Actual source code: aij.h
2: #ifndef __AIJ_H
5: #include <petsc/private/matimpl.h>
6: #include <petscctable.h>
8: /* Operations provided by MATSEQAIJ and its subclasses */
9: typedef struct {
10: PetscErrorCode (*getarray)(Mat, PetscScalar **);
11: PetscErrorCode (*restorearray)(Mat, PetscScalar **);
12: PetscErrorCode (*getarrayread)(Mat, const PetscScalar **);
13: PetscErrorCode (*restorearrayread)(Mat, const PetscScalar **);
14: PetscErrorCode (*getarraywrite)(Mat, PetscScalar **);
15: PetscErrorCode (*restorearraywrite)(Mat, PetscScalar **);
16: PetscErrorCode (*getcsrandmemtype)(Mat, const PetscInt **, const PetscInt **, PetscScalar **, PetscMemType *);
17: } Mat_SeqAIJOps;
19: /*
20: Struct header shared by SeqAIJ, SeqBAIJ and SeqSBAIJ matrix formats
21: */
22: #define SEQAIJHEADER(datatype) \
23: PetscBool roworiented; /* if true, row-oriented input, default */ \
24: PetscInt nonew; /* 1 don't add new nonzeros, -1 generate error on new */ \
25: PetscInt nounused; /* -1 generate error on unused space */ \
26: PetscBool singlemalloc; /* if true a, i, and j have been obtained with one big malloc */ \
27: PetscInt maxnz; /* allocated nonzeros */ \
28: PetscInt *imax; /* maximum space allocated for each row */ \
29: PetscInt *ilen; /* actual length of each row */ \
30: PetscInt *ipre; /* space preallocated for each row by user */ \
31: PetscBool free_imax_ilen; \
32: PetscInt reallocs; /* number of mallocs done during MatSetValues() \
33: as more values are set than were prealloced */ \
34: PetscInt rmax; /* max nonzeros in any row */ \
35: PetscBool keepnonzeropattern; /* keeps matrix structure same in calls to MatZeroRows()*/ \
36: PetscBool ignorezeroentries; \
37: PetscBool free_ij; /* free the column indices j and row offsets i when the matrix is destroyed */ \
38: PetscBool free_a; /* free the numerical values when matrix is destroy */ \
39: Mat_CompressedRow compressedrow; /* use compressed row format */ \
40: PetscInt nz; /* nonzeros */ \
41: PetscInt *i; /* pointer to beginning of each row */ \
42: PetscInt *j; /* column values: j + i[k] - 1 is start of row k */ \
43: PetscInt *diag; /* pointers to diagonal elements */ \
44: PetscInt nonzerorowcnt; /* how many rows have nonzero entries */ \
45: PetscBool free_diag; \
46: datatype *a; /* nonzero elements */ \
47: PetscScalar *solve_work; /* work space used in MatSolve */ \
48: IS row, col, icol; /* index sets, used for reorderings */ \
49: PetscBool pivotinblocks; /* pivot inside factorization of each diagonal block */ \
50: Mat parent; /* set if this matrix was formed with MatDuplicate(...,MAT_SHARE_NONZERO_PATTERN,....); \
51: means that this shares some data structures with the parent including diag, ilen, imax, i, j */ \
52: Mat_SubSppt *submatis1; /* used by MatCreateSubMatrices_MPIXAIJ_Local */ \
53: Mat_SeqAIJOps ops[1] /* operations for SeqAIJ and its subclasses */
55: typedef struct {
56: MatTransposeColoring matcoloring;
57: Mat Bt_den; /* dense matrix of B^T */
58: Mat ABt_den; /* dense matrix of A*B^T */
59: PetscBool usecoloring;
60: } Mat_MatMatTransMult;
62: typedef struct { /* used by MatTransposeMatMult() */
63: Mat At; /* transpose of the first matrix */
64: Mat mA; /* maij matrix of A */
65: Vec bt, ct; /* vectors to hold locally transposed arrays of B and C */
66: /* used by PtAP */
67: void *data;
68: PetscErrorCode (*destroy)(void *);
69: } Mat_MatTransMatMult;
71: typedef struct {
72: PetscInt *api, *apj; /* symbolic structure of A*P */
73: PetscScalar *apa; /* temporary array for storing one row of A*P */
74: } Mat_AP;
76: typedef struct {
77: MatTransposeColoring matcoloring;
78: Mat Rt; /* sparse or dense matrix of R^T */
79: Mat RARt; /* dense matrix of R*A*R^T */
80: Mat ARt; /* A*R^T used for the case -matrart_color_art */
81: MatScalar *work; /* work array to store columns of A*R^T used in MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqDense() */
82: /* free intermediate products needed for PtAP */
83: void *data;
84: PetscErrorCode (*destroy)(void *);
85: } Mat_RARt;
87: typedef struct {
88: Mat BC; /* temp matrix for storing B*C */
89: } Mat_MatMatMatMult;
91: /*
92: MATSEQAIJ format - Compressed row storage (also called Yale sparse matrix
93: format) or compressed sparse row (CSR). The i[] and j[] arrays start at 0. For example,
94: j[i[k]+p] is the pth column in row k. Note that the diagonal
95: matrix elements are stored with the rest of the nonzeros (not separately).
96: */
98: /* Info about i-nodes (identical nodes) helper class for SeqAIJ */
99: typedef struct {
100: MatScalar *bdiag, *ibdiag, *ssor_work; /* diagonal blocks of matrix used for MatSOR_SeqAIJ_Inode() */
101: PetscInt bdiagsize; /* length of bdiag and ibdiag */
102: PetscBool ibdiagvalid; /* do ibdiag[] and bdiag[] contain the most recent values */
104: PetscBool use;
105: PetscInt node_count; /* number of inodes */
106: PetscInt *size; /* size of each inode */
107: PetscInt limit; /* inode limit */
108: PetscInt max_limit; /* maximum supported inode limit */
109: PetscBool checked; /* if inodes have been checked for */
110: PetscObjectState mat_nonzerostate; /* non-zero state when inodes were checked for */
111: } Mat_SeqAIJ_Inode;
113: PETSC_INTERN PetscErrorCode MatView_SeqAIJ_Inode(Mat, PetscViewer);
114: PETSC_INTERN PetscErrorCode MatAssemblyEnd_SeqAIJ_Inode(Mat, MatAssemblyType);
115: PETSC_INTERN PetscErrorCode MatDestroy_SeqAIJ_Inode(Mat);
116: PETSC_INTERN PetscErrorCode MatCreate_SeqAIJ_Inode(Mat);
117: PETSC_INTERN PetscErrorCode MatSetOption_SeqAIJ_Inode(Mat, MatOption, PetscBool);
118: PETSC_INTERN PetscErrorCode MatDuplicate_SeqAIJ_Inode(Mat, MatDuplicateOption, Mat *);
119: PETSC_INTERN PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat, Mat, MatDuplicateOption, PetscBool);
120: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_Inode_inplace(Mat, Mat, const MatFactorInfo *);
121: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_Inode(Mat, Mat, const MatFactorInfo *);
122: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat, PetscScalar **);
123: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat, PetscScalar **);
125: typedef struct {
126: SEQAIJHEADER(MatScalar);
127: Mat_SeqAIJ_Inode inode;
128: MatScalar *saved_values; /* location for stashing nonzero values of matrix */
130: PetscScalar *idiag, *mdiag, *ssor_work; /* inverse of diagonal entries, diagonal values and workspace for Eisenstat trick */
131: PetscBool idiagvalid; /* current idiag[] and mdiag[] are valid */
132: PetscScalar *ibdiag; /* inverses of block diagonals */
133: PetscBool ibdiagvalid; /* inverses of block diagonals are valid. */
134: PetscBool diagonaldense; /* all entries along the diagonal have been set; i.e. no missing diagonal terms */
135: PetscScalar fshift, omega; /* last used omega and fshift */
137: /* MatSetValuesCOO() related fields on host */
138: PetscCount coo_n; /* Number of entries in MatSetPreallocationCOO() */
139: PetscCount Atot; /* Total number of valid (i.e., w/ non-negative indices) entries in the COO array */
140: PetscCount *jmap; /* perm[jmap[i]..jmap[i+1]) give indices of entries in v[] associated with i-th nonzero of the matrix */
141: PetscCount *perm; /* The permutation array in sorting (i,j) by row and then by col */
142: } Mat_SeqAIJ;
144: /*
145: Frees the a, i, and j arrays from the XAIJ (AIJ, BAIJ, and SBAIJ) matrix types
146: */
147: static inline PetscErrorCode MatSeqXAIJFreeAIJ(Mat AA, MatScalar **a, PetscInt **j, PetscInt **i)
148: {
149: Mat_SeqAIJ *A = (Mat_SeqAIJ *)AA->data;
150: if (A->singlemalloc) {
151: PetscFree3(*a, *j, *i);
152: } else {
153: if (A->free_a) PetscFree(*a);
154: if (A->free_ij) PetscFree(*j);
155: if (A->free_ij) PetscFree(*i);
156: }
157: return 0;
158: }
159: /*
160: Allocates larger a, i, and j arrays for the XAIJ (AIJ, BAIJ, and SBAIJ) matrix types
161: This is a macro because it takes the datatype as an argument which can be either a Mat or a MatScalar
162: */
163: #define MatSeqXAIJReallocateAIJ(Amat, AM, BS2, NROW, ROW, COL, RMAX, AA, AI, AJ, RP, AP, AIMAX, NONEW, datatype) \
164: if (NROW >= RMAX) { \
165: Mat_SeqAIJ *Ain = (Mat_SeqAIJ *)Amat->data; \
166: /* there is no extra room in row, therefore enlarge */ \
167: PetscInt CHUNKSIZE = 15, new_nz = AI[AM] + CHUNKSIZE, len, *new_i = NULL, *new_j = NULL; \
168: datatype *new_a; \
169: \
171: /* malloc new storage space */ \
172: PetscMalloc3(BS2 *new_nz, &new_a, new_nz, &new_j, AM + 1, &new_i); \
173: \
174: /* copy over old data into new slots */ \
175: for (ii = 0; ii < ROW + 1; ii++) new_i[ii] = AI[ii]; \
176: for (ii = ROW + 1; ii < AM + 1; ii++) new_i[ii] = AI[ii] + CHUNKSIZE; \
177: PetscArraycpy(new_j, AJ, AI[ROW] + NROW); \
178: len = (new_nz - CHUNKSIZE - AI[ROW] - NROW); \
179: PetscArraycpy(new_j + AI[ROW] + NROW + CHUNKSIZE, AJ + AI[ROW] + NROW, len); \
180: PetscArraycpy(new_a, AA, BS2 *(AI[ROW] + NROW)); \
181: PetscArrayzero(new_a + BS2 * (AI[ROW] + NROW), BS2 * CHUNKSIZE); \
182: PetscArraycpy(new_a + BS2 * (AI[ROW] + NROW + CHUNKSIZE), AA + BS2 * (AI[ROW] + NROW), BS2 * len); \
183: /* free up old matrix storage */ \
184: MatSeqXAIJFreeAIJ(A, &Ain->a, &Ain->j, &Ain->i); \
185: AA = new_a; \
186: Ain->a = (MatScalar *)new_a; \
187: AI = Ain->i = new_i; \
188: AJ = Ain->j = new_j; \
189: Ain->singlemalloc = PETSC_TRUE; \
190: \
191: RP = AJ + AI[ROW]; \
192: AP = AA + BS2 * AI[ROW]; \
193: RMAX = AIMAX[ROW] = AIMAX[ROW] + CHUNKSIZE; \
194: Ain->maxnz += BS2 * CHUNKSIZE; \
195: Ain->reallocs++; \
196: }
198: #define MatSeqXAIJReallocateAIJ_structure_only(Amat, AM, BS2, NROW, ROW, COL, RMAX, AI, AJ, RP, AIMAX, NONEW, datatype) \
199: if (NROW >= RMAX) { \
200: Mat_SeqAIJ *Ain = (Mat_SeqAIJ *)Amat->data; \
201: /* there is no extra room in row, therefore enlarge */ \
202: PetscInt CHUNKSIZE = 15, new_nz = AI[AM] + CHUNKSIZE, len, *new_i = NULL, *new_j = NULL; \
203: \
205: /* malloc new storage space */ \
206: PetscMalloc1(new_nz, &new_j); \
207: PetscMalloc1(AM + 1, &new_i); \
208: \
209: /* copy over old data into new slots */ \
210: for (ii = 0; ii < ROW + 1; ii++) new_i[ii] = AI[ii]; \
211: for (ii = ROW + 1; ii < AM + 1; ii++) new_i[ii] = AI[ii] + CHUNKSIZE; \
212: PetscArraycpy(new_j, AJ, AI[ROW] + NROW); \
213: len = (new_nz - CHUNKSIZE - AI[ROW] - NROW); \
214: PetscArraycpy(new_j + AI[ROW] + NROW + CHUNKSIZE, AJ + AI[ROW] + NROW, len); \
215: \
216: /* free up old matrix storage */ \
217: MatSeqXAIJFreeAIJ(A, &Ain->a, &Ain->j, &Ain->i); \
218: Ain->a = NULL; \
219: AI = Ain->i = new_i; \
220: AJ = Ain->j = new_j; \
221: Ain->singlemalloc = PETSC_FALSE; \
222: Ain->free_a = PETSC_FALSE; \
223: \
224: RP = AJ + AI[ROW]; \
225: RMAX = AIMAX[ROW] = AIMAX[ROW] + CHUNKSIZE; \
226: Ain->maxnz += BS2 * CHUNKSIZE; \
227: Ain->reallocs++; \
228: }
230: PETSC_INTERN PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat, PetscInt, const PetscInt *);
231: PETSC_INTERN PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat, PetscCount, PetscInt[], PetscInt[]);
232: PETSC_INTERN PetscErrorCode MatResetPreallocationCOO_SeqAIJ(Mat);
234: PETSC_INTERN PetscErrorCode MatILUFactorSymbolic_SeqAIJ_inplace(Mat, Mat, IS, IS, const MatFactorInfo *);
235: PETSC_INTERN PetscErrorCode MatILUFactorSymbolic_SeqAIJ(Mat, Mat, IS, IS, const MatFactorInfo *);
236: PETSC_INTERN PetscErrorCode MatILUFactorSymbolic_SeqAIJ_ilu0(Mat, Mat, IS, IS, const MatFactorInfo *);
238: PETSC_INTERN PetscErrorCode MatICCFactorSymbolic_SeqAIJ_inplace(Mat, Mat, IS, const MatFactorInfo *);
239: PETSC_INTERN PetscErrorCode MatICCFactorSymbolic_SeqAIJ(Mat, Mat, IS, const MatFactorInfo *);
240: PETSC_INTERN PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJ_inplace(Mat, Mat, IS, const MatFactorInfo *);
241: PETSC_INTERN PetscErrorCode MatCholeskyFactorSymbolic_SeqAIJ(Mat, Mat, IS, const MatFactorInfo *);
242: PETSC_INTERN PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ_inplace(Mat, Mat, const MatFactorInfo *);
243: PETSC_INTERN PetscErrorCode MatCholeskyFactorNumeric_SeqAIJ(Mat, Mat, const MatFactorInfo *);
244: PETSC_INTERN PetscErrorCode MatDuplicate_SeqAIJ(Mat, MatDuplicateOption, Mat *);
245: PETSC_INTERN PetscErrorCode MatCopy_SeqAIJ(Mat, Mat, MatStructure);
246: PETSC_INTERN PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat, PetscBool *, PetscInt *);
247: PETSC_INTERN PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat);
248: PETSC_INTERN PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat, PetscInt *, PetscInt **);
250: PETSC_INTERN PetscErrorCode MatMult_SeqAIJ(Mat, Vec, Vec);
251: PETSC_INTERN PetscErrorCode MatMult_SeqAIJ_Inode(Mat, Vec, Vec);
252: PETSC_INTERN PetscErrorCode MatMultAdd_SeqAIJ(Mat, Vec, Vec, Vec);
253: PETSC_INTERN PetscErrorCode MatMultAdd_SeqAIJ_Inode(Mat, Vec, Vec, Vec);
254: PETSC_INTERN PetscErrorCode MatMultTranspose_SeqAIJ(Mat, Vec, Vec);
255: PETSC_INTERN PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat, Vec, Vec, Vec);
256: PETSC_INTERN PetscErrorCode MatSOR_SeqAIJ(Mat, Vec, PetscReal, MatSORType, PetscReal, PetscInt, PetscInt, Vec);
257: PETSC_INTERN PetscErrorCode MatSOR_SeqAIJ_Inode(Mat, Vec, PetscReal, MatSORType, PetscReal, PetscInt, PetscInt, Vec);
259: PETSC_INTERN PetscErrorCode MatSetOption_SeqAIJ(Mat, MatOption, PetscBool);
261: PETSC_INTERN PetscErrorCode MatGetSymbolicTranspose_SeqAIJ(Mat, PetscInt *[], PetscInt *[]);
262: PETSC_INTERN PetscErrorCode MatRestoreSymbolicTranspose_SeqAIJ(Mat, PetscInt *[], PetscInt *[]);
263: PETSC_INTERN PetscErrorCode MatGetSymbolicTransposeReduced_SeqAIJ(Mat, PetscInt, PetscInt, PetscInt *[], PetscInt *[]);
264: PETSC_INTERN PetscErrorCode MatTransposeSymbolic_SeqAIJ(Mat, Mat *);
265: PETSC_INTERN PetscErrorCode MatTranspose_SeqAIJ(Mat, MatReuse, Mat *);
267: PETSC_INTERN PetscErrorCode MatToSymmetricIJ_SeqAIJ(PetscInt, PetscInt *, PetscInt *, PetscBool, PetscInt, PetscInt, PetscInt **, PetscInt **);
268: PETSC_INTERN PetscErrorCode MatLUFactorSymbolic_SeqAIJ_inplace(Mat, Mat, IS, IS, const MatFactorInfo *);
269: PETSC_INTERN PetscErrorCode MatLUFactorSymbolic_SeqAIJ(Mat, Mat, IS, IS, const MatFactorInfo *);
270: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_inplace(Mat, Mat, const MatFactorInfo *);
271: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ(Mat, Mat, const MatFactorInfo *);
272: PETSC_INTERN PetscErrorCode MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(Mat, Mat, const MatFactorInfo *);
273: PETSC_INTERN PetscErrorCode MatLUFactor_SeqAIJ(Mat, IS, IS, const MatFactorInfo *);
274: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_inplace(Mat, Vec, Vec);
275: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ(Mat, Vec, Vec);
276: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_Inode_inplace(Mat, Vec, Vec);
277: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_Inode(Mat, Vec, Vec);
278: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_NaturalOrdering_inplace(Mat, Vec, Vec);
279: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_NaturalOrdering(Mat, Vec, Vec);
280: PETSC_INTERN PetscErrorCode MatSolve_SeqAIJ_InplaceWithPerm(Mat, Vec, Vec);
281: PETSC_INTERN PetscErrorCode MatSolveAdd_SeqAIJ_inplace(Mat, Vec, Vec, Vec);
282: PETSC_INTERN PetscErrorCode MatSolveAdd_SeqAIJ(Mat, Vec, Vec, Vec);
283: PETSC_INTERN PetscErrorCode MatSolveTranspose_SeqAIJ_inplace(Mat, Vec, Vec);
284: PETSC_INTERN PetscErrorCode MatSolveTranspose_SeqAIJ(Mat, Vec, Vec);
285: PETSC_INTERN PetscErrorCode MatSolveTransposeAdd_SeqAIJ_inplace(Mat, Vec, Vec, Vec);
286: PETSC_INTERN PetscErrorCode MatSolveTransposeAdd_SeqAIJ(Mat, Vec, Vec, Vec);
287: PETSC_INTERN PetscErrorCode MatMatSolve_SeqAIJ_inplace(Mat, Mat, Mat);
288: PETSC_INTERN PetscErrorCode MatMatSolve_SeqAIJ(Mat, Mat, Mat);
289: PETSC_INTERN PetscErrorCode MatEqual_SeqAIJ(Mat, Mat, PetscBool *);
290: PETSC_INTERN PetscErrorCode MatFDColoringCreate_SeqXAIJ(Mat, ISColoring, MatFDColoring);
291: PETSC_INTERN PetscErrorCode MatFDColoringSetUp_SeqXAIJ(Mat, ISColoring, MatFDColoring);
292: PETSC_INTERN PetscErrorCode MatFDColoringSetUpBlocked_AIJ_Private(Mat, MatFDColoring, PetscInt);
293: PETSC_INTERN PetscErrorCode MatLoad_AIJ_HDF5(Mat, PetscViewer);
294: PETSC_INTERN PetscErrorCode MatLoad_SeqAIJ_Binary(Mat, PetscViewer);
295: PETSC_INTERN PetscErrorCode MatLoad_SeqAIJ(Mat, PetscViewer);
296: PETSC_INTERN PetscErrorCode RegisterApplyPtAPRoutines_Private(Mat);
298: #if defined(PETSC_HAVE_HYPRE)
299: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_Transpose_AIJ_AIJ(Mat);
300: #endif
301: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_SeqAIJ(Mat);
303: PETSC_INTERN PetscErrorCode MatProductSymbolic_SeqAIJ_SeqAIJ(Mat);
304: PETSC_INTERN PetscErrorCode MatProductSymbolic_PtAP_SeqAIJ_SeqAIJ(Mat);
305: PETSC_INTERN PetscErrorCode MatProductSymbolic_RARt_SeqAIJ_SeqAIJ(Mat);
307: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
308: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Sorted(Mat, Mat, PetscReal, Mat);
309: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat, Mat, PetscReal, Mat);
310: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable(Mat, Mat, PetscReal, Mat);
311: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Scalable_fast(Mat, Mat, PetscReal, Mat);
312: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_Heap(Mat, Mat, PetscReal, Mat);
313: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_BTHeap(Mat, Mat, PetscReal, Mat);
314: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_RowMerge(Mat, Mat, PetscReal, Mat);
315: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_SeqAIJ_SeqAIJ_LLCondensed(Mat, Mat, PetscReal, Mat);
316: #if defined(PETSC_HAVE_HYPRE)
317: PETSC_INTERN PetscErrorCode MatMatMultSymbolic_AIJ_AIJ_wHYPRE(Mat, Mat, PetscReal, Mat);
318: #endif
320: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
321: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted(Mat, Mat, Mat);
323: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat, Mat, Mat);
324: PETSC_INTERN PetscErrorCode MatMatMultNumeric_SeqAIJ_SeqAIJ_Scalable(Mat, Mat, Mat);
326: PETSC_INTERN PetscErrorCode MatPtAPSymbolic_SeqAIJ_SeqAIJ_SparseAxpy(Mat, Mat, PetscReal, Mat);
327: PETSC_INTERN PetscErrorCode MatPtAPNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
328: PETSC_INTERN PetscErrorCode MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy(Mat, Mat, Mat);
330: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
331: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ_matmattransposemult(Mat, Mat, PetscReal, Mat);
332: PETSC_INTERN PetscErrorCode MatRARtSymbolic_SeqAIJ_SeqAIJ_colorrart(Mat, Mat, PetscReal, Mat);
333: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
334: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ_matmattransposemult(Mat, Mat, Mat);
335: PETSC_INTERN PetscErrorCode MatRARtNumeric_SeqAIJ_SeqAIJ_colorrart(Mat, Mat, Mat);
337: PETSC_INTERN PetscErrorCode MatTransposeMatMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
338: PETSC_INTERN PetscErrorCode MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
339: PETSC_INTERN PetscErrorCode MatDestroy_SeqAIJ_MatTransMatMult(void *);
341: PETSC_INTERN PetscErrorCode MatMatTransposeMultSymbolic_SeqAIJ_SeqAIJ(Mat, Mat, PetscReal, Mat);
342: PETSC_INTERN PetscErrorCode MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ(Mat, Mat, Mat);
343: PETSC_INTERN PetscErrorCode MatTransposeColoringCreate_SeqAIJ(Mat, ISColoring, MatTransposeColoring);
344: PETSC_INTERN PetscErrorCode MatTransColoringApplySpToDen_SeqAIJ(MatTransposeColoring, Mat, Mat);
345: PETSC_INTERN PetscErrorCode MatTransColoringApplyDenToSp_SeqAIJ(MatTransposeColoring, Mat, Mat);
347: PETSC_INTERN PetscErrorCode MatMatMatMultSymbolic_SeqAIJ_SeqAIJ_SeqAIJ(Mat, Mat, Mat, PetscReal, Mat);
348: PETSC_INTERN PetscErrorCode MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ(Mat, Mat, Mat, Mat);
350: PETSC_INTERN PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat, PetscInt, PetscInt, PetscRandom);
351: PETSC_INTERN PetscErrorCode MatSetValues_SeqAIJ(Mat, PetscInt, const PetscInt[], PetscInt, const PetscInt[], const PetscScalar[], InsertMode);
352: PETSC_INTERN PetscErrorCode MatGetRow_SeqAIJ(Mat, PetscInt, PetscInt *, PetscInt **, PetscScalar **);
353: PETSC_INTERN PetscErrorCode MatRestoreRow_SeqAIJ(Mat, PetscInt, PetscInt *, PetscInt **, PetscScalar **);
354: PETSC_INTERN PetscErrorCode MatScale_SeqAIJ(Mat, PetscScalar);
355: PETSC_INTERN PetscErrorCode MatDiagonalScale_SeqAIJ(Mat, Vec, Vec);
356: PETSC_INTERN PetscErrorCode MatDiagonalSet_SeqAIJ(Mat, Vec, InsertMode);
357: PETSC_INTERN PetscErrorCode MatAXPY_SeqAIJ(Mat, PetscScalar, Mat, MatStructure);
358: PETSC_INTERN PetscErrorCode MatGetRowIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
359: PETSC_INTERN PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
360: PETSC_INTERN PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
361: PETSC_INTERN PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscBool *);
362: PETSC_INTERN PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscInt *[], PetscBool *);
363: PETSC_INTERN PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat, PetscInt, PetscBool, PetscBool, PetscInt *, const PetscInt *[], const PetscInt *[], PetscInt *[], PetscBool *);
364: PETSC_INTERN PetscErrorCode MatDestroy_SeqAIJ(Mat);
365: PETSC_INTERN PetscErrorCode MatSetUp_SeqAIJ(Mat);
366: PETSC_INTERN PetscErrorCode MatView_SeqAIJ(Mat, PetscViewer);
368: PETSC_INTERN PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat);
369: PETSC_INTERN PetscErrorCode MatSeqAIJInvalidateDiagonal_Inode(Mat);
370: PETSC_INTERN PetscErrorCode MatSeqAIJCheckInode(Mat);
371: PETSC_INTERN PetscErrorCode MatSeqAIJCheckInode_FactorLU(Mat);
373: PETSC_INTERN PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat, Mat, PetscInt *);
375: #if defined(PETSC_HAVE_MATLAB)
376: PETSC_EXTERN PetscErrorCode MatlabEnginePut_SeqAIJ(PetscObject, void *);
377: PETSC_EXTERN PetscErrorCode MatlabEngineGet_SeqAIJ(PetscObject, void *);
378: #endif
379: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqSBAIJ(Mat, MatType, MatReuse, Mat *);
380: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqBAIJ(Mat, MatType, MatReuse, Mat *);
381: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqDense(Mat, MatType, MatReuse, Mat *);
382: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
383: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
384: #if defined(PETSC_HAVE_SCALAPACK)
385: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
386: #endif
387: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat, MatType, MatReuse, Mat *);
388: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJPERM(Mat, MatType, MatReuse, Mat *);
389: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJSELL(Mat, MatType, MatReuse, Mat *);
390: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJMKL(Mat, MatType, MatReuse, Mat *);
391: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJViennaCL(Mat, MatType, MatReuse, Mat *);
392: PETSC_INTERN PetscErrorCode MatReorderForNonzeroDiagonal_SeqAIJ(Mat, PetscReal, IS, IS);
393: PETSC_INTERN PetscErrorCode MatRARt_SeqAIJ_SeqAIJ(Mat, Mat, MatReuse, PetscReal, Mat *);
394: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat);
395: PETSC_INTERN PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat, MatAssemblyType);
396: PETSC_EXTERN PetscErrorCode MatZeroEntries_SeqAIJ(Mat);
398: PETSC_INTERN PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt, const PetscInt *, const PetscInt *, const PetscInt *, const PetscInt *, PetscInt *);
399: PETSC_INTERN PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm, Mat, PetscInt, MatReuse, Mat *);
400: PETSC_INTERN PetscErrorCode MatCreateMPIMatConcatenateSeqMat_MPIAIJ(MPI_Comm, Mat, PetscInt, MatReuse, Mat *);
402: PETSC_INTERN PetscErrorCode MatSetSeqMat_SeqAIJ(Mat, IS, IS, MatStructure, Mat);
403: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *);
404: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat);
405: PETSC_INTERN PetscErrorCode MatDestroySubMatrix_Dummy(Mat);
406: PETSC_INTERN PetscErrorCode MatDestroySubMatrices_Dummy(PetscInt, Mat *[]);
407: PETSC_INTERN PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat, IS, IS, PetscInt, MatReuse, Mat *);
409: PETSC_INTERN PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat, ISLocalToGlobalMapping *);
410: PETSC_INTERN PetscErrorCode MatSetSeqAIJWithArrays_private(MPI_Comm, PetscInt, PetscInt, PetscInt[], PetscInt[], PetscScalar[], MatType, Mat);
412: /*
413: PetscSparseDenseMinusDot - The inner kernel of triangular solves and Gauss-Siedel smoothing. \sum_i xv[i] * r[xi[i]] for CSR storage
415: Input Parameters:
416: + nnz - the number of entries
417: . r - the array of vector values
418: . xv - the matrix values for the row
419: - xi - the column indices of the nonzeros in the row
421: Output Parameter:
422: . sum - negative the sum of results
424: PETSc compile flags:
425: + PETSC_KERNEL_USE_UNROLL_4
426: - PETSC_KERNEL_USE_UNROLL_2
428: Developer Note:
429: The macro changes sum but not other parameters
431: .seealso: `PetscSparseDensePlusDot()`
432: */
433: #if defined(PETSC_KERNEL_USE_UNROLL_4)
434: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
435: { \
436: if (nnz > 0) { \
437: PetscInt nnz2 = nnz, rem = nnz & 0x3; \
438: switch (rem) { \
439: case 3: \
440: sum -= *xv++ * r[*xi++]; \
441: case 2: \
442: sum -= *xv++ * r[*xi++]; \
443: case 1: \
444: sum -= *xv++ * r[*xi++]; \
445: nnz2 -= rem; \
446: } \
447: while (nnz2 > 0) { \
448: sum -= xv[0] * r[xi[0]] + xv[1] * r[xi[1]] + xv[2] * r[xi[2]] + xv[3] * r[xi[3]]; \
449: xv += 4; \
450: xi += 4; \
451: nnz2 -= 4; \
452: } \
453: xv -= nnz; \
454: xi -= nnz; \
455: } \
456: }
458: #elif defined(PETSC_KERNEL_USE_UNROLL_2)
459: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
460: { \
461: PetscInt __i, __i1, __i2; \
462: for (__i = 0; __i < nnz - 1; __i += 2) { \
463: __i1 = xi[__i]; \
464: __i2 = xi[__i + 1]; \
465: sum -= (xv[__i] * r[__i1] + xv[__i + 1] * r[__i2]); \
466: } \
467: if (nnz & 0x1) sum -= xv[__i] * r[xi[__i]]; \
468: }
470: #else
471: #define PetscSparseDenseMinusDot(sum, r, xv, xi, nnz) \
472: { \
473: PetscInt __i; \
474: for (__i = 0; __i < nnz; __i++) sum -= xv[__i] * r[xi[__i]]; \
475: }
476: #endif
478: /*
479: PetscSparseDensePlusDot - The inner kernel of matrix-vector product \sum_i xv[i] * r[xi[i]] for CSR storage
481: Input Parameters:
482: + nnz - the number of entries
483: . r - the array of vector values
484: . xv - the matrix values for the row
485: - xi - the column indices of the nonzeros in the row
487: Output Parameter:
488: . sum - the sum of results
490: PETSc compile flags:
491: + PETSC_KERNEL_USE_UNROLL_4
492: - PETSC_KERNEL_USE_UNROLL_2
494: Developer Note:
495: The macro changes sum but not other parameters
497: .seealso: `PetscSparseDenseMinusDot()`
498: */
499: #if defined(PETSC_KERNEL_USE_UNROLL_4)
500: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
501: { \
502: if (nnz > 0) { \
503: PetscInt nnz2 = nnz, rem = nnz & 0x3; \
504: switch (rem) { \
505: case 3: \
506: sum += *xv++ * r[*xi++]; \
507: case 2: \
508: sum += *xv++ * r[*xi++]; \
509: case 1: \
510: sum += *xv++ * r[*xi++]; \
511: nnz2 -= rem; \
512: } \
513: while (nnz2 > 0) { \
514: sum += xv[0] * r[xi[0]] + xv[1] * r[xi[1]] + xv[2] * r[xi[2]] + xv[3] * r[xi[3]]; \
515: xv += 4; \
516: xi += 4; \
517: nnz2 -= 4; \
518: } \
519: xv -= nnz; \
520: xi -= nnz; \
521: } \
522: }
524: #elif defined(PETSC_KERNEL_USE_UNROLL_2)
525: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
526: { \
527: PetscInt __i, __i1, __i2; \
528: for (__i = 0; __i < nnz - 1; __i += 2) { \
529: __i1 = xi[__i]; \
530: __i2 = xi[__i + 1]; \
531: sum += (xv[__i] * r[__i1] + xv[__i + 1] * r[__i2]); \
532: } \
533: if (nnz & 0x1) sum += xv[__i] * r[xi[__i]]; \
534: }
536: #elif 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)
537: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) PetscSparseDensePlusDot_AVX512_Private(&(sum), (r), (xv), (xi), (nnz))
539: #else
540: #define PetscSparseDensePlusDot(sum, r, xv, xi, nnz) \
541: { \
542: PetscInt __i; \
543: for (__i = 0; __i < nnz; __i++) sum += xv[__i] * r[xi[__i]]; \
544: }
545: #endif
547: #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)
548: #include <immintrin.h>
549: #if !defined(_MM_SCALE_8)
550: #define _MM_SCALE_8 8
551: #endif
553: static inline void PetscSparseDensePlusDot_AVX512_Private(PetscScalar *sum, const PetscScalar *x, const MatScalar *aa, const PetscInt *aj, PetscInt n)
554: {
555: __m512d vec_x, vec_y, vec_vals;
556: __m256i vec_idx;
557: PetscInt j;
559: vec_y = _mm512_setzero_pd();
560: for (j = 0; j < (n >> 3); j++) {
561: vec_idx = _mm256_loadu_si256((__m256i const *)aj);
562: vec_vals = _mm512_loadu_pd(aa);
563: vec_x = _mm512_i32gather_pd(vec_idx, x, _MM_SCALE_8);
564: vec_y = _mm512_fmadd_pd(vec_x, vec_vals, vec_y);
565: aj += 8;
566: aa += 8;
567: }
568: #if defined(__AVX512VL__)
569: /* masked load requires avx512vl, which is not supported by KNL */
570: if (n & 0x07) {
571: __mmask8 mask;
572: mask = (__mmask8)(0xff >> (8 - (n & 0x07)));
573: vec_idx = _mm256_mask_loadu_epi32(vec_idx, mask, aj);
574: vec_vals = _mm512_mask_loadu_pd(vec_vals, mask, aa);
575: vec_x = _mm512_mask_i32gather_pd(vec_x, mask, vec_idx, x, _MM_SCALE_8);
576: vec_y = _mm512_mask3_fmadd_pd(vec_x, vec_vals, vec_y, mask);
577: }
578: *sum += _mm512_reduce_add_pd(vec_y);
579: #else
580: *sum += _mm512_reduce_add_pd(vec_y);
581: for (j = 0; j < (n & 0x07); j++) *sum += aa[j] * x[aj[j]];
582: #endif
583: }
584: #endif
586: /*
587: PetscSparseDenseMaxDot - The inner kernel of a modified matrix-vector product \max_i xv[i] * r[xi[i]] for CSR storage
589: Input Parameters:
590: + nnz - the number of entries
591: . r - the array of vector values
592: . xv - the matrix values for the row
593: - xi - the column indices of the nonzeros in the row
595: Output Parameter:
596: . max - the max of results
598: .seealso: `PetscSparseDensePlusDot()`, `PetscSparseDenseMinusDot()`
599: */
600: #define PetscSparseDenseMaxDot(max, r, xv, xi, nnz) \
601: { \
602: PetscInt __i; \
603: for (__i = 0; __i < nnz; __i++) max = PetscMax(PetscRealPart(max), PetscRealPart(xv[__i] * r[xi[__i]])); \
604: }
606: /*
607: Add column indices into table for counting the max nonzeros of merged rows
608: */
609: #define MatRowMergeMax_SeqAIJ(mat, nrows, ta) \
610: { \
611: PetscInt _j, _row, _nz, *_col; \
612: if (mat) { \
613: for (_row = 0; _row < nrows; _row++) { \
614: _nz = mat->i[_row + 1] - mat->i[_row]; \
615: for (_j = 0; _j < _nz; _j++) { \
616: _col = _j + mat->j + mat->i[_row]; \
617: PetscTableAdd(ta, *_col + 1, 1, INSERT_VALUES); \
618: } \
619: } \
620: } \
621: }
623: /*
624: Add column indices into table for counting the nonzeros of merged rows
625: */
626: #define MatMergeRows_SeqAIJ(mat, nrows, rows, ta) \
627: { \
628: PetscInt _j, _row, _nz, *_col, _i; \
629: for (_i = 0; _i < nrows; _i++) { \
630: _row = rows[_i]; \
631: _nz = mat->i[_row + 1] - mat->i[_row]; \
632: for (_j = 0; _j < _nz; _j++) { \
633: _col = _j + mat->j + mat->i[_row]; \
634: PetscTableAdd(ta, *_col + 1, 1, INSERT_VALUES); \
635: } \
636: } \
637: }
639: #endif