PaStiX Handbook  6.4.0
core_stqrcp.c
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1 /**
2  *
3  * @file core_stqrcp.c
4  *
5  * PaStiX implementation of the truncated rank-revealing QR with column pivoting
6  * based on Lapack GEQP3.
7  *
8  * @copyright 2016-2024 Bordeaux INP, CNRS (LaBRI UMR 5800), Inria,
9  * Univ. Bordeaux. All rights reserved.
10  *
11  * @version 6.4.0
12  * @author Alfredo Buttari
13  * @author Gregoire Pichon
14  * @author Esragul Korkmaz
15  * @author Mathieu Faverge
16  * @date 2024-07-05
17  * @generated from /builds/solverstack/pastix/kernels/core_ztqrcp.c, normal z -> s, Tue Oct 8 14:17:22 2024
18  *
19  **/
20 #include "common.h"
21 #include <cblas.h>
22 #include <lapacke.h>
23 #include "blend/solver.h"
24 #include "pastix_scores.h"
25 #include "pastix_slrcores.h"
26 #include "s_nan_check.h"
27 
28 #ifndef DOXYGEN_SHOULD_SKIP_THIS
29 static float msone = -1.0;
30 static float sone = 1.0;
31 static float szero = 0.0;
32 #endif /* DOXYGEN_SHOULD_SKIP_THIS */
33 
34 /**
35  *******************************************************************************
36  *
37  * @brief Compute a randomized QR factorization with truncated updates.
38  *
39  * This routine is derivated from "Randomized QR with Column Pivoting",
40  * J. A. Duersch and M. Gu, SIAM Journal on Scientific Computing, vol. 39,
41  * no. 4, pp. C263-C291, 2017.
42  *
43  *******************************************************************************
44  *
45  * @param[in] tol
46  * The relative tolerance criterion. Computations are stopped when the
47  * frobenius norm of the residual matrix is lower than tol.
48  * If tol < 0, then maxrank reflectors are computed.
49  *
50  * @param[in] maxrank
51  * Maximum number of reflectors computed. Computations are stopped when
52  * the rank exceeds maxrank. If maxrank < 0, all reflectors are computed
53  * or up to the tolerance criterion.
54  *
55  * @param[in] refine
56  * TODO
57  *
58  * @param[in] nb
59  * Tuning parameter for the GEMM blocking size. if nb < 0, nb is set to
60  * 32.
61  *
62  * @param[in] m
63  * Number of rows of the matrix A.
64  *
65  * @param[in] n
66  * Number of columns of the matrix A.
67  *
68  * @param[in] A
69  * The matrix of dimension lda-by-n that needs to be compressed.
70  *
71  * @param[in] lda
72  * The leading dimension of the matrix A. lda >= max(1, m).
73  *
74  * @param[out] jpvt
75  * The array that describes the permutation of A.
76  *
77  * @param[out] tau
78  * Contains scalar factors of the elementary reflectors for the matrix
79  * Q.
80  *
81  * @param[in] work
82  * Workspace array of size lwork.
83  *
84  * @param[in] lwork
85  * The dimension of the work area. lwork >= (nb * n + max(n, m) )
86  * If lwork == -1, the functions returns immediately and work[0]
87  * contains the optimal size of work.
88  *
89  * @param[in] rwork
90  * Workspace array used to store partial and exact column norms (2-by-n)
91  *
92  *******************************************************************************
93  *
94  * @return This routine will return the rank of A (>=0) or -1 if it didn't
95  * manage to compress within the margins of tolerance and maximum rank.
96  *
97  *******************************************************************************/
98 int
99 core_stqrcp( float tol,
100  pastix_int_t maxrank,
101  int refine,
102  pastix_int_t nb,
103  pastix_int_t m,
104  pastix_int_t n,
105  float *A,
106  pastix_int_t lda,
107  pastix_int_t *jpvt,
108  float *tau,
109  float *work,
110  pastix_int_t lwork,
111  float *rwork )
112 {
113  int SEED[4] = {26, 67, 52, 197};
114  pastix_int_t j, k, in, itmp, d, ib, loop = 1;
115  int ret;
116  pastix_int_t minMN, lwkopt;
117  pastix_int_t p = 5;
118  pastix_int_t bp = ( nb < p ) ? 32 : nb;
119  pastix_int_t b = bp - p;
120  pastix_int_t size_B, size_O, size_W, size_Y, size_A, size_T, sublw;
121  pastix_int_t ldb, ldw, ldy;
122  pastix_int_t *jpvt_b;
123  pastix_int_t rk;
124  float tolB = sqrtf( (float)(bp) ) * tol;
125  float *AP, *Y, *WT, *T, *B, *tau_b, *omega, *subw;
126 
127  minMN = pastix_imin(m, n);
128  if ( maxrank < 0 ) {
129  maxrank = minMN;
130  }
131  maxrank = pastix_imin( maxrank, minMN );
132 
133  ldb = bp;
134  ldw = maxrank;
135 
136  size_B = ldb * n;
137  size_O = ldb * m;
138  size_W = n * maxrank;
139  size_Y = b * b;
140  ldy = b;
141  size_A = m * n;
142  size_T = b * b;
143 
144  sublw = n * bp + pastix_imax( bp, n ); /* pqrcp */
145  sublw = pastix_imax( sublw, size_O ); /* Omega */
146  sublw = pastix_imax( sublw, b * maxrank ); /* update */
147 
148  lwkopt = size_A + size_Y + size_W
149  + size_T + size_B + n + sublw;
150 
151  if ( lwork == -1 ) {
152  work[0] = (float)lwkopt;
153  return 0;
154  }
155 #if !defined(NDEBUG)
156  if (m < 0) {
157  return -1;
158  }
159  if (n < 0) {
160  return -2;
161  }
162  if (lda < pastix_imax(1, m)) {
163  return -4;
164  }
165  if( lwork < lwkopt ) {
166  return -8;
167  }
168 #endif
169 
170  /**
171  * If maximum rank is 0, then either the matrix norm is below the tolerance,
172  * and we can return a null rank matrix, or it is not and we need to return
173  * a full rank matrix.
174  */
175  if ( maxrank == 0 ) {
176  float norm;
177  if ( tol < 0. ) {
178  return 0;
179  }
180  norm = LAPACKE_slange_work( LAPACK_COL_MAJOR, 'f', m, n,
181  A, lda, NULL );
182  if ( norm < tol ) {
183  return 0;
184  }
185  return -1;
186  }
187 
188  jpvt_b = malloc( n * sizeof(pastix_int_t) );
189 
190  AP = work;
191  Y = AP + size_A;
192  WT = Y + size_Y;
193  T = WT + size_W;
194  B = T + size_T;
195  tau_b = B + size_B;
196  omega = tau_b + n;
197  subw = tau_b + n;
198 
199  /* Initialize diagonal block of Housholders reflectors */
200  ret = LAPACKE_slaset_work( LAPACK_COL_MAJOR, 'A', b, b,
201  0., 1., Y, ldy );
202  assert( ret == 0 );
203 
204  /* Initialize T */
205  memset(T, 0, size_T * sizeof(float));
206 
207  /* Backup A */
208  ret = LAPACKE_slacpy_work( LAPACK_COL_MAJOR, 'A', m, n,
209  A, lda, AP, m );
210  assert( ret == 0 );
211 
212  /* Initialize pivots */
213  for (j=0; j<n; j++) jpvt[j] = j;
214 
215  /*
216  * Computation of the Gaussian matrix
217  */
218  ret = LAPACKE_slarnv_work(3, SEED, size_O, omega);
219  assert( ret == 0 );
220  cblas_sgemm( CblasColMajor, CblasNoTrans, CblasNoTrans,
221  bp, n, m,
222  (sone), omega, bp,
223  A, lda,
224  (szero), B, ldb );
225 
226  rk = 0;
227  d = 0;
228  while ( loop )
229  {
230  ib = pastix_imin( b, minMN-rk );
231  d = core_spqrcp( tolB, ib, 1, bp,
232  bp, n-rk,
233  B + rk * ldb, ldb,
234  jpvt_b + rk, tau_b,
235  subw, sublw, rwork );
236 
237  /* If fails to reach the tolerance before maxrank, let's restore the max value */
238  if ( d == -1 ) {
239  d = ib;
240  }
241  /* If smaller than ib, we reached the threshold */
242  if ( d < ib ) {
243  loop = 0;
244  }
245  if ( d == 0 ) {
246  break;
247  }
248  /* If we exceeded the max rank, let's stop now */
249  if ( (rk + d) > maxrank ) {
250  rk = -1;
251  break;
252  }
253 
254  /* Updating jpvt, A, and AP */
255  for (j = rk; j < rk + d; j++) {
256  if (jpvt_b[j] >= 0) {
257  k = j;
258  in = jpvt_b[k] + rk;
259 
260  /* Mark as done */
261  jpvt_b[k] = - jpvt_b[k] - 1;
262 
263  while( jpvt_b[in] >= 0 ) {
264 
265  if (k != in) {
266  cblas_sswap( m, A + k * lda, 1,
267  A + in * lda, 1 );
268  cblas_sswap( m, AP + k * m, 1,
269  AP + in * m, 1 );
270 
271  itmp = jpvt[k];
272  jpvt[k] = jpvt[in];
273  jpvt[in] = itmp;
274 
275  if (rk > 0) {
276  cblas_sswap( rk, WT + k * ldw, 1,
277  WT + in * ldw, 1 );
278  }
279  }
280  itmp = jpvt_b[in];
281  jpvt_b[in] = - jpvt_b[in] - 1;
282  k = in;
283  in = itmp + rk;
284  }
285  }
286  }
287 
288  if (rk > 0) {
289  /* Update the selected columns before factorization */
290  cblas_sgemm( CblasColMajor, CblasNoTrans, CblasNoTrans,
291  m-rk, d, rk,
292  (msone), A + rk, lda,
293  WT + rk * ldw, ldw,
294  (sone), A + rk * lda + rk, lda );
295  }
296 
297  /*
298  * Factorize the d selected columns of A without pivoting
299  */
300  ret = LAPACKE_sgeqrf_work( LAPACK_COL_MAJOR, m-rk, d,
301  A + rk * lda + rk, lda, tau + rk,
302  work, lwork );
303  assert( ret == 0 );
304 
305  ret = LAPACKE_slarft_work( LAPACK_COL_MAJOR, 'F', 'C', m-rk, d,
306  A + rk * lda + rk, lda, tau + rk, T, b );
307  assert( ret == 0 );
308 
309  /*
310  * Compute the update line 11 of algorithm 6 in "Randomized QR with
311  * Column pivoting" from Duersch and Gu
312  *
313  * W_2^h = T^h ( Y_2^h * A - (Y_2^h * Y) * W_1^h )
314  *
315  * Step 1: Y_2^h * A
316  * a) W[rk:rk+d] <- A
317  * b) W[rk:rk+d] <- Y_2^h * A, split in triangular part + rectangular part
318  */
319  ret = LAPACKE_slacpy_work( LAPACK_COL_MAJOR, 'L', d-1, d-1,
320  A + lda * rk + rk + 1, lda,
321  Y + 1, ldy );
322  assert( ret == 0 );
323 
324  /* Triangular part */
325  cblas_sgemm( CblasColMajor, CblasTrans, CblasNoTrans,
326  d, n, d,
327  (sone), Y, ldy,
328  AP + rk, m,
329  (szero), WT + rk, ldw );
330 
331  /* Rectangular part */
332  if ( rk + d < m ) {
333  cblas_sgemm( CblasColMajor, CblasTrans, CblasNoTrans,
334  d, n, m-rk-d,
335  (sone), A + rk * lda + rk + d, lda,
336  AP + rk + d, m,
337  (sone), WT + rk, ldw );
338  }
339 
340  /*
341  * Step 2: (Y_2^h * A) - (Y_2^h * Y) * W_1^h
342  * a) work = (Y_2^h * Y)
343  * b) (Y_2^h * A) - work * W_1^h
344  */
345  if ( rk > 0 ) {
346  /* Triangular part */
347  cblas_sgemm( CblasColMajor, CblasTrans, CblasNoTrans,
348  d, rk, d,
349  (sone), Y, ldy,
350  A + rk, lda,
351  (szero), subw, d );
352 
353  /* Rectangular part */
354  if ( rk + d < m ) {
355  cblas_sgemm( CblasColMajor, CblasTrans, CblasNoTrans,
356  d, rk, m-rk-d,
357  (sone), A + rk * lda + rk + d, lda,
358  A + rk + d, lda,
359  (sone), subw, d );
360  }
361 
362  cblas_sgemm( CblasColMajor, CblasNoTrans, CblasNoTrans,
363  d, n, rk,
364  (msone), subw, d,
365  WT, ldw,
366  (sone), WT + rk, ldw );
367  }
368 
369  /*
370  * Step 3: W_2^h = T^h ( Y_2^h * A - (Y_2^h * Y) * W_1^h )
371  * W_2^h = T^h W_2^h
372  */
373  cblas_strmm( CblasColMajor, CblasLeft, CblasUpper, CblasTrans, CblasNonUnit,
374  d, n, (sone),
375  T, b,
376  WT + rk, ldw );
377 
378  /* Update current d rows of R */
379  if ( rk+d < n ) {
380  cblas_sgemm( CblasColMajor, CblasNoTrans, CblasNoTrans,
381  d, n-rk-d, rk,
382  (msone), A + rk, lda,
383  WT + (rk+d)*ldw, ldw,
384  (sone), A + rk + (rk+d)*lda, lda );
385 
386  cblas_sgemm( CblasColMajor, CblasNoTrans, CblasNoTrans,
387  d, n-rk-d, d,
388  (msone), Y, ldy,
389  WT + rk + (rk+d)*ldw, ldw,
390  (sone), A + rk + (rk+d)*lda, lda );
391  }
392 
393  if ( loop && (rk+d < maxrank) ) {
394  /*
395  * The Q from partial QRCP is stored in the lower part of the matrix,
396  * we need to remove it
397  */
398  ret = LAPACKE_slaset_work( LAPACK_COL_MAJOR, 'L', d-1, d-1,
399  0, 0, B + rk*ldb + 1, ldb );
400  assert( ret == 0 );
401 
402  /* Updating B */
403  /* Solving S_11 * R_11^{-1} */
404  cblas_strsm( CblasColMajor, CblasRight, CblasUpper,
405  CblasNoTrans, CblasNonUnit,
406  d, d,
407  (sone), A + rk*lda + rk, lda,
408  B + rk*ldb, ldb );
409 
410  /* Updating S_12 = S_12 - (S_11 * R_11^{-1}) * R_12 */
411  cblas_sgemm( CblasColMajor, CblasNoTrans, CblasNoTrans,
412  d, n - (rk+d), d,
413  (msone), B + rk *ldb, ldb,
414  A + (rk+d)*lda + rk, lda,
415  (sone), B + (rk+d)*ldb, ldb );
416  }
417  rk += d;
418  }
419  free( jpvt_b );
420 
421  (void)ret;
422  (void)refine;
423  return rk;
424 }
425 
426 /**
427  *******************************************************************************
428  *
429  * @brief Convert a full rank matrix in a low rank matrix, using TQRCP.
430  *
431  *******************************************************************************
432  *
433  * @param[in] use_reltol
434  * Defines if the kernel should use relative tolerance (tol *||A||), or
435  * absolute tolerance (tol).
436  *
437  * @param[in] tol
438  * The tolerance used as a criterion to eliminate information from the
439  * full rank matrix
440  *
441  * @param[in] rklimit
442  * The maximum rank to store the matrix in low-rank format. If
443  * -1, set to min(m, n) / PASTIX_LR_MINRATIO.
444  *
445  * @param[in] m
446  * Number of rows of the matrix A, and of the low rank matrix Alr.
447  *
448  * @param[in] n
449  * Number of columns of the matrix A, and of the low rank matrix Alr.
450  *
451  * @param[in] A
452  * The matrix of dimension lda-by-n that needs to be compressed
453  *
454  * @param[in] lda
455  * The leading dimension of the matrix A. lda >= max(1, m)
456  *
457  * @param[out] Alr
458  * The low rank matrix structure that will store the low rank
459  * representation of A
460  *
461  *******************************************************************************
462  *
463  * @return TODO
464  *
465  *******************************************************************************/
466 pastix_fixdbl_t
467 core_sge2lr_tqrcp( int use_reltol,
468  pastix_fixdbl_t tol,
469  pastix_int_t rklimit,
470  pastix_int_t m,
471  pastix_int_t n,
472  const void *A,
473  pastix_int_t lda,
474  pastix_lrblock_t *Alr )
475 {
476  return core_sge2lr_qrcp( core_stqrcp, use_reltol, tol, rklimit,
477  m, n, A, lda, Alr );
478 }
479 
480 
481 /**
482  *******************************************************************************
483  *
484  * @brief Add two LR structures A=(-u1) v1^T and B=u2 v2^T into u2 v2^T
485  *
486  * u2v2^T - u1v1^T = (u2 u1) (v2 v1)^T
487  * Orthogonalize (u2 u1) = (u2, u1 - u2(u2^T u1)) * (I u2^T u1)
488  * (0 I )
489  * Compute TQRCP decomposition of (I u2^T u1) * (v2 v1)^T
490  * (0 I )
491  *
492  *******************************************************************************
493  *
494  * @param[in] lowrank
495  * The structure with low-rank parameters.
496  *
497  * @param[in] transA1
498  * @arg PastixNoTrans: No transpose, op( A ) = A;
499  * @arg PastixTrans: Transpose, op( A ) = A';
500  *
501  * @param[in] alphaptr
502  * alpha * A is add to B
503  *
504  * @param[in] M1
505  * The number of rows of the matrix A.
506  *
507  * @param[in] N1
508  * The number of columns of the matrix A.
509  *
510  * @param[in] A
511  * The low-rank representation of the matrix A.
512  *
513  * @param[in] M2
514  * The number of rows of the matrix B.
515  *
516  * @param[in] N2
517  * The number of columns of the matrix B.
518  *
519  * @param[in] B
520  * The low-rank representation of the matrix B.
521  *
522  * @param[in] offx
523  * The horizontal offset of A with respect to B.
524  *
525  * @param[in] offy
526  * The vertical offset of A with respect to B.
527  *
528  *******************************************************************************
529  *
530  * @return The new rank of u2 v2^T or -1 if ranks are too large for
531  * recompression
532  *
533  *******************************************************************************/
534 pastix_fixdbl_t
535 core_srradd_tqrcp( const pastix_lr_t *lowrank,
536  pastix_trans_t transA1,
537  const void *alphaptr,
538  pastix_int_t M1,
539  pastix_int_t N1,
540  const pastix_lrblock_t *A,
541  pastix_int_t M2,
542  pastix_int_t N2,
543  pastix_lrblock_t *B,
544  pastix_int_t offx,
545  pastix_int_t offy)
546 {
547  return core_srradd_qr( core_stqrcp, lowrank, transA1, alphaptr,
548  M1, N1, A, M2, N2, B, offx, offy );
549 }
pastix_int_t
BEGIN_C_DECLS typedef int pastix_int_t
Definition: datatypes.h:51
pastix_fixdbl_t
double pastix_fixdbl_t
Definition: datatypes.h:65
core_stqrcp
int core_stqrcp(float tol, pastix_int_t maxrank, int refine, pastix_int_t nb, pastix_int_t m, pastix_int_t n, float *A, pastix_int_t lda, pastix_int_t *jpvt, float *tau, float *work, pastix_int_t lwork, float *rwork)
Compute a randomized QR factorization with truncated updates.
Definition: core_stqrcp.c:99
core_spqrcp
int core_spqrcp(float tol, pastix_int_t maxrank, int full_update, pastix_int_t nb, pastix_int_t m, pastix_int_t n, float *A, pastix_int_t lda, pastix_int_t *jpvt, float *tau, float *work, pastix_int_t lwork, float *rwork)
Compute a rank-reavealing QR factorization.
Definition: core_spqrcp.c:105
pastix_lr_s
Structure to define the type of function to use for the low-rank kernels and their parameters.
Definition: pastix_lowrank.h:201
pastix_lrblock_s
The block low-rank structure to hold a matrix in low-rank form.
Definition: pastix_lowrank.h:166
core_sge2lr_tqrcp
pastix_fixdbl_t core_sge2lr_tqrcp(int use_reltol, pastix_fixdbl_t tol, pastix_int_t rklimit, pastix_int_t m, pastix_int_t n, const void *A, pastix_int_t lda, pastix_lrblock_t *Alr)
Convert a full rank matrix in a low rank matrix, using TQRCP.
Definition: core_stqrcp.c:467
core_sge2lr_qrcp
pastix_fixdbl_t core_sge2lr_qrcp(core_srrqr_cp_t rrqrfct, int use_reltol, pastix_fixdbl_t tol, pastix_int_t rklimit, pastix_int_t m, pastix_int_t n, const void *Avoid, pastix_int_t lda, pastix_lrblock_t *Alr)
Template to convert a full rank matrix into a low rank matrix through QR decompositions.
Definition: core_sgelrops.c:778
core_srradd_tqrcp
pastix_fixdbl_t core_srradd_tqrcp(const pastix_lr_t *lowrank, pastix_trans_t transA1, const void *alphaptr, pastix_int_t M1, pastix_int_t N1, const pastix_lrblock_t *A, pastix_int_t M2, pastix_int_t N2, pastix_lrblock_t *B, pastix_int_t offx, pastix_int_t offy)
Add two LR structures A=(-u1) v1^T and B=u2 v2^T into u2 v2^T.
Definition: core_stqrcp.c:535
core_srradd_qr
pastix_fixdbl_t core_srradd_qr(core_srrqr_cp_t rrqrfct, const pastix_lr_t *lowrank, pastix_trans_t transA1, const void *alphaptr, pastix_int_t M1, pastix_int_t N1, const pastix_lrblock_t *A, pastix_int_t M2, pastix_int_t N2, pastix_lrblock_t *B, pastix_int_t offx, pastix_int_t offy)
Template to perform the addition of two low-rank structures with compression kernel based on QR decom...
Definition: core_sgelrops.c:1218
pastix_trans_t
enum pastix_trans_e pastix_trans_t
Transpostion.
pastix_scores.h
pastix_slrcores.h
s_nan_check.h
Manage nancheck for lowrank kernels. This header describes all the LAPACKE functions used for low-ran...