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 PetscDefined(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 nonzero 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: PetscObjectState diagNonzeroState; /* nonzero state of the matrix when diag was obtained */ \
69: PetscBool diagDense; /* all entries along the diagonal have been set; i.e. no missing diagonal terms */ \
70: PetscInt nonzerorowcnt; /* how many rows have nonzero entries */ \
71: datatype *a; /* nonzero elements */ \
72: PetscScalar *solve_work; /* work space used in MatSolve */ \
73: IS row, col, icol; /* index sets, used for reorderings */ \
74: PetscBool pivotinblocks; /* pivot inside factorization of each diagonal block */ \
75: Mat parent; /* set if this matrix was formed with MatDuplicate(...,MAT_SHARE_NONZERO_PATTERN,....); \
76: means that this shares some data structures with the parent including diag, ilen, imax, i, j */ \
77: Mat_SubSppt *submatis1; /* used by MatCreateSubMatrices_MPIXAIJ_Local */ \
78: Mat_SeqAIJOps ops[1] /* operations for SeqAIJ and its subclasses */
80: typedef struct {
81: MatTransposeColoring matcoloring;
82: Mat Bt_den; /* dense matrix of B^T */
83: Mat ABt_den; /* dense matrix of A*B^T */
84: PetscBool usecoloring;
85: } MatProductCtx_MatMatTransMult;
87: typedef struct { /* used by MatTransposeMatMult() */
88: Mat At; /* transpose of the first matrix */
89: Mat mA; /* maij matrix of A */
90: Vec bt, ct; /* vectors to hold locally transposed arrays of B and C */
91: /* used by PtAP */
92: void *data;
93: PetscCtxDestroyFn *destroy;
94: } MatProductCtx_MatTransMatMult;
96: typedef struct {
97: PetscInt *api, *apj; /* symbolic structure of A*P */
98: PetscScalar *apa; /* temporary array for storing one row of A*P */
99: } MatProductCtx_AP;
101: typedef struct {
102: MatTransposeColoring matcoloring;
103: Mat Rt; /* sparse or dense matrix of R^T */
104: Mat RARt; /* dense matrix of R*A*R^T */
105: Mat ARt; /* A*R^T used for the case -matrart_color_art */
106: MatScalar *work; /* work array to store columns of A*R^T used in MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqDense() */
107: /* free intermediate products needed for PtAP */
108: void *data;
109: PetscCtxDestroyFn *destroy;
110: } MatProductCtx_RARt;
112: typedef struct {
113: Mat BC; /* temp matrix for storing B*C */
114: } MatProductCtx_MatMatMatMult;
116: /*
117: MATSEQAIJ format - Compressed row storage (also called Yale sparse matrix
118: format) or compressed sparse row (CSR). The i[] and j[] arrays start at 0. For example,
119: j[i[k]+p] is the pth column in row k. Note that the diagonal
120: matrix elements are stored with the rest of the nonzeros (not separately).
121: */
123: /* Info about i-nodes (identical nodes) helper class for SeqAIJ */
124: typedef struct {
125: /* data for MatSOR_SeqAIJ_Inode() */
126: MatScalar *bdiag, *ibdiag, *ssor_work; /* diagonal blocks of matrices */
127: PetscInt bdiagsize; /* length of bdiag and ibdiag */
128: PetscObjectState ibdiagState; /* state of the matrix when ibdiag[] and bdiag[] were constructed */
130: PetscBool use;
131: PetscInt node_count; /* number of inodes */
132: PetscInt *size_csr; /* inode sizes in csr with size_csr[0] = 0 and i-th node size = size_csr[i+1] - size_csr[i], to facilitate parallel computation */
133: PetscInt limit; /* inode limit */
134: PetscInt max_limit; /* maximum supported inode limit */
135: PetscBool checked; /* if inodes have been checked for */
136: PetscObjectState mat_nonzerostate; /* non-zero state when inodes were checked for */
137: } Mat_SeqAIJ_Inode;
139: PETSC_INTERN PetscErrorCode MatView_SeqAIJ_Inode(Mat, PetscViewer);
140: PETSC_INTERN PetscErrorCode MatAssemblyEnd_SeqAIJ_Inode(Mat, MatAssemblyType);
141: PETSC_INTERN PetscErrorCode MatDestroy_SeqAIJ_Inode(Mat);
142: PETSC_INTERN PetscErrorCode MatCreate_SeqAIJ_Inode(Mat);
143: PETSC_INTERN PetscErrorCode MatSetOption_SeqAIJ_Inode(Mat, MatOption, PetscBool);
144: PETSC_INTERN PetscErrorCode MatDuplicate_SeqAIJ_Inode(Mat, MatDuplicateOption, Mat *);
145: PETSC_INTERN PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat, Mat, MatDuplicateOption, PetscBool);
146: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_Inode(Mat, Mat, const MatFactorInfo *);
147: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat, PetscScalar **);
148: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat, PetscScalar **);
150: typedef struct {
151: SEQAIJHEADER(MatScalar);
152: Mat_SeqAIJ_Inode inode;
153: MatScalar *saved_values; /* location for stashing nonzero values of matrix */
155: /* data needed for MatSOR_SeqAIJ() */
156: PetscScalar *mdiag, *idiag; /* diagonal values, inverse of diagonal entries */
157: PetscScalar *ssor_work; /* workspace for Eisenstat trick */
158: PetscObjectState idiagState; /* state of the matrix when mdiag and idiag was obtained */
159: PetscScalar fshift, omega; /* last used omega and fshift */
161: PetscScalar *ibdiag; /* inverses of block diagonals */
162: PetscInt ibdiagsize; /* length of ibdiag[], which changes if the block size does */
163: PetscObjectState ibdiagState; /* state of the matrix when ibdiag[] was obtained */
165: /* MatSetValues() via hash related fields */
166: PetscHMapIJV ht;
167: PetscInt *dnz;
168: struct _MatOps cops;
169: } Mat_SeqAIJ;
171: typedef struct {
172: PetscInt nz; /* nz of the matrix after assembly */
173: PetscCount n; /* Number of entries in MatSetPreallocationCOO() */
174: PetscCount Atot; /* Total number of valid (i.e., w/ non-negative indices) entries in the COO array */
175: PetscCount *jmap; /* perm[jmap[i]..jmap[i+1]) give indices of entries in v[] associated with i-th nonzero of the matrix */
176: PetscCount *perm; /* The permutation array in sorting (i,j) by row and then by col */
177: } MatCOOStruct_SeqAIJ;
179: #define MatSeqXAIJGetOptions_Private(A) \
180: { \
181: const PetscBool oldvalues = (PetscBool)(A != PETSC_NULLPTR); \
182: PetscInt nonew = 0, nounused = 0; \
183: PetscBool roworiented = PETSC_FALSE; \
184: if (oldvalues) { \
185: nonew = ((Mat_SeqAIJ *)A->data)->nonew; \
186: nounused = ((Mat_SeqAIJ *)A->data)->nounused; \
187: roworiented = ((Mat_SeqAIJ *)A->data)->roworiented; \
188: } \
189: (void)0
191: #define MatSeqSBAIJGetOptions_Private(A) \
192: { \
193: PetscBool ignore_ltriangular = PETSC_FALSE, getrow_utriangular = PETSC_FALSE; \
194: MatSeqXAIJGetOptions_Private(A); \
195: if (oldvalues) { \
196: ignore_ltriangular = ((Mat_SeqSBAIJ *)A->data)->ignore_ltriangular; \
197: getrow_utriangular = ((Mat_SeqSBAIJ *)A->data)->getrow_utriangular; \
198: } \
199: (void)0
201: #define MatSeqXAIJRestoreOptions_Private(A) \
202: if (oldvalues) { \
203: ((Mat_SeqAIJ *)A->data)->nonew = nonew; \
204: ((Mat_SeqAIJ *)A->data)->nounused = nounused; \
205: ((Mat_SeqAIJ *)A->data)->roworiented = roworiented; \
206: } \
207: } \
208: (void)0
210: #define MatSeqSBAIJRestoreOptions_Private(A) \
211: if (oldvalues) { \
212: ((Mat_SeqSBAIJ *)A->data)->ignore_ltriangular = ignore_ltriangular; \
213: ((Mat_SeqSBAIJ *)A->data)->getrow_utriangular = getrow_utriangular; \
214: } \
215: MatSeqXAIJRestoreOptions_Private(A); \
216: } \
217: (void)0
219: static inline PetscErrorCode MatXAIJAllocatea(Mat A, PetscInt nz, PetscScalar **array)
220: {
221: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
223: PetscFunctionBegin;
224: PetscCall(PetscShmgetAllocateArray(nz, sizeof(PetscScalar), (void **)array));
225: a->free_a = PETSC_TRUE;
226: PetscFunctionReturn(PETSC_SUCCESS);
227: }
229: static inline PetscErrorCode MatXAIJDeallocatea(Mat A, PetscScalar **array)
230: {
231: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
233: PetscFunctionBegin;
234: if (a->free_a) PetscCall(PetscShmgetDeallocateArray((void **)array));
235: a->free_a = PETSC_FALSE;
236: PetscFunctionReturn(PETSC_SUCCESS);
237: }
239: /*
240: Frees the a, i, and j arrays from the XAIJ (AIJ, BAIJ, and SBAIJ) matrix types
241: */
242: static inline PetscErrorCode MatSeqXAIJFreeAIJ(Mat AA, MatScalar **a, PetscInt **j, PetscInt **i)
243: {
244: Mat_SeqAIJ *A = (Mat_SeqAIJ *)AA->data;
246: PetscFunctionBegin;
247: if (A->free_a) PetscCall(PetscShmgetDeallocateArray((void **)a));
248: if (A->free_ij) PetscCall(PetscShmgetDeallocateArray((void **)j));
249: if (A->free_ij) PetscCall(PetscShmgetDeallocateArray((void **)i));
250: PetscFunctionReturn(PETSC_SUCCESS);
251: }
252: /*
253: Allocates larger a, i, and j arrays for the XAIJ (AIJ, BAIJ, and SBAIJ) matrix types
254: This is a macro because it takes the datatype as an argument which can be either a Mat or a MatScalar
255: */
256: #define MatSeqXAIJReallocateAIJ(Amat, AM, BS2, NROW, ROW, COL, RMAX, AA, AI, AJ, RP, AP, AIMAX, NONEW, datatype) \
257: do { \
258: if (NROW >= RMAX) { \
259: Mat_SeqAIJ *Ain = (Mat_SeqAIJ *)Amat->data; \
260: PetscInt CHUNKSIZE = 15, new_nz = AI[AM] + CHUNKSIZE, len, *new_i = NULL, *new_j = NULL; \
261: datatype *new_a; \
262: \
263: 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); \
264: /* malloc new storage space */ \
265: PetscCall(PetscShmgetAllocateArray(BS2 * new_nz, sizeof(PetscScalar), (void **)&new_a)); \
266: PetscCall(PetscShmgetAllocateArray(new_nz, sizeof(PetscInt), (void **)&new_j)); \
267: PetscCall(PetscShmgetAllocateArray(AM + 1, sizeof(PetscInt), (void **)&new_i)); \
268: Ain->free_a = PETSC_TRUE; \
269: Ain->free_ij = PETSC_TRUE; \
270: /* copy over old data into new slots */ \
271: for (ii = 0; ii < ROW + 1; ii++) new_i[ii] = AI[ii]; \
272: for (ii = ROW + 1; ii < AM + 1; ii++) new_i[ii] = AI[ii] + CHUNKSIZE; \
273: PetscCall(PetscArraycpy(new_j, AJ, AI[ROW] + NROW)); \
274: len = (new_nz - CHUNKSIZE - AI[ROW] - NROW); \
275: PetscCall(PetscArraycpy(new_j + AI[ROW] + NROW + CHUNKSIZE, PetscSafePointerPlusOffset(AJ, AI[ROW] + NROW), len)); \
276: PetscCall(PetscArraycpy(new_a, AA, BS2 * (AI[ROW] + NROW))); \
277: PetscCall(PetscArrayzero(new_a + BS2 * (AI[ROW] + NROW), BS2 * CHUNKSIZE)); \
278: PetscCall(PetscArraycpy(new_a + BS2 * (AI[ROW] + NROW + CHUNKSIZE), PetscSafePointerPlusOffset(AA, BS2 * (AI[ROW] + NROW)), BS2 * len)); \
279: /* free up old matrix storage */ \
280: PetscCall(MatSeqXAIJFreeAIJ(A, &Ain->a, &Ain->j, &Ain->i)); \
281: AA = new_a; \
282: Ain->a = new_a; \
283: AI = Ain->i = new_i; \
284: AJ = Ain->j = new_j; \
285: \
286: RP = AJ + AI[ROW]; \
287: AP = AA + BS2 * AI[ROW]; \
288: RMAX = AIMAX[ROW] = AIMAX[ROW] + CHUNKSIZE; \
289: Ain->maxnz += BS2 * CHUNKSIZE; \
290: Ain->reallocs++; \
291: Amat->nonzerostate++; \
292: } \
293: } while (0)
295: #define MatSeqXAIJReallocateAIJ_structure_only(Amat, AM, BS2, NROW, ROW, COL, RMAX, AI, AJ, RP, AIMAX, NONEW, datatype) \
296: do { \
297: if (NROW >= RMAX) { \
298: Mat_SeqAIJ *Ain = (Mat_SeqAIJ *)Amat->data; \
299: /* there is no extra room in row, therefore enlarge */ \
300: PetscInt CHUNKSIZE = 15, new_nz = AI[AM] + CHUNKSIZE, len, *new_i = NULL, *new_j = NULL; \
301: \
302: 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); \
303: /* malloc new storage space */ \
304: PetscCall(PetscShmgetAllocateArray(new_nz, sizeof(PetscInt), (void **)&new_j)); \
305: PetscCall(PetscShmgetAllocateArray(AM + 1, sizeof(PetscInt), (void **)&new_i)); \
306: Ain->free_a = PETSC_FALSE; \
307: Ain->free_ij = PETSC_TRUE; \
308: \
309: /* copy over old data into new slots */ \
310: for (ii = 0; ii < ROW + 1; ii++) new_i[ii] = AI[ii]; \
311: for (ii = ROW + 1; ii < AM + 1; ii++) new_i[ii] = AI[ii] + CHUNKSIZE; \
312: PetscCall(PetscArraycpy(new_j, AJ, AI[ROW] + NROW)); \
313: len = (new_nz - CHUNKSIZE - AI[ROW] - NROW); \
314: PetscCall(PetscArraycpy(new_j + AI[ROW] + NROW + CHUNKSIZE, AJ + AI[ROW] + NROW, len)); \
315: \
316: /* free up old matrix storage */ \
317: PetscCall(MatSeqXAIJFreeAIJ(A, &Ain->a, &Ain->j, &Ain->i)); \
318: Ain->a = NULL; \
319: AI = Ain->i = new_i; \
320: AJ = Ain->j = new_j; \
321: \
322: RP = AJ + AI[ROW]; \
323: RMAX = AIMAX[ROW] = AIMAX[ROW] + CHUNKSIZE; \
324: Ain->maxnz += BS2 * CHUNKSIZE; \
325: Ain->reallocs++; \
326: Amat->nonzerostate++; \
327: } \
328: } while (0)
330: PETSC_INTERN PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat, PetscInt, const PetscInt *);
331: PETSC_INTERN PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat, PetscCount, PetscInt[], PetscInt[]);
333: PETSC_INTERN PetscErrorCode MatILUFactorSymbolic_SeqAIJ(Mat, Mat, IS, IS, const MatFactorInfo *);
334: PETSC_INTERN PetscErrorCode MatILUFactorSymbolic_SeqAIJ_ilu0(Mat, Mat, IS, IS, const MatFactorInfo *);
336: PETSC_INTERN PetscErrorCode MatICCFactorSymbolic_SeqAIJ(Mat, Mat, IS, const MatFactorInfo *);
337: PETSC_INTERN PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJ(Mat, Mat, IS, const MatFactorInfo *);
338: PETSC_INTERN PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ_inplace(Mat, Mat, const MatFactorInfo *);
339: PETSC_INTERN PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ(Mat, Mat, const MatFactorInfo *);
340: PETSC_INTERN PetscErrorCode MatDuplicate_SeqAIJ(Mat, MatDuplicateOption, Mat *);
341: PETSC_INTERN PetscErrorCode MatCopy_SeqAIJ(Mat, Mat, MatStructure);
342: PETSC_EXTERN PetscErrorCode MatGetDiagonalMarkers_SeqAIJ(Mat, const PetscInt **, PetscBool *);
343: PETSC_INTERN PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat, PetscInt *, PetscInt **);
345: PETSC_INTERN PetscErrorCode MatMult_SeqAIJ(Mat, Vec, Vec);
346: PETSC_INTERN PetscErrorCode MatMult_SeqAIJ_Inode(Mat, Vec, Vec);
347: PETSC_INTERN PetscErrorCode MatMultAdd_SeqAIJ(Mat, Vec, Vec, Vec);
348: PETSC_INTERN PetscErrorCode MatMultAdd_SeqAIJ_Inode(Mat, Vec, Vec, Vec);
349: PETSC_INTERN PetscErrorCode MatMultTranspose_SeqAIJ(Mat, Vec, Vec);
350: PETSC_INTERN PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat, Vec, Vec, Vec);
351: PETSC_INTERN PetscErrorCode MatSOR_SeqAIJ(Mat, Vec, PetscReal, MatSORType, PetscReal, PetscInt, PetscInt, Vec);
352: PETSC_INTERN PetscErrorCode MatSOR_SeqAIJ_Inode(Mat, Vec, PetscReal, MatSORType, PetscReal, PetscInt, PetscInt, Vec);
354: PETSC_INTERN PetscErrorCode MatSetOption_SeqAIJ(Mat, MatOption, PetscBool);
356: PETSC_INTERN PetscErrorCode MatGetSymbolicTranspose_SeqAIJ(Mat, PetscInt *[], PetscInt *[]);
357: PETSC_INTERN PetscErrorCode MatRestoreSymbolicTranspose_SeqAIJ(Mat, PetscInt *[], PetscInt *[]);
358: PETSC_INTERN PetscErrorCode MatGetSymbolicTransposeReduced_SeqAIJ(Mat, PetscInt, PetscInt, PetscInt *[], PetscInt *[]);
359: PETSC_INTERN PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat, Mat *);
360: PETSC_INTERN PetscErrorCode MatTranspose_SeqAIJ(Mat, MatReuse, Mat *);
362: PETSC_INTERN PetscErrorCode MatToSymmetricIJ_SeqAIJ(PetscInt, PetscInt *, PetscInt *, PetscBool, PetscInt, PetscInt, PetscInt **, PetscInt **);
363: PETSC_INTERN PetscErrorCode MatLUFactorSymbolic_SeqAIJ(Mat, Mat, IS, IS, const MatFactorInfo *);
364: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_inplace(Mat, Mat, const MatFactorInfo *);
365: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ(Mat, Mat, const MatFactorInfo *);
366: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(Mat, Mat, const MatFactorInfo *);
367: PETSC_INTERN PetscErrorCode MatLUFactor_SeqAIJ(Mat, IS, IS, const MatFactorInfo *);
368: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_inplace(Mat, Vec, Vec);
369: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ(Mat, Vec, Vec);
370: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_Inode(Mat, Vec, Vec);
371: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_NaturalOrdering(Mat, Vec, Vec);
372: PETSC_INTERN PetscErrorCode MatSolveAdd_SeqAIJ(Mat, Vec, Vec, Vec);
373: PETSC_INTERN PetscErrorCode MatSolveTranspose_SeqAIJ_inplace(Mat, Vec, Vec);
374: PETSC_INTERN PetscErrorCode MatSolveTranspose_SeqAIJ(Mat, Vec, Vec);
375: PETSC_INTERN PetscErrorCode MatSolveTransposeAdd_SeqAIJ_inplace(Mat, Vec, Vec, Vec);
376: PETSC_INTERN PetscErrorCode MatSolveTransposeAdd_SeqAIJ(Mat, Vec, Vec, Vec);
377: PETSC_INTERN PetscErrorCode MatMatSolve_SeqAIJ(Mat, Mat, Mat);
378: PETSC_INTERN PetscErrorCode MatMatSolveTranspose_SeqAIJ(Mat, Mat, Mat);
379: PETSC_INTERN PetscErrorCode MatEqual_SeqAIJ(Mat, Mat, PetscBool *);
380: PETSC_INTERN PetscErrorCode MatFDColoringCreate_SeqXAIJ(Mat, ISColoring, MatFDColoring);
381: PETSC_INTERN PetscErrorCode MatFDColoringSetUp_SeqXAIJ(Mat, ISColoring, MatFDColoring);
382: PETSC_INTERN PetscErrorCode MatFDColoringSetUpBlocked_AIJ_Private(Mat, MatFDColoring, PetscInt);
383: PETSC_INTERN PetscErrorCode MatLoad_AIJ_HDF5(Mat, PetscViewer);
384: PETSC_INTERN PetscErrorCode MatLoad_SeqAIJ_Binary(Mat, PetscViewer);
385: PETSC_INTERN PetscErrorCode MatLoad_SeqAIJ(Mat, PetscViewer);
387: #if PetscDefined(HAVE_HYPRE)
388: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_Transpose_AIJ_AIJ(Mat);
389: #endif
390: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ(Mat);
392: PETSC_INTERN PetscErrorCode MatProductSymbolic_PtAP_SeqAIJ_SeqAIJ(Mat);
393: PETSC_INTERN PetscErrorCode MatProductSymbolic_RARt_SeqAIJ_SeqAIJ(Mat);
395: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
396: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Sorted(Mat, Mat, PetscReal, Mat);
397: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat, Mat, PetscReal, Mat);
398: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat, Mat, PetscReal, Mat);
399: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat, Mat, PetscReal, Mat);
400: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat, Mat, PetscReal, Mat);
401: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat, Mat, PetscReal, Mat);
402: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_RowMerge(Mat, Mat, PetscReal, Mat);
403: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat, Mat, PetscReal, Mat);
404: #if PetscDefined(HAVE_HYPRE)
405: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_AIJ_AIJ_wHYPRE(Mat, Mat, PetscReal, Mat);
406: #endif
408: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
409: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted(Mat, Mat, Mat);
411: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat, Mat, Mat);
412: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat, Mat, Mat);
414: PETSC_INTERN PetscErrorCode MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy(Mat, Mat, PetscReal, Mat);
415: PETSC_INTERN PetscErrorCode MatPtAPNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
416: PETSC_INTERN PetscErrorCode MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy(Mat, Mat, Mat);
418: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
419: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ_matmattransposemult(Mat, Mat, PetscReal, Mat);
420: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ_colorrart(Mat, Mat, PetscReal, Mat);
421: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
422: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ_matmattransposemult(Mat, Mat, Mat);
423: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ_colorrart(Mat, Mat, Mat);
425: PETSC_INTERN PetscErrorCode MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
426: PETSC_INTERN PetscErrorCode MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
427: PETSC_INTERN PetscErrorCode MatProductCtxDestroy_SeqAIJ_MatTransMatMult(PetscCtxRt);
429: PETSC_INTERN PetscErrorCode MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
430: PETSC_INTERN PetscErrorCode MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
431: PETSC_INTERN PetscErrorCode MatTransposeColoringCreate_SeqAIJ(Mat, ISColoring, MatTransposeColoring);
432: PETSC_INTERN PetscErrorCode MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring, Mat, Mat);
433: PETSC_INTERN PetscErrorCode MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring, Mat, Mat);
435: PETSC_INTERN PetscErrorCode MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ(Mat, Mat, Mat, PetscReal, Mat);
436: PETSC_INTERN PetscErrorCode MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ(Mat, Mat, Mat, Mat);
438: PETSC_INTERN PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat, PetscInt, PetscInt, PetscRandom);
439: PETSC_INTERN PetscErrorCode MatSetValues_SeqAIJ(Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[], const PetscScalar[], InsertMode);
440: PETSC_INTERN PetscErrorCode MatGetRow_SeqAIJ(Mat, PetscInt, PetscInt *, PetscInt **, PetscScalar **);
441: PETSC_INTERN PetscErrorCode MatRestoreRow_SeqAIJ(Mat, PetscInt, PetscInt *, PetscInt **, PetscScalar **);
442: PETSC_INTERN PetscErrorCode MatScale_SeqAIJ(Mat, PetscScalar);
443: PETSC_INTERN PetscErrorCode MatDiagonalScale_SeqAIJ(Mat, Vec, Vec);
444: PETSC_INTERN PetscErrorCode MatDiagonalSet_SeqAIJ(Mat, Vec, InsertMode);
445: PETSC_INTERN PetscErrorCode MatAXPY_SeqAIJ(Mat, PetscScalar, Mat, MatStructure);
446: PETSC_INTERN PetscErrorCode MatGetRowIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
447: PETSC_INTERN PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
448: PETSC_INTERN PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
449: PETSC_INTERN PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
450: PETSC_INTERN PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscInt *[], PetscBool *);
451: PETSC_INTERN PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscInt *[], PetscBool *);
452: PETSC_INTERN PetscErrorCode MatDestroy_SeqAIJ(Mat);
453: PETSC_INTERN PetscErrorCode MatView_SeqAIJ(Mat, PetscViewer);
455: PETSC_INTERN PetscErrorCode MatSeqAIJCheckInode(Mat);
456: PETSC_INTERN PetscErrorCode MatSeqAIJCheckInode_FactorLU(Mat);
458: PETSC_INTERN PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat, Mat, PetscInt *);
460: #if PetscDefined(HAVE_MATLAB)
461: PETSC_EXTERN PetscErrorCode MatlabEnginePut_SeqAIJ(PetscObject, void *);
462: PETSC_EXTERN PetscErrorCode MatlabEngineGet_SeqAIJ(PetscObject, void *);
463: #endif
464: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqSBAIJ(Mat, MatType, MatReuse, Mat *);
465: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqBAIJ(Mat, MatType, MatReuse, Mat *);
466: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat, MatType, MatReuse, Mat *);
467: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
468: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
469: #if PetscDefined(HAVE_SCALAPACK)
470: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
471: #endif
472: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
473: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJPERM(Mat, MatType, MatReuse, Mat *);
474: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJSELL(Mat, MatType, MatReuse, Mat *);
475: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJMKL(Mat, MatType, MatReuse, Mat *);
476: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJViennaCL(Mat, MatType, MatReuse, Mat *);
477: PETSC_INTERN PetscErrorCode MatReorderForNonzeroDiagonal_SeqAIJ(Mat, PetscReal, IS, IS);
478: PETSC_INTERN PetscErrorCode MatRARt_SeqAIJ_SeqAIJ(Mat, Mat, MatReuse, PetscReal, Mat *);
479: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat);
480: PETSC_INTERN PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat, MatAssemblyType);
481: PETSC_INTERN PetscErrorCode MatZeroEntries_SeqAIJ(Mat);
483: PETSC_INTERN PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt, const PetscInt *, const PetscInt *, const PetscInt *, const PetscInt *, PetscInt *);
484: PETSC_INTERN PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm, Mat, PetscInt, MatReuse, Mat *);
485: PETSC_INTERN PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm, Mat, PetscInt, MatReuse, Mat *);
487: PETSC_INTERN PetscErrorCode MatSetSeqMat_SeqAIJ(Mat, IS, IS, MatStructure, Mat);
488: PETSC_INTERN PetscErrorCode MatEliminateZeros_SeqAIJ(Mat, PetscBool);
489: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *);
490: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat);
491: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_Dummy(Mat);
492: PETSC_INTERN PetscErrorCode MatDestroySubMatrices_Dummy(PetscInt, Mat *[]);
493: PETSC_INTERN PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat, IS, IS, PetscInt, MatReuse, Mat *);
495: PETSC_INTERN PetscErrorCode MatSetSeqAIJWithArrays_private(MPI_Comm, PetscInt, PetscInt, PetscInt[], PetscInt[], PetscScalar[], MatType, Mat);
497: PETSC_INTERN PetscErrorCode MatResetPreallocation_SeqAIJ_Private(Mat A, PetscBool *memoryreset);
499: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat, ISLocalToGlobalMapping *);
501: /*
502: PetscSparseDenseMinusDot - The inner kernel of triangular solves and Gauss-Siedel smoothing. \sum_i xv[i] * r[xi[i]] for CSR storage
504: Input Parameters:
505: + nnz - the number of entries
506: . r - the array of vector values
507: . xv - the matrix values for the row
508: - xi - the column indices of the nonzeros in the row
510: Output Parameter:
511: . sum - negative the sum of results
513: PETSc compile flags:
514: + PETSC_KERNEL_USE_UNROLL_4
515: - PETSC_KERNEL_USE_UNROLL_2
517: Developer Note:
518: The macro changes sum but not other parameters
520: .seealso: `PetscSparseDensePlusDot()`
521: */
522: #if PetscDefined(KERNEL_USE_UNROLL_4)
523: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
524: do { \
525: if (nnz > 0) { \
526: PetscInt nnz2 = nnz, rem = nnz & 0x3; \
527: switch (rem) { \
528: case 3: \
529: sum -= *xv++ * r[*xi++]; \
530: case 2: \
531: sum -= *xv++ * r[*xi++]; \
532: case 1: \
533: sum -= *xv++ * r[*xi++]; \
534: nnz2 -= rem; \
535: } \
536: while (nnz2 > 0) { \
537: sum -= xv[0] * r[xi[0]] + xv[1] * r[xi[1]] + xv[2] * r[xi[2]] + xv[3] * r[xi[3]]; \
538: xv += 4; \
539: xi += 4; \
540: nnz2 -= 4; \
541: } \
542: xv -= nnz; \
543: xi -= nnz; \
544: } \
545: } while (0)
547: #elif PetscDefined(KERNEL_USE_UNROLL_2)
548: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
549: do { \
550: PetscInt __i, __i1, __i2; \
551: for (__i = 0; __i < nnz - 1; __i += 2) { \
552: __i1 = xi[__i]; \
553: __i2 = xi[__i + 1]; \
554: sum -= (xv[__i] * r[__i1] + xv[__i + 1] * r[__i2]); \
555: } \
556: if (nnz & 0x1) sum -= xv[__i] * r[xi[__i]]; \
557: } while (0)
559: #else
560: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
561: do { \
562: PetscInt __i; \
563: for (__i = 0; __i < nnz; __i++) sum -= xv[__i] * r[xi[__i]]; \
564: } while (0)
565: #endif
567: /*
568: PetscSparseDensePlusDot - The inner kernel of matrix-vector product \sum_i xv[i] * r[xi[i]] for CSR storage
570: Input Parameters:
571: + nnz - the number of entries
572: . r - the array of vector values
573: . xv - the matrix values for the row
574: - xi - the column indices of the nonzeros in the row
576: Output Parameter:
577: . sum - the sum of results
579: PETSc compile flags:
580: + PETSC_KERNEL_USE_UNROLL_4
581: - PETSC_KERNEL_USE_UNROLL_2
583: Developer Note:
584: The macro changes sum but not other parameters
586: .seealso: `PetscSparseDenseMinusDot()`
587: */
588: #if PetscDefined(KERNEL_USE_UNROLL_4)
589: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
590: do { \
591: if (nnz > 0) { \
592: PetscInt nnz2 = nnz, rem = nnz & 0x3; \
593: switch (rem) { \
594: case 3: \
595: sum += *xv++ * r[*xi++]; \
596: case 2: \
597: sum += *xv++ * r[*xi++]; \
598: case 1: \
599: sum += *xv++ * r[*xi++]; \
600: nnz2 -= rem; \
601: } \
602: while (nnz2 > 0) { \
603: sum += xv[0] * r[xi[0]] + xv[1] * r[xi[1]] + xv[2] * r[xi[2]] + xv[3] * r[xi[3]]; \
604: xv += 4; \
605: xi += 4; \
606: nnz2 -= 4; \
607: } \
608: xv -= nnz; \
609: xi -= nnz; \
610: } \
611: } while (0)
613: #elif PetscDefined(KERNEL_USE_UNROLL_2)
614: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
615: do { \
616: PetscInt __i, __i1, __i2; \
617: for (__i = 0; __i < nnz - 1; __i += 2) { \
618: __i1 = xi[__i]; \
619: __i2 = xi[__i + 1]; \
620: sum += (xv[__i] * r[__i1] + xv[__i + 1] * r[__i2]); \
621: } \
622: if (nnz & 0x1) sum += xv[__i] * r[xi[__i]]; \
623: } while (0)
625: #elif !(defined(__GNUC__) && defined(_OPENMP)) && PetscDefined(USE_AVX512_KERNELS) && PetscDefined(HAVE_IMMINTRIN_H) && defined(__AVX512F__) && PetscDefined(USE_REAL_DOUBLE) && !PetscDefined(USE_COMPLEX) && !PetscDefined(USE_64BIT_INDICES) && !PetscDefined(SKIP_IMMINTRIN_H_CUDAWORKAROUND)
626: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) PetscSparseDensePlusDot_AVX512_Private(&(sum), (r), (xv), (xi), (nnz))
628: #else
629: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
630: do { \
631: PetscInt __i; \
632: for (__i = 0; __i < nnz; __i++) sum += xv[__i] * r[xi[__i]]; \
633: } while (0)
634: #endif
636: #if PetscDefined(USE_AVX512_KERNELS) && PetscDefined(HAVE_IMMINTRIN_H) && defined(__AVX512F__) && PetscDefined(USE_REAL_DOUBLE) && !PetscDefined(USE_COMPLEX) && !PetscDefined(USE_64BIT_INDICES) && !PetscDefined(SKIP_IMMINTRIN_H_CUDAWORKAROUND)
637: #include <immintrin.h>
638: #if !defined(_MM_SCALE_8)
639: #define _MM_SCALE_8 8
640: #endif
642: static inline void PetscSparseDensePlusDot_AVX512_Private(PetscScalar *sum, const PetscScalar *x, const MatScalar *aa, const PetscInt *aj, PetscInt n)
643: {
644: __m512d vec_x, vec_y, vec_vals;
645: __m256i vec_idx;
646: PetscInt j;
648: vec_y = _mm512_setzero_pd();
649: for (j = 0; j < (n >> 3); j++) {
650: vec_idx = _mm256_loadu_si256((__m256i const *)aj);
651: vec_vals = _mm512_loadu_pd(aa);
652: vec_x = _mm512_i32gather_pd(vec_idx, x, _MM_SCALE_8);
653: vec_y = _mm512_fmadd_pd(vec_x, vec_vals, vec_y);
654: aj += 8;
655: aa += 8;
656: }
657: #if defined(__AVX512VL__)
658: /* masked load requires avx512vl, which is not supported by KNL */
659: if (n & 0x07) {
660: __mmask8 mask;
661: mask = (__mmask8)(0xff >> (8 - (n & 0x07)));
662: vec_idx = _mm256_mask_loadu_epi32(vec_idx, mask, aj);
663: vec_vals = _mm512_mask_loadu_pd(vec_vals, mask, aa);
664: vec_x = _mm512_mask_i32gather_pd(vec_x, mask, vec_idx, x, _MM_SCALE_8);
665: vec_y = _mm512_mask3_fmadd_pd(vec_x, vec_vals, vec_y, mask);
666: }
667: *sum += _mm512_reduce_add_pd(vec_y);
668: #else
669: *sum += _mm512_reduce_add_pd(vec_y);
670: for (j = 0; j < (n & 0x07); j++) *sum += aa[j] * x[aj[j]];
671: #endif
672: }
673: #endif
675: /*
676: PetscSparseDenseMaxDot - The inner kernel of a modified matrix-vector product \max_i xv[i] * r[xi[i]] for CSR storage
678: Input Parameters:
679: + nnz - the number of entries
680: . r - the array of vector values
681: . xv - the matrix values for the row
682: - xi - the column indices of the nonzeros in the row
684: Output Parameter:
685: . max - the max of results
687: .seealso: `PetscSparseDensePlusDot()`, `PetscSparseDenseMinusDot()`
688: */
689: #define PetscSparseDenseMaxDot(max, r, xv, xi, nnz) \
690: do { \
691: for (PetscInt __i = 0; __i < (nnz); __i++) max = PetscMax(PetscRealPart(max), PetscRealPart((xv)[__i] * (r)[(xi)[__i]])); \
692: } while (0)
694: /*
695: Add column indices into table for counting the max nonzeros of merged rows
696: */
697: #define MatRowMergeMax_SeqAIJ(mat, nrows, ta) \
698: do { \
699: if (mat) { \
700: for (PetscInt _row = 0; _row < (nrows); _row++) { \
701: const PetscInt _nz = (mat)->i[_row + 1] - (mat)->i[_row]; \
702: for (PetscInt _j = 0; _j < _nz; _j++) { \
703: PetscInt *_col = _j + (mat)->j + (mat)->i[_row]; \
704: PetscCall(PetscHMapISet((ta), *_col + 1, 1)); \
705: } \
706: } \
707: } \
708: } while (0)
710: /*
711: Add column indices into table for counting the nonzeros of merged rows
712: */
713: #define MatMergeRows_SeqAIJ(mat, nrows, rows, ta) \
714: do { \
715: for (PetscInt _i = 0; _i < (nrows); _i++) { \
716: const PetscInt _row = (rows)[_i]; \
717: const PetscInt _nz = (mat)->i[_row + 1] - (mat)->i[_row]; \
718: for (PetscInt _j = 0; _j < _nz; _j++) { \
719: PetscInt *_col = _j + (mat)->j + (mat)->i[_row]; \
720: PetscCall(PetscHMapISetWithMode((ta), *_col + 1, 1, INSERT_VALUES)); \
721: } \
722: } \
723: } while (0)