Actual source code: ex52.c
2: static char help[] = "Solves a linear system in parallel with KSP. Modified from ex2.c \n\
3: Illustrate how to use external packages MUMPS, SUPERLU and STRUMPACK \n\
4: Input parameters include:\n\
5: -random_exact_sol : use a random exact solution vector\n\
6: -view_exact_sol : write exact solution vector to stdout\n\
7: -m <mesh_x> : number of mesh points in x-direction\n\
8: -n <mesh_y> : number of mesh points in y-direction\n\n";
10: #include <petscksp.h>
12: #if defined(PETSC_HAVE_MUMPS)
13: /* Subroutine contributed by Varun Hiremath */
14: PetscErrorCode printMumpsMemoryInfo(Mat F)
15: {
16: PetscInt maxMem, sumMem;
18: PetscFunctionBeginUser;
19: PetscCall(MatMumpsGetInfog(F, 16, &maxMem));
20: PetscCall(MatMumpsGetInfog(F, 17, &sumMem));
21: PetscCall(PetscPrintf(PETSC_COMM_WORLD, "\n MUMPS INFOG(16) :: Max memory in MB = %" PetscInt_FMT, maxMem));
22: PetscCall(PetscPrintf(PETSC_COMM_WORLD, "\n MUMPS INFOG(17) :: Sum memory in MB = %" PetscInt_FMT "\n", sumMem));
23: PetscFunctionReturn(PETSC_SUCCESS);
24: }
25: #endif
27: int main(int argc, char **args)
28: {
29: Vec x, b, u; /* approx solution, RHS, exact solution */
30: Mat A, F;
31: KSP ksp; /* linear solver context */
32: PC pc;
33: PetscRandom rctx; /* random number generator context */
34: PetscReal norm; /* norm of solution error */
35: PetscInt i, j, Ii, J, Istart, Iend, m = 8, n = 7, its;
36: PetscBool flg = PETSC_FALSE, flg_ilu = PETSC_FALSE, flg_ch = PETSC_FALSE;
37: #if defined(PETSC_HAVE_MUMPS)
38: PetscBool flg_mumps = PETSC_FALSE, flg_mumps_ch = PETSC_FALSE;
39: #endif
40: #if defined(PETSC_HAVE_SUPERLU) || defined(PETSC_HAVE_SUPERLU_DIST)
41: PetscBool flg_superlu = PETSC_FALSE;
42: #endif
43: #if defined(PETSC_HAVE_STRUMPACK)
44: PetscBool flg_strumpack = PETSC_FALSE;
45: #endif
46: PetscScalar v;
47: PetscMPIInt rank, size;
48: #if defined(PETSC_USE_LOG)
49: PetscLogStage stage;
50: #endif
52: PetscFunctionBeginUser;
53: PetscCall(PetscInitialize(&argc, &args, (char *)0, help));
54: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
55: PetscCallMPI(MPI_Comm_size(PETSC_COMM_WORLD, &size));
56: PetscCall(PetscOptionsGetInt(NULL, NULL, "-m", &m, NULL));
57: PetscCall(PetscOptionsGetInt(NULL, NULL, "-n", &n, NULL));
58: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
59: Compute the matrix and right-hand-side vector that define
60: the linear system, Ax = b.
61: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
62: PetscCall(MatCreate(PETSC_COMM_WORLD, &A));
63: PetscCall(MatSetSizes(A, PETSC_DECIDE, PETSC_DECIDE, m * n, m * n));
64: PetscCall(MatSetFromOptions(A));
65: PetscCall(MatMPIAIJSetPreallocation(A, 5, NULL, 5, NULL));
66: PetscCall(MatSeqAIJSetPreallocation(A, 5, NULL));
67: PetscCall(MatSetUp(A));
69: /*
70: Currently, all PETSc parallel matrix formats are partitioned by
71: contiguous chunks of rows across the processors. Determine which
72: rows of the matrix are locally owned.
73: */
74: PetscCall(MatGetOwnershipRange(A, &Istart, &Iend));
76: /*
77: Set matrix elements for the 2-D, five-point stencil in parallel.
78: - Each processor needs to insert only elements that it owns
79: locally (but any non-local elements will be sent to the
80: appropriate processor during matrix assembly).
81: - Always specify global rows and columns of matrix entries.
83: Note: this uses the less common natural ordering that orders first
84: all the unknowns for x = h then for x = 2h etc; Hence you see J = Ii +- n
85: instead of J = I +- m as you might expect. The more standard ordering
86: would first do all variables for y = h, then y = 2h etc.
88: */
89: PetscCall(PetscLogStageRegister("Assembly", &stage));
90: PetscCall(PetscLogStagePush(stage));
91: for (Ii = Istart; Ii < Iend; Ii++) {
92: v = -1.0;
93: i = Ii / n;
94: j = Ii - i * n;
95: if (i > 0) {
96: J = Ii - n;
97: PetscCall(MatSetValues(A, 1, &Ii, 1, &J, &v, INSERT_VALUES));
98: }
99: if (i < m - 1) {
100: J = Ii + n;
101: PetscCall(MatSetValues(A, 1, &Ii, 1, &J, &v, INSERT_VALUES));
102: }
103: if (j > 0) {
104: J = Ii - 1;
105: PetscCall(MatSetValues(A, 1, &Ii, 1, &J, &v, INSERT_VALUES));
106: }
107: if (j < n - 1) {
108: J = Ii + 1;
109: PetscCall(MatSetValues(A, 1, &Ii, 1, &J, &v, INSERT_VALUES));
110: }
111: v = 4.0;
112: PetscCall(MatSetValues(A, 1, &Ii, 1, &Ii, &v, INSERT_VALUES));
113: }
115: /*
116: Assemble matrix, using the 2-step process:
117: MatAssemblyBegin(), MatAssemblyEnd()
118: Computations can be done while messages are in transition
119: by placing code between these two statements.
120: */
121: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
122: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
123: PetscCall(PetscLogStagePop());
125: /* A is symmetric. Set symmetric flag to enable ICC/Cholesky preconditioner */
126: PetscCall(MatSetOption(A, MAT_SYMMETRIC, PETSC_TRUE));
128: /* Create parallel vectors */
129: PetscCall(MatCreateVecs(A, &u, &b));
130: PetscCall(VecDuplicate(u, &x));
132: /*
133: Set exact solution; then compute right-hand-side vector.
134: By default we use an exact solution of a vector with all
135: elements of 1.0; Alternatively, using the runtime option
136: -random_sol forms a solution vector with random components.
137: */
138: PetscCall(PetscOptionsGetBool(NULL, NULL, "-random_exact_sol", &flg, NULL));
139: if (flg) {
140: PetscCall(PetscRandomCreate(PETSC_COMM_WORLD, &rctx));
141: PetscCall(PetscRandomSetFromOptions(rctx));
142: PetscCall(VecSetRandom(u, rctx));
143: PetscCall(PetscRandomDestroy(&rctx));
144: } else {
145: PetscCall(VecSet(u, 1.0));
146: }
147: PetscCall(MatMult(A, u, b));
149: /*
150: View the exact solution vector if desired
151: */
152: flg = PETSC_FALSE;
153: PetscCall(PetscOptionsGetBool(NULL, NULL, "-view_exact_sol", &flg, NULL));
154: if (flg) PetscCall(VecView(u, PETSC_VIEWER_STDOUT_WORLD));
156: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
157: Create the linear solver and set various options
158: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
160: /*
161: Create linear solver context
162: */
163: PetscCall(KSPCreate(PETSC_COMM_WORLD, &ksp));
164: PetscCall(KSPSetOperators(ksp, A, A));
166: /*
167: Example of how to use external package MUMPS
168: Note: runtime options
169: '-ksp_type preonly -pc_type lu -pc_factor_mat_solver_type mumps -mat_mumps_icntl_7 3 -mat_mumps_icntl_1 0.0'
170: are equivalent to these procedural calls
171: */
172: #if defined(PETSC_HAVE_MUMPS)
173: flg_mumps = PETSC_FALSE;
174: flg_mumps_ch = PETSC_FALSE;
175: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_mumps_lu", &flg_mumps, NULL));
176: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_mumps_ch", &flg_mumps_ch, NULL));
177: if (flg_mumps || flg_mumps_ch) {
178: PetscCall(KSPSetType(ksp, KSPPREONLY));
179: PetscInt ival, icntl;
180: PetscReal val;
181: PetscCall(KSPGetPC(ksp, &pc));
182: if (flg_mumps) {
183: PetscCall(PCSetType(pc, PCLU));
184: } else if (flg_mumps_ch) {
185: PetscCall(MatSetOption(A, MAT_SPD, PETSC_TRUE)); /* set MUMPS id%SYM=1 */
186: PetscCall(PCSetType(pc, PCCHOLESKY));
187: }
188: PetscCall(PCFactorSetMatSolverType(pc, MATSOLVERMUMPS));
189: PetscCall(PCFactorSetUpMatSolverType(pc)); /* call MatGetFactor() to create F */
190: PetscCall(PCFactorGetMatrix(pc, &F));
192: if (flg_mumps) {
193: /* Zero the first and last rows in the rank, they should then show up in corresponding null pivot rows output via
194: MatMumpsGetNullPivots */
195: flg = PETSC_FALSE;
196: PetscCall(PetscOptionsGetBool(NULL, NULL, "-zero_first_and_last_rows", &flg, NULL));
197: if (flg) {
198: PetscInt rows[2];
199: rows[0] = Istart; /* first row of the rank */
200: rows[1] = Iend - 1; /* last row of the rank */
201: PetscCall(MatZeroRows(A, 2, rows, 0.0, NULL, NULL));
202: }
203: /* Get memory estimates from MUMPS' MatLUFactorSymbolic(), e.g. INFOG(16), INFOG(17).
204: KSPSetUp() below will do nothing inside MatLUFactorSymbolic() */
205: MatFactorInfo info;
206: PetscCall(MatLUFactorSymbolic(F, A, NULL, NULL, &info));
207: flg = PETSC_FALSE;
208: PetscCall(PetscOptionsGetBool(NULL, NULL, "-print_mumps_memory", &flg, NULL));
209: if (flg) PetscCall(printMumpsMemoryInfo(F));
210: }
212: /* sequential ordering */
213: icntl = 7;
214: ival = 2;
215: PetscCall(MatMumpsSetIcntl(F, icntl, ival));
217: /* threshold for row pivot detection */
218: PetscCall(MatMumpsSetIcntl(F, 24, 1));
219: icntl = 3;
220: val = 1.e-6;
221: PetscCall(MatMumpsSetCntl(F, icntl, val));
223: /* compute determinant of A */
224: PetscCall(MatMumpsSetIcntl(F, 33, 1));
225: }
226: #endif
228: /*
229: Example of how to use external package SuperLU
230: Note: runtime options
231: '-ksp_type preonly -pc_type ilu -pc_factor_mat_solver_type superlu -mat_superlu_ilu_droptol 1.e-8'
232: are equivalent to these procedual calls
233: */
234: #if defined(PETSC_HAVE_SUPERLU) || defined(PETSC_HAVE_SUPERLU_DIST)
235: flg_ilu = PETSC_FALSE;
236: flg_superlu = PETSC_FALSE;
237: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_superlu_lu", &flg_superlu, NULL));
238: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_superlu_ilu", &flg_ilu, NULL));
239: if (flg_superlu || flg_ilu) {
240: PetscCall(KSPSetType(ksp, KSPPREONLY));
241: PetscCall(KSPGetPC(ksp, &pc));
242: if (flg_superlu) PetscCall(PCSetType(pc, PCLU));
243: else if (flg_ilu) PetscCall(PCSetType(pc, PCILU));
244: if (size == 1) {
245: #if !defined(PETSC_HAVE_SUPERLU)
246: SETERRQ(PETSC_COMM_WORLD, PETSC_ERR_SUP, "This test requires SUPERLU");
247: #else
248: PetscCall(PCFactorSetMatSolverType(pc, MATSOLVERSUPERLU));
249: #endif
250: } else {
251: #if !defined(PETSC_HAVE_SUPERLU_DIST)
252: SETERRQ(PETSC_COMM_WORLD, PETSC_ERR_SUP, "This test requires SUPERLU_DIST");
253: #else
254: PetscCall(PCFactorSetMatSolverType(pc, MATSOLVERSUPERLU_DIST));
255: #endif
256: }
257: PetscCall(PCFactorSetUpMatSolverType(pc)); /* call MatGetFactor() to create F */
258: PetscCall(PCFactorGetMatrix(pc, &F));
259: #if defined(PETSC_HAVE_SUPERLU)
260: if (size == 1) PetscCall(MatSuperluSetILUDropTol(F, 1.e-8));
261: #endif
262: }
263: #endif
265: /*
266: Example of how to use external package STRUMPACK
267: Note: runtime options
268: '-pc_type lu/ilu \
269: -pc_factor_mat_solver_type strumpack \
270: -mat_strumpack_reordering METIS \
271: -mat_strumpack_colperm 0 \
272: -mat_strumpack_hss_rel_tol 1.e-3 \
273: -mat_strumpack_hss_min_sep_size 50 \
274: -mat_strumpack_max_rank 100 \
275: -mat_strumpack_leaf_size 4'
276: are equivalent to these procedural calls
278: We refer to the STRUMPACK-sparse manual, section 5, for more info on
279: how to tune the preconditioner.
280: */
281: #if defined(PETSC_HAVE_STRUMPACK)
282: flg_ilu = PETSC_FALSE;
283: flg_strumpack = PETSC_FALSE;
284: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_strumpack_lu", &flg_strumpack, NULL));
285: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_strumpack_ilu", &flg_ilu, NULL));
286: if (flg_strumpack || flg_ilu) {
287: PetscCall(KSPSetType(ksp, KSPPREONLY));
288: PetscCall(KSPGetPC(ksp, &pc));
289: if (flg_strumpack) PetscCall(PCSetType(pc, PCLU));
290: else if (flg_ilu) PetscCall(PCSetType(pc, PCILU));
291: #if !defined(PETSC_HAVE_STRUMPACK)
292: SETERRQ(PETSC_COMM_WORLD, PETSC_ERR_SUP, "This test requires STRUMPACK");
293: #endif
294: PetscCall(PCFactorSetMatSolverType(pc, MATSOLVERSTRUMPACK));
295: PetscCall(PCFactorSetUpMatSolverType(pc)); /* call MatGetFactor() to create F */
296: PetscCall(PCFactorGetMatrix(pc, &F));
297: #if defined(PETSC_HAVE_STRUMPACK)
298: /* Set the fill-reducing reordering. */
299: PetscCall(MatSTRUMPACKSetReordering(F, MAT_STRUMPACK_METIS));
300: /* Since this is a simple discretization, the diagonal is always */
301: /* nonzero, and there is no need for the extra MC64 permutation. */
302: PetscCall(MatSTRUMPACKSetColPerm(F, PETSC_FALSE));
303: /* The compression tolerance used when doing low-rank compression */
304: /* in the preconditioner. This is problem specific! */
305: PetscCall(MatSTRUMPACKSetHSSRelTol(F, 1.e-3));
306: /* Set minimum matrix size for HSS compression to 15 in order to */
307: /* demonstrate preconditioner on small problems. For performance */
308: /* a value of say 500 is better. */
309: PetscCall(MatSTRUMPACKSetHSSMinSepSize(F, 15));
310: /* You can further limit the fill in the preconditioner by */
311: /* setting a maximum rank */
312: PetscCall(MatSTRUMPACKSetHSSMaxRank(F, 100));
313: /* Set the size of the diagonal blocks (the leafs) in the HSS */
314: /* approximation. The default value should be better for real */
315: /* problems. This is mostly for illustration on a small problem. */
316: PetscCall(MatSTRUMPACKSetHSSLeafSize(F, 4));
317: #endif
318: }
319: #endif
321: /*
322: Example of how to use procedural calls that are equivalent to
323: '-ksp_type preonly -pc_type lu/ilu -pc_factor_mat_solver_type petsc'
324: */
325: flg = PETSC_FALSE;
326: flg_ilu = PETSC_FALSE;
327: flg_ch = PETSC_FALSE;
328: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_petsc_lu", &flg, NULL));
329: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_petsc_ilu", &flg_ilu, NULL));
330: PetscCall(PetscOptionsGetBool(NULL, NULL, "-use_petsc_ch", &flg_ch, NULL));
331: if (flg || flg_ilu || flg_ch) {
332: Vec diag;
334: PetscCall(KSPSetType(ksp, KSPPREONLY));
335: PetscCall(KSPGetPC(ksp, &pc));
336: if (flg) PetscCall(PCSetType(pc, PCLU));
337: else if (flg_ilu) PetscCall(PCSetType(pc, PCILU));
338: else if (flg_ch) PetscCall(PCSetType(pc, PCCHOLESKY));
339: PetscCall(PCFactorSetMatSolverType(pc, MATSOLVERPETSC));
340: PetscCall(PCFactorSetUpMatSolverType(pc)); /* call MatGetFactor() to create F */
341: PetscCall(PCFactorGetMatrix(pc, &F));
343: /* Test MatGetDiagonal() */
344: PetscCall(KSPSetUp(ksp));
345: PetscCall(VecDuplicate(x, &diag));
346: PetscCall(MatGetDiagonal(F, diag));
347: /* PetscCall(VecView(diag,PETSC_VIEWER_STDOUT_WORLD)); */
348: PetscCall(VecDestroy(&diag));
349: }
351: PetscCall(KSPSetFromOptions(ksp));
353: /* Get info from matrix factors */
354: PetscCall(KSPSetUp(ksp));
356: #if defined(PETSC_HAVE_MUMPS)
357: if (flg_mumps || flg_mumps_ch) {
358: PetscInt icntl, infog34, num_null_pivots, *null_pivots;
359: PetscReal cntl, rinfo12, rinfo13;
360: icntl = 3;
361: PetscCall(MatMumpsGetCntl(F, icntl, &cntl));
363: /* compute determinant and check for any null pivots*/
364: if (rank == 0) {
365: PetscCall(MatMumpsGetInfog(F, 34, &infog34));
366: PetscCall(MatMumpsGetRinfog(F, 12, &rinfo12));
367: PetscCall(MatMumpsGetRinfog(F, 13, &rinfo13));
368: PetscCall(MatMumpsGetNullPivots(F, &num_null_pivots, &null_pivots));
369: PetscCall(PetscPrintf(PETSC_COMM_SELF, " Mumps row pivot threshold = %g\n", cntl));
370: PetscCall(PetscPrintf(PETSC_COMM_SELF, " Mumps determinant = (%g, %g) * 2^%" PetscInt_FMT " \n", (double)rinfo12, (double)rinfo13, infog34));
371: if (num_null_pivots > 0) {
372: PetscCall(PetscPrintf(PETSC_COMM_SELF, " Mumps num of null pivots detected = %" PetscInt_FMT "\n", num_null_pivots));
373: for (j = 0; j < num_null_pivots; j++) PetscCall(PetscPrintf(PETSC_COMM_SELF, " Mumps row with null pivots is = %" PetscInt_FMT "\n", null_pivots[j]));
374: }
375: PetscCall(PetscFree(null_pivots));
376: }
377: }
378: #endif
380: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
381: Solve the linear system
382: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
383: PetscCall(KSPSolve(ksp, b, x));
385: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
386: Check solution and clean up
387: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
388: PetscCall(VecAXPY(x, -1.0, u));
389: PetscCall(VecNorm(x, NORM_2, &norm));
390: PetscCall(KSPGetIterationNumber(ksp, &its));
392: /*
393: Print convergence information. PetscPrintf() produces a single
394: print statement from all processes that share a communicator.
395: An alternative is PetscFPrintf(), which prints to a file.
396: */
397: if (norm < 1.e-12) {
398: PetscCall(PetscPrintf(PETSC_COMM_WORLD, "Norm of error < 1.e-12 iterations %" PetscInt_FMT "\n", its));
399: } else {
400: PetscCall(PetscPrintf(PETSC_COMM_WORLD, "Norm of error %g iterations %" PetscInt_FMT "\n", (double)norm, its));
401: }
403: /*
404: Free work space. All PETSc objects should be destroyed when they
405: are no longer needed.
406: */
407: PetscCall(KSPDestroy(&ksp));
408: PetscCall(VecDestroy(&u));
409: PetscCall(VecDestroy(&x));
410: PetscCall(VecDestroy(&b));
411: PetscCall(MatDestroy(&A));
413: /*
414: Always call PetscFinalize() before exiting a program. This routine
415: - finalizes the PETSc libraries as well as MPI
416: - provides summary and diagnostic information if certain runtime
417: options are chosen (e.g., -log_view).
418: */
419: PetscCall(PetscFinalize());
420: return 0;
421: }
423: /*TEST
425: test:
426: args: -use_petsc_lu
427: output_file: output/ex52_2.out
429: test:
430: suffix: mumps
431: nsize: 3
432: requires: mumps
433: args: -use_mumps_lu
434: output_file: output/ex52_1.out
436: test:
437: suffix: mumps_2
438: nsize: 3
439: requires: mumps
440: args: -use_mumps_ch
441: output_file: output/ex52_1.out
443: test:
444: suffix: mumps_3
445: nsize: 3
446: requires: mumps
447: args: -use_mumps_ch -mat_type sbaij
448: output_file: output/ex52_1.out
450: test:
451: suffix: mumps_4
452: nsize: 3
453: requires: mumps !complex !single
454: args: -use_mumps_lu -m 50 -n 50 -print_mumps_memory
455: output_file: output/ex52_4.out
457: test:
458: suffix: mumps_5
459: nsize: 3
460: requires: mumps !complex !single
461: args: -use_mumps_lu -m 50 -n 50 -zero_first_and_last_rows
462: output_file: output/ex52_5.out
464: test:
465: suffix: mumps_omp_2
466: nsize: 4
467: requires: mumps hwloc openmp pthread defined(PETSC_HAVE_MPI_PROCESS_SHARED_MEMORY)
468: args: -use_mumps_lu -mat_mumps_use_omp_threads 2
469: output_file: output/ex52_1.out
471: test:
472: suffix: mumps_omp_3
473: nsize: 4
474: requires: mumps hwloc openmp pthread defined(PETSC_HAVE_MPI_PROCESS_SHARED_MEMORY)
475: args: -use_mumps_ch -mat_mumps_use_omp_threads 3
476: # Ignore the warning since we are intentionally testing the imbalanced case
477: filter: grep -v "Warning: number of OpenMP threads"
478: output_file: output/ex52_1.out
480: test:
481: suffix: mumps_omp_4
482: nsize: 4
483: requires: mumps hwloc openmp pthread defined(PETSC_HAVE_MPI_PROCESS_SHARED_MEMORY)
484: # let petsc guess a proper number for threads
485: args: -use_mumps_ch -mat_type sbaij -mat_mumps_use_omp_threads
486: output_file: output/ex52_1.out
488: testset:
489: suffix: strumpack_2
490: nsize: {{1 2}}
491: requires: strumpack
492: args: -use_strumpack_lu
493: output_file: output/ex52_3.out
495: test:
496: suffix: aij
497: args: -mat_type aij
499: test:
500: requires: kokkos_kernels
501: suffix: kok
502: args: -mat_type aijkokkos
504: test:
505: requires: cuda
506: suffix: cuda
507: args: -mat_type aijcusparse
509: test:
510: requires: hip
511: suffix: hip
512: args: -mat_type aijhipsparse
514: test:
515: suffix: strumpack_ilu
516: nsize: {{1 2}}
517: requires: strumpack
518: args: -use_strumpack_ilu
519: output_file: output/ex52_3.out
521: testset:
522: suffix: superlu_dist
523: nsize: {{1 2}}
524: requires: superlu superlu_dist
525: args: -use_superlu_lu
526: output_file: output/ex52_2.out
528: test:
529: suffix: aij
530: args: -mat_type aij
532: test:
533: requires: kokkos_kernels
534: suffix: kok
535: args: -mat_type aijkokkos
537: test:
538: requires: cuda
539: suffix: cuda
540: args: -mat_type aijcusparse
542: test:
543: requires: hip
544: suffix: hip
545: args: -mat_type aijhipsparse
547: test:
548: suffix: superlu_ilu
549: requires: superlu superlu_dist
550: args: -use_superlu_ilu
551: output_file: output/ex52_2.out
553: TEST*/