */ \
72: IS row, col, icol; /* index sets, used for reorderings */ \
73: PetscBool pivotinblocks; /* pivot inside factorization of each diagonal block */ \
74: Mat parent; /* set if this matrix was formed with MatDuplicate(...,MAT_SHARE_NONZERO_PATTERN,....); \
75: means that this shares some data structures with the parent including diag, ilen, imax, i, j */ \
76: Mat_SubSppt *submatis1; /* used by MatCreateSubMatrices_MPIXAIJ_Local */ \
77: Mat_SeqAIJOps ops[1] /* operations for SeqAIJ and its subclasses */
79: typedef struct {
80: MatTransposeColoring matcoloring;
81: Mat Bt_den; /* dense matrix of B^T */
82: Mat ABt_den; /* dense matrix of A*B^T */
83: PetscBool usecoloring;
84: } Mat_MatMatTransMult;
86: typedef struct { /* used by MatTransposeMatMult() */
87: Mat At; /* transpose of the first matrix */
88: Mat mA; /* maij matrix of A */
89: Vec bt, ct; /* vectors to hold locally transposed arrays of B and C */
90: /* used by PtAP */
91: void *data;
92: PetscErrorCode (*destroy)(void *);
93: } Mat_MatTransMatMult;
95: typedef struct {
96: PetscInt *api, *apj; /* symbolic structure of A*P */
97: PetscScalar *apa; /* temporary array for storing one row of A*P */
98: } Mat_AP;
100: typedef struct {
101: MatTransposeColoring matcoloring;
102: Mat Rt; /* sparse or dense matrix of R^T */
103: Mat RARt; /* dense matrix of R*A*R^T */
104: Mat ARt; /* A*R^T used for the case -matrart_color_art */
105: MatScalar *work; /* work array to store columns of A*R^T used in MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqDense() */
106: /* free intermediate products needed for PtAP */
107: void *data;
108: PetscErrorCode (*destroy)(void *);
109: } Mat_RARt;
111: typedef struct {
112: Mat BC; /* temp matrix for storing B*C */
113: } Mat_MatMatMatMult;
115: /*
116: MATSEQAIJ format - Compressed row storage (also called Yale sparse matrix
117: format) or compressed sparse row (CSR). The i[] and j[] arrays start at 0. For example,
118: j[i[k]+p] is the pth column in row k. Note that the diagonal
119: matrix elements are stored with the rest of the nonzeros (not separately).
120: */
122: /* Info about i-nodes (identical nodes) helper class for SeqAIJ */
123: typedef struct {
124: MatScalar *bdiag, *ibdiag, *ssor_work; /* diagonal blocks of matrix used for MatSOR_SeqAIJ_Inode() */
125: PetscInt bdiagsize; /* length of bdiag and ibdiag */
126: PetscBool ibdiagvalid; /* do ibdiag[] and bdiag[] contain the most recent values */
128: PetscBool use;
129: PetscInt node_count; /* number of inodes */
130: PetscInt *size; /* size of each inode */
131: PetscInt limit; /* inode limit */
132: PetscInt max_limit; /* maximum supported inode limit */
133: PetscBool checked; /* if inodes have been checked for */
134: PetscObjectState mat_nonzerostate; /* non-zero state when inodes were checked for */
135: } Mat_SeqAIJ_Inode;
137: PETSC_INTERN PetscErrorCode MatView_SeqAIJ_Inode(Mat, PetscViewer);
138: PETSC_INTERN PetscErrorCode MatAssemblyEnd_SeqAIJ_Inode(Mat, MatAssemblyType);
139: PETSC_INTERN PetscErrorCode MatDestroy_SeqAIJ_Inode(Mat);
140: PETSC_INTERN PetscErrorCode MatCreate_SeqAIJ_Inode(Mat);
141: PETSC_INTERN PetscErrorCode MatSetOption_SeqAIJ_Inode(Mat, MatOption, PetscBool);
142: PETSC_INTERN PetscErrorCode MatDuplicate_SeqAIJ_Inode(Mat, MatDuplicateOption, Mat *);
143: PETSC_INTERN PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat, Mat, MatDuplicateOption, PetscBool);
144: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_Inode(Mat, Mat, const MatFactorInfo *);
145: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat, PetscScalar **);
146: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat, PetscScalar **);
148: typedef struct {
149: SEQAIJHEADER(MatScalar);
150: Mat_SeqAIJ_Inode inode;
151: MatScalar *saved_values; /* location for stashing nonzero values of matrix */
153: PetscScalar *idiag, *mdiag, *ssor_work; /* inverse of diagonal entries, diagonal values and workspace for Eisenstat trick */
154: PetscBool idiagvalid; /* current idiag[] and mdiag[] are valid */
155: PetscScalar *ibdiag; /* inverses of block diagonals */
156: PetscBool ibdiagvalid; /* inverses of block diagonals are valid. */
157: PetscBool diagonaldense; /* all entries along the diagonal have been set; i.e. no missing diagonal terms */
158: PetscScalar fshift, omega; /* last used omega and fshift */
160: /* MatSetValues() via hash related fields */
161: PetscHMapIJV ht;
162: PetscInt *dnz;
163: struct _MatOps cops;
164: } Mat_SeqAIJ;
166: typedef struct {
167: PetscInt nz; /* nz of the matrix after assembly */
168: PetscCount n; /* Number of entries in MatSetPreallocationCOO() */
169: PetscCount Atot; /* Total number of valid (i.e., w/ non-negative indices) entries in the COO array */
170: PetscCount *jmap; /* perm[jmap[i]..jmap[i+1]) give indices of entries in v[] associated with i-th nonzero of the matrix */
171: PetscCount *perm; /* The permutation array in sorting (i,j) by row and then by col */
172: } MatCOOStruct_SeqAIJ;
174: #define MatSeqXAIJGetOptions_Private(A) \
175: { \
176: const PetscBool oldvalues = (PetscBool)(A != PETSC_NULLPTR); \
177: PetscInt nonew = 0, nounused = 0; \
178: PetscBool roworiented = PETSC_FALSE; \
179: if (oldvalues) { \
180: nonew = ((Mat_SeqAIJ *)A->data)->nonew; \
181: nounused = ((Mat_SeqAIJ *)A->data)->nounused; \
182: roworiented = ((Mat_SeqAIJ *)A->data)->roworiented; \
183: } \
184: (void)0
186: #define MatSeqXAIJRestoreOptions_Private(A) \
187: if (oldvalues) { \
188: ((Mat_SeqAIJ *)A->data)->nonew = nonew; \
189: ((Mat_SeqAIJ *)A->data)->nounused = nounused; \
190: ((Mat_SeqAIJ *)A->data)->roworiented = roworiented; \
191: } \
192: } \
193: (void)0
195: static inline PetscErrorCode MatXAIJAllocatea(Mat A, PetscInt nz, PetscScalar **array)
196: {
197: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
199: PetscFunctionBegin;
200: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)array));
201: a->free_a = PETSC_TRUE;
202: PetscFunctionReturn(PETSC_SUCCESS);
203: }
205: static inline PetscErrorCode MatXAIJDeallocatea(Mat A, PetscScalar **array)
206: {
207: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
209: PetscFunctionBegin;
210: if (a->free_a) PetscCall(PetscShmgetDeallocateArray((void **)array));
211: a->free_a = PETSC_FALSE;
212: PetscFunctionReturn(PETSC_SUCCESS);
213: }
215: /*
216: Frees the a, i, and j arrays from the XAIJ (AIJ, BAIJ, and SBAIJ) matrix types
217: */
218: static inline PetscErrorCode MatSeqXAIJFreeAIJ(Mat AA, MatScalar **a, PetscInt **j, PetscInt **i)
219: {
220: Mat_SeqAIJ *A = (Mat_SeqAIJ *)AA->data;
222: PetscFunctionBegin;
223: if (A->free_a) PetscCall(PetscShmgetDeallocateArray((void **)a));
224: if (A->free_ij) PetscCall(PetscShmgetDeallocateArray((void **)j));
225: if (A->free_ij) PetscCall(PetscShmgetDeallocateArray((void **)i));
226: PetscFunctionReturn(PETSC_SUCCESS);
227: }
228: /*
229: Allocates larger a, i, and j arrays for the XAIJ (AIJ, BAIJ, and SBAIJ) matrix types
230: This is a macro because it takes the datatype as an argument which can be either a Mat or a MatScalar
231: */
232: #define MatSeqXAIJReallocateAIJ(Amat, AM, BS2, NROW, ROW, COL, RMAX, AA, AI, AJ, RP, AP, AIMAX, NONEW, datatype) \
233: do { \
234: if (NROW >= RMAX) { \
235: Mat_SeqAIJ *Ain = (Mat_SeqAIJ *)Amat->data; \
236: PetscInt CHUNKSIZE = 15, new_nz = AI[AM] + CHUNKSIZE, len, *new_i = NULL, *new_j = NULL; \
237: datatype *new_a; \
238: \
239: PetscCheck(NONEW != -2, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "New nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") caused a malloc. Use MatSetOption(A, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE) to turn off this check", ROW, COL); \
240: /* malloc new storage space */ \
241: PetscCall(PetscShmgetAllocateArray(BS2 *new_nz, sizeof(PetscScalar), (void **)&new_a)); \
242: PetscCall(PetscShmgetAllocateArray(new_nz, sizeof(PetscInt), (void **)&new_j)); \
243: PetscCall(PetscShmgetAllocateArray(AM + 1, sizeof(PetscInt), (void **)&new_i)); \
244: Ain->free_a = PETSC_TRUE; \
245: Ain->free_ij = PETSC_TRUE; \
246: /* copy over old data into new slots */ \
247: for (ii = 0; ii < ROW + 1; ii++) new_i[ii] = AI[ii]; \
248: for (ii = ROW + 1; ii < AM + 1; ii++) new_i[ii] = AI[ii] + CHUNKSIZE; \
249: PetscCall(PetscArraycpy(new_j, AJ, AI[ROW] + NROW)); \
250: len = (new_nz - CHUNKSIZE - AI[ROW] - NROW); \
251: PetscCall(PetscArraycpy(new_j + AI[ROW] + NROW + CHUNKSIZE, PetscSafePointerPlusOffset(AJ, AI[ROW] + NROW), len)); \
252: PetscCall(PetscArraycpy(new_a, AA, BS2 *(AI[ROW] + NROW))); \
253: PetscCall(PetscArrayzero(new_a + BS2 * (AI[ROW] + NROW), BS2 * CHUNKSIZE)); \
254: PetscCall(PetscArraycpy(new_a + BS2 * (AI[ROW] + NROW + CHUNKSIZE), PetscSafePointerPlusOffset(AA, BS2 * (AI[ROW] + NROW)), BS2 * len)); \
255: /* free up old matrix storage */ \
256: PetscCall(MatSeqXAIJFreeAIJ(A, &Ain->a, &Ain->j, &Ain->i)); \
257: AA = new_a; \
258: Ain->a = new_a; \
259: AI = Ain->i = new_i; \
260: AJ = Ain->j = new_j; \
261: \
262: RP = AJ + AI[ROW]; \
263: AP = AA + BS2 * AI[ROW]; \
264: RMAX = AIMAX[ROW] = AIMAX[ROW] + CHUNKSIZE; \
265: Ain->maxnz += BS2 * CHUNKSIZE; \
266: Ain->reallocs++; \
267: Amat->nonzerostate++; \
268: } \
269: } while (0)
271: #define MatSeqXAIJReallocateAIJ_structure_only(Amat, AM, BS2, NROW, ROW, COL, RMAX, AI, AJ, RP, AIMAX, NONEW, datatype) \
272: do { \
273: if (NROW >= RMAX) { \
274: Mat_SeqAIJ *Ain = (Mat_SeqAIJ *)Amat->data; \
275: /* there is no extra room in row, therefore enlarge */ \
276: PetscInt CHUNKSIZE = 15, new_nz = AI[AM] + CHUNKSIZE, len, *new_i = NULL, *new_j = NULL; \
277: \
278: PetscCheck(NONEW != -2, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "New nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") caused a malloc. Use MatSetOption(A, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_FALSE) to turn off this check", ROW, COL); \
279: /* malloc new storage space */ \
280: PetscCall(PetscShmgetAllocateArray(new_nz, sizeof(PetscInt), (void **)&new_j)); \
281: PetscCall(PetscShmgetAllocateArray(AM + 1, sizeof(PetscInt), (void **)&new_i)); \
282: Ain->free_a = PETSC_FALSE; \
283: Ain->free_ij = PETSC_TRUE; \
284: \
285: /* copy over old data into new slots */ \
286: for (ii = 0; ii < ROW + 1; ii++) new_i[ii] = AI[ii]; \
287: for (ii = ROW + 1; ii < AM + 1; ii++) new_i[ii] = AI[ii] + CHUNKSIZE; \
288: PetscCall(PetscArraycpy(new_j, AJ, AI[ROW] + NROW)); \
289: len = (new_nz - CHUNKSIZE - AI[ROW] - NROW); \
290: PetscCall(PetscArraycpy(new_j + AI[ROW] + NROW + CHUNKSIZE, AJ + AI[ROW] + NROW, len)); \
291: \
292: /* free up old matrix storage */ \
293: PetscCall(MatSeqXAIJFreeAIJ(A, &Ain->a, &Ain->j, &Ain->i)); \
294: Ain->a = NULL; \
295: AI = Ain->i = new_i; \
296: AJ = Ain->j = new_j; \
297: \
298: RP = AJ + AI[ROW]; \
299: RMAX = AIMAX[ROW] = AIMAX[ROW] + CHUNKSIZE; \
300: Ain->maxnz += BS2 * CHUNKSIZE; \
301: Ain->reallocs++; \
302: Amat->nonzerostate++; \
303: } \
304: } while (0)
306: PETSC_INTERN PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat, PetscInt, const PetscInt *);
307: PETSC_INTERN PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat, PetscCount, PetscInt[], PetscInt[]);
309: PETSC_INTERN PetscErrorCode MatILUFactorSymbolic_SeqAIJ(Mat, Mat, IS, IS, const MatFactorInfo *);
310: PETSC_INTERN PetscErrorCode MatILUFactorSymbolic_SeqAIJ_ilu0(Mat, Mat, IS, IS, const MatFactorInfo *);
312: PETSC_INTERN PetscErrorCode MatICCFactorSymbolic_SeqAIJ(Mat, Mat, IS, const MatFactorInfo *);
313: PETSC_INTERN PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJ(Mat, Mat, IS, const MatFactorInfo *);
314: PETSC_INTERN PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ_inplace(Mat, Mat, const MatFactorInfo *);
315: PETSC_INTERN PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ(Mat, Mat, const MatFactorInfo *);
316: PETSC_INTERN PetscErrorCode MatDuplicate_SeqAIJ(Mat, MatDuplicateOption, Mat *);
317: PETSC_INTERN PetscErrorCode MatCopy_SeqAIJ(Mat, Mat, MatStructure);
318: PETSC_INTERN PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat, PetscBool *, PetscInt *);
319: PETSC_INTERN PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat);
320: PETSC_INTERN PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat, PetscInt *, PetscInt **);
322: PETSC_INTERN PetscErrorCode MatMult_SeqAIJ(Mat, Vec, Vec);
323: PETSC_INTERN PetscErrorCode MatMult_SeqAIJ_Inode(Mat, Vec, Vec);
324: PETSC_INTERN PetscErrorCode MatMultAdd_SeqAIJ(Mat, Vec, Vec, Vec);
325: PETSC_INTERN PetscErrorCode MatMultAdd_SeqAIJ_Inode(Mat, Vec, Vec, Vec);
326: PETSC_INTERN PetscErrorCode MatMultTranspose_SeqAIJ(Mat, Vec, Vec);
327: PETSC_INTERN PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat, Vec, Vec, Vec);
328: PETSC_INTERN PetscErrorCode MatSOR_SeqAIJ(Mat, Vec, PetscReal, MatSORType, PetscReal, PetscInt, PetscInt, Vec);
329: PETSC_INTERN PetscErrorCode MatSOR_SeqAIJ_Inode(Mat, Vec, PetscReal, MatSORType, PetscReal, PetscInt, PetscInt, Vec);
331: PETSC_INTERN PetscErrorCode MatSetOption_SeqAIJ(Mat, MatOption, PetscBool);
333: PETSC_INTERN PetscErrorCode MatGetSymbolicTranspose_SeqAIJ(Mat, PetscInt *[], PetscInt *[]);
334: PETSC_INTERN PetscErrorCode MatRestoreSymbolicTranspose_SeqAIJ(Mat, PetscInt *[], PetscInt *[]);
335: PETSC_INTERN PetscErrorCode MatGetSymbolicTransposeReduced_SeqAIJ(Mat, PetscInt, PetscInt, PetscInt *[], PetscInt *[]);
336: PETSC_INTERN PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat, Mat *);
337: PETSC_INTERN PetscErrorCode MatTranspose_SeqAIJ(Mat, MatReuse, Mat *);
339: PETSC_INTERN PetscErrorCode MatToSymmetricIJ_SeqAIJ(PetscInt, PetscInt *, PetscInt *, PetscBool, PetscInt, PetscInt, PetscInt **, PetscInt **);
340: PETSC_INTERN PetscErrorCode MatLUFactorSymbolic_SeqAIJ(Mat, Mat, IS, IS, const MatFactorInfo *);
341: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_inplace(Mat, Mat, const MatFactorInfo *);
342: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ(Mat, Mat, const MatFactorInfo *);
343: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(Mat, Mat, const MatFactorInfo *);
344: PETSC_INTERN PetscErrorCode MatLUFactor_SeqAIJ(Mat, IS, IS, const MatFactorInfo *);
345: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_inplace(Mat, Vec, Vec);
346: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ(Mat, Vec, Vec);
347: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_Inode(Mat, Vec, Vec);
348: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_NaturalOrdering(Mat, Vec, Vec);
349: PETSC_INTERN PetscErrorCode MatSolveAdd_SeqAIJ(Mat, Vec, Vec, Vec);
350: PETSC_INTERN PetscErrorCode MatSolveTranspose_SeqAIJ_inplace(Mat, Vec, Vec);
351: PETSC_INTERN PetscErrorCode MatSolveTranspose_SeqAIJ(Mat, Vec, Vec);
352: PETSC_INTERN PetscErrorCode MatSolveTransposeAdd_SeqAIJ_inplace(Mat, Vec, Vec, Vec);
353: PETSC_INTERN PetscErrorCode MatSolveTransposeAdd_SeqAIJ(Mat, Vec, Vec, Vec);
354: PETSC_INTERN PetscErrorCode MatMatSolve_SeqAIJ(Mat, Mat, Mat);
355: PETSC_INTERN PetscErrorCode MatMatSolveTranspose_SeqAIJ(Mat, Mat, Mat);
356: PETSC_INTERN PetscErrorCode MatEqual_SeqAIJ(Mat, Mat, PetscBool *);
357: PETSC_INTERN PetscErrorCode MatFDColoringCreate_SeqXAIJ(Mat, ISColoring, MatFDColoring);
358: PETSC_INTERN PetscErrorCode MatFDColoringSetUp_SeqXAIJ(Mat, ISColoring, MatFDColoring);
359: PETSC_INTERN PetscErrorCode MatFDColoringSetUpBlocked_AIJ_Private(Mat, MatFDColoring, PetscInt);
360: PETSC_INTERN PetscErrorCode MatLoad_AIJ_HDF5(Mat, PetscViewer);
361: PETSC_INTERN PetscErrorCode MatLoad_SeqAIJ_Binary(Mat, PetscViewer);
362: PETSC_INTERN PetscErrorCode MatLoad_SeqAIJ(Mat, PetscViewer);
363: PETSC_INTERN PetscErrorCode RegisterApplyPtAPRoutines_Private(Mat);
365: #if defined(PETSC_HAVE_HYPRE)
366: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_Transpose_AIJ_AIJ(Mat);
367: #endif
368: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ(Mat);
370: PETSC_INTERN PetscErrorCode MatProductSymbolic_SeqAIJ_SeqAIJ(Mat);
371: PETSC_INTERN PetscErrorCode MatProductSymbolic_PtAP_SeqAIJ_SeqAIJ(Mat);
372: PETSC_INTERN PetscErrorCode MatProductSymbolic_RARt_SeqAIJ_SeqAIJ(Mat);
374: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
375: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Sorted(Mat, Mat, PetscReal, Mat);
376: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat, Mat, PetscReal, Mat);
377: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat, Mat, PetscReal, Mat);
378: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat, Mat, PetscReal, Mat);
379: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat, Mat, PetscReal, Mat);
380: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat, Mat, PetscReal, Mat);
381: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_RowMerge(Mat, Mat, PetscReal, Mat);
382: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat, Mat, PetscReal, Mat);
383: #if defined(PETSC_HAVE_HYPRE)
384: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_AIJ_AIJ_wHYPRE(Mat, Mat, PetscReal, Mat);
385: #endif
387: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
388: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted(Mat, Mat, Mat);
390: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat, Mat, Mat);
391: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat, Mat, Mat);
393: PETSC_INTERN PetscErrorCode MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy(Mat, Mat, PetscReal, Mat);
394: PETSC_INTERN PetscErrorCode MatPtAPNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
395: PETSC_INTERN PetscErrorCode MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy(Mat, Mat, Mat);
397: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
398: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ_matmattransposemult(Mat, Mat, PetscReal, Mat);
399: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ_colorrart(Mat, Mat, PetscReal, Mat);
400: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
401: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ_matmattransposemult(Mat, Mat, Mat);
402: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ_colorrart(Mat, Mat, Mat);
404: PETSC_INTERN PetscErrorCode MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
405: PETSC_INTERN PetscErrorCode MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
406: PETSC_INTERN PetscErrorCode MatDestroy_SeqAIJ_MatTransMatMult(void *);
408: PETSC_INTERN PetscErrorCode MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
409: PETSC_INTERN PetscErrorCode MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
410: PETSC_INTERN PetscErrorCode MatTransposeColoringCreate_SeqAIJ(Mat, ISColoring, MatTransposeColoring);
411: PETSC_INTERN PetscErrorCode MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring, Mat, Mat);
412: PETSC_INTERN PetscErrorCode MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring, Mat, Mat);
414: PETSC_INTERN PetscErrorCode MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ(Mat, Mat, Mat, PetscReal, Mat);
415: PETSC_INTERN PetscErrorCode MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ(Mat, Mat, Mat, Mat);
417: PETSC_INTERN PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat, PetscInt, PetscInt, PetscRandom);
418: PETSC_INTERN PetscErrorCode MatSetValues_SeqAIJ(Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[], const PetscScalar[], InsertMode);
419: PETSC_INTERN PetscErrorCode MatGetRow_SeqAIJ(Mat, PetscInt, PetscInt *, PetscInt **, PetscScalar **);
420: PETSC_INTERN PetscErrorCode MatRestoreRow_SeqAIJ(Mat, PetscInt, PetscInt *, PetscInt **, PetscScalar **);
421: PETSC_INTERN PetscErrorCode MatScale_SeqAIJ(Mat, PetscScalar);
422: PETSC_INTERN PetscErrorCode MatDiagonalScale_SeqAIJ(Mat, Vec, Vec);
423: PETSC_INTERN PetscErrorCode MatDiagonalSet_SeqAIJ(Mat, Vec, InsertMode);
424: PETSC_INTERN PetscErrorCode MatAXPY_SeqAIJ(Mat, PetscScalar, Mat, MatStructure);
425: PETSC_INTERN PetscErrorCode MatGetRowIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
426: PETSC_INTERN PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
427: PETSC_INTERN PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
428: PETSC_INTERN PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
429: PETSC_INTERN PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscInt *[], PetscBool *);
430: PETSC_INTERN PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscInt *[], PetscBool *);
431: PETSC_INTERN PetscErrorCode MatDestroy_SeqAIJ(Mat);
432: PETSC_INTERN PetscErrorCode MatView_SeqAIJ(Mat, PetscViewer);
434: PETSC_INTERN PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat);
435: PETSC_INTERN PetscErrorCode MatSeqAIJInvalidateDiagonal_Inode(Mat);
436: PETSC_INTERN PetscErrorCode MatSeqAIJCheckInode(Mat);
437: PETSC_INTERN PetscErrorCode MatSeqAIJCheckInode_FactorLU(Mat);
439: PETSC_INTERN PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat, Mat, PetscInt *);
441: #if defined(PETSC_HAVE_MATLAB)
442: PETSC_EXTERN PetscErrorCode MatlabEnginePut_SeqAIJ(PetscObject, void *);
443: PETSC_EXTERN PetscErrorCode MatlabEngineGet_SeqAIJ(PetscObject, void *);
444: #endif
445: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqSBAIJ(Mat, MatType, MatReuse, Mat *);
446: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqBAIJ(Mat, MatType, MatReuse, Mat *);
447: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat, MatType, MatReuse, Mat *);
448: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
449: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
450: #if defined(PETSC_HAVE_SCALAPACK)
451: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
452: #endif
453: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
454: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJPERM(Mat, MatType, MatReuse, Mat *);
455: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJSELL(Mat, MatType, MatReuse, Mat *);
456: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJMKL(Mat, MatType, MatReuse, Mat *);
457: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJViennaCL(Mat, MatType, MatReuse, Mat *);
458: PETSC_INTERN PetscErrorCode MatReorderForNonzeroDiagonal_SeqAIJ(Mat, PetscReal, IS, IS);
459: PETSC_INTERN PetscErrorCode MatRARt_SeqAIJ_SeqAIJ(Mat, Mat, MatReuse, PetscReal, Mat *);
460: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat);
461: PETSC_INTERN PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat, MatAssemblyType);
462: PETSC_EXTERN PetscErrorCode MatZeroEntries_SeqAIJ(Mat);
464: PETSC_INTERN PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt, const PetscInt *, const PetscInt *, const PetscInt *, const PetscInt *, PetscInt *);
465: PETSC_INTERN PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm, Mat, PetscInt, MatReuse, Mat *);
466: PETSC_INTERN PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm, Mat, PetscInt, MatReuse, Mat *);
468: PETSC_INTERN PetscErrorCode MatSetSeqMat_SeqAIJ(Mat, IS, IS, MatStructure, Mat);
469: PETSC_INTERN PetscErrorCode MatEliminateZeros_SeqAIJ(Mat, PetscBool);
470: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *);
471: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat);
472: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_Dummy(Mat);
473: PETSC_INTERN PetscErrorCode MatDestroySubMatrices_Dummy(PetscInt, Mat *[]);
474: PETSC_INTERN PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat, IS, IS, PetscInt, MatReuse, Mat *);
476: PETSC_INTERN PetscErrorCode MatSetSeqAIJWithArrays_private(MPI_Comm, PetscInt, PetscInt, PetscInt[], PetscInt[], PetscScalar[], MatType, Mat);
478: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat, ISLocalToGlobalMapping *);
480: /*
481: PetscSparseDenseMinusDot - The inner kernel of triangular solves and Gauss-Siedel smoothing. \sum_i xv[i] * r[xi[i]] for CSR storage
483: Input Parameters:
484: + nnz - the number of entries
485: . r - the array of vector values
486: . xv - the matrix values for the row
487: - xi - the column indices of the nonzeros in the row
489: Output Parameter:
490: . sum - negative the sum of results
492: PETSc compile flags:
493: + PETSC_KERNEL_USE_UNROLL_4
494: - PETSC_KERNEL_USE_UNROLL_2
496: Developer Note:
497: The macro changes sum but not other parameters
499: .seealso: `PetscSparseDensePlusDot()`
500: */
501: #if defined(PETSC_KERNEL_USE_UNROLL_4)
502: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
503: do { \
504: if (nnz > 0) { \
505: PetscInt nnz2 = nnz, rem = nnz & 0x3; \
506: switch (rem) { \
507: case 3: \
508: sum -= *xv++ * r[*xi++]; \
509: case 2: \
510: sum -= *xv++ * r[*xi++]; \
511: case 1: \
512: sum -= *xv++ * r[*xi++]; \
513: nnz2 -= rem; \
514: } \
515: while (nnz2 > 0) { \
516: sum -= xv[0] * r[xi[0]] + xv[1] * r[xi[1]] + xv[2] * r[xi[2]] + xv[3] * r[xi[3]]; \
517: xv += 4; \
518: xi += 4; \
519: nnz2 -= 4; \
520: } \
521: xv -= nnz; \
522: xi -= nnz; \
523: } \
524: } while (0)
526: #elif defined(PETSC_KERNEL_USE_UNROLL_2)
527: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
528: do { \
529: PetscInt __i, __i1, __i2; \
530: for (__i = 0; __i < nnz - 1; __i += 2) { \
531: __i1 = xi[__i]; \
532: __i2 = xi[__i + 1]; \
533: sum -= (xv[__i] * r[__i1] + xv[__i + 1] * r[__i2]); \
534: } \
535: if (nnz & 0x1) sum -= xv[__i] * r[xi[__i]]; \
536: } while (0)
538: #else
539: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
540: do { \
541: PetscInt __i; \
542: for (__i = 0; __i < nnz; __i++) sum -= xv[__i] * r[xi[__i]]; \
543: } while (0)
544: #endif
546: /*
547: PetscSparseDensePlusDot - The inner kernel of matrix-vector product \sum_i xv[i] * r[xi[i]] for CSR storage
549: Input Parameters:
550: + nnz - the number of entries
551: . r - the array of vector values
552: . xv - the matrix values for the row
553: - xi - the column indices of the nonzeros in the row
555: Output Parameter:
556: . sum - the sum of results
558: PETSc compile flags:
559: + PETSC_KERNEL_USE_UNROLL_4
560: - PETSC_KERNEL_USE_UNROLL_2
562: Developer Note:
563: The macro changes sum but not other parameters
565: .seealso: `PetscSparseDenseMinusDot()`
566: */
567: #if defined(PETSC_KERNEL_USE_UNROLL_4)
568: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
569: do { \
570: if (nnz > 0) { \
571: PetscInt nnz2 = nnz, rem = nnz & 0x3; \
572: switch (rem) { \
573: case 3: \
574: sum += *xv++ * r[*xi++]; \
575: case 2: \
576: sum += *xv++ * r[*xi++]; \
577: case 1: \
578: sum += *xv++ * r[*xi++]; \
579: nnz2 -= rem; \
580: } \
581: while (nnz2 > 0) { \
582: sum += xv[0] * r[xi[0]] + xv[1] * r[xi[1]] + xv[2] * r[xi[2]] + xv[3] * r[xi[3]]; \
583: xv += 4; \
584: xi += 4; \
585: nnz2 -= 4; \
586: } \
587: xv -= nnz; \
588: xi -= nnz; \
589: } \
590: } while (0)
592: #elif defined(PETSC_KERNEL_USE_UNROLL_2)
593: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
594: do { \
595: PetscInt __i, __i1, __i2; \
596: for (__i = 0; __i < nnz - 1; __i += 2) { \
597: __i1 = xi[__i]; \
598: __i2 = xi[__i + 1]; \
599: sum += (xv[__i] * r[__i1] + xv[__i + 1] * r[__i2]); \
600: } \
601: if (nnz & 0x1) sum += xv[__i] * r[xi[__i]]; \
602: } while (0)
604: #elif !(defined(__GNUC__) && defined(_OPENMP)) && defined(PETSC_USE_AVX512_KERNELS) && defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES) && !defined(PETSC_SKIP_IMMINTRIN_H_CUDAWORKAROUND)
605: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) PetscSparseDensePlusDot_AVX512_Private(&(sum), (r), (xv), (xi), (nnz))
607: #else
608: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
609: do { \
610: PetscInt __i; \
611: for (__i = 0; __i < nnz; __i++) sum += xv[__i] * r[xi[__i]]; \
612: } while (0)
613: #endif
615: #if defined(PETSC_USE_AVX512_KERNELS) && defined(PETSC_HAVE_IMMINTRIN_H) && defined(__AVX512F__) && defined(PETSC_USE_REAL_DOUBLE) && !defined(PETSC_USE_COMPLEX) && !defined(PETSC_USE_64BIT_INDICES) && !defined(PETSC_SKIP_IMMINTRIN_H_CUDAWORKAROUND)
616: #include <immintrin.h>
617: #if !defined(_MM_SCALE_8)
618: #define _MM_SCALE_8 8
619: #endif
621: static inline void PetscSparseDensePlusDot_AVX512_Private(PetscScalar *sum, const PetscScalar *x, const MatScalar *aa, const PetscInt *aj, PetscInt n)
622: {
623: __m512d vec_x, vec_y, vec_vals;
624: __m256i vec_idx;
625: PetscInt j;
627: vec_y = _mm512_setzero_pd();
628: for (j = 0; j < (n >> 3); j++) {
629: vec_idx = _mm256_loadu_si256((__m256i const *)aj);
630: vec_vals = _mm512_loadu_pd(aa);
631: vec_x = _mm512_i32gather_pd(vec_idx, x, _MM_SCALE_8);
632: vec_y = _mm512_fmadd_pd(vec_x, vec_vals, vec_y);
633: aj += 8;
634: aa += 8;
635: }
636: #if defined(__AVX512VL__)
637: /* masked load requires avx512vl, which is not supported by KNL */
638: if (n & 0x07) {
639: __mmask8 mask;
640: mask = (__mmask8)(0xff >> (8 - (n & 0x07)));
641: vec_idx = _mm256_mask_loadu_epi32(vec_idx, mask, aj);
642: vec_vals = _mm512_mask_loadu_pd(vec_vals, mask, aa);
643: vec_x = _mm512_mask_i32gather_pd(vec_x, mask, vec_idx, x, _MM_SCALE_8);
644: vec_y = _mm512_mask3_fmadd_pd(vec_x, vec_vals, vec_y, mask);
645: }
646: *sum += _mm512_reduce_add_pd(vec_y);
647: #else
648: *sum += _mm512_reduce_add_pd(vec_y);
649: for (j = 0; j < (n & 0x07); j++) *sum += aa[j] * x[aj[j]];
650: #endif
651: }
652: #endif
654: /*
655: PetscSparseDenseMaxDot - The inner kernel of a modified matrix-vector product \max_i xv[i] * r[xi[i]] for CSR storage
657: Input Parameters:
658: + nnz - the number of entries
659: . r - the array of vector values
660: . xv - the matrix values for the row
661: - xi - the column indices of the nonzeros in the row
663: Output Parameter:
664: . max - the max of results
666: .seealso: `PetscSparseDensePlusDot()`, `PetscSparseDenseMinusDot()`
667: */
668: #define PetscSparseDenseMaxDot(max, r, xv, xi, nnz) \
669: do { \
670: for (PetscInt __i = 0; __i < (nnz); __i++) { max = PetscMax(PetscRealPart(max), PetscRealPart((xv)[__i] * (r)[(xi)[__i]])); } \
671: } while (0)
673: /*
674: Add column indices into table for counting the max nonzeros of merged rows
675: */
676: #define MatRowMergeMax_SeqAIJ(mat, nrows, ta) \
677: do { \
678: if (mat) { \
679: for (PetscInt _row = 0; _row < (nrows); _row++) { \
680: const PetscInt _nz = (mat)->i[_row + 1] - (mat)->i[_row]; \
681: for (PetscInt _j = 0; _j < _nz; _j++) { \
682: PetscInt *_col = _j + (mat)->j + (mat)->i[_row]; \
683: PetscCall(PetscHMapISet((ta), *_col + 1, 1)); \
684: } \
685: } \
686: } \
687: } while (0)
689: /*
690: Add column indices into table for counting the nonzeros of merged rows
691: */
692: #define MatMergeRows_SeqAIJ(mat, nrows, rows, ta) \
693: do { \
694: for (PetscInt _i = 0; _i < (nrows); _i++) { \
695: const PetscInt _row = (rows)[_i]; \
696: const PetscInt _nz = (mat)->i[_row + 1] - (mat)->i[_row]; \
697: for (PetscInt _j = 0; _j < _nz; _j++) { \
698: PetscInt *_col = _j + (mat)->j + (mat)->i[_row]; \
699: PetscCall(PetscHMapISetWithMode((ta), *_col + 1, 1, INSERT_VALUES)); \
700: } \
701: } \
702: } while (0)