Actual source code: ex4.c
petsc-3.4.5 2014-06-29
1: static char help[] = "Test MatSetValuesBatch: setting batches of elements using the GPU.\n\
2: This works with SeqAIJCUSP and MPIAIJCUSP matrices.\n\n";
3: #include <petscdmda.h>
4: #include <petscksp.h>
6: /* We will use a structured mesh for this assembly test. Each square will be divided into two triangles:
7: C D
8: _______
9: |\ | The matrix for 0 and 1 is / 1 -0.5 -0.5 \
10: | \ 1 | | -0.5 0.5 0.0 |
11: | \ | \ -0.5 0.0 0.5 /
12: | \ |
13: | \ |
14: | 0 \ |
15: | \|
16: ---------
17: A B
19: TO ADD:
20: DONE 1) Build and run on baconost
21: - Gather data for CPU/GPU up to da_grid_x 1300
22: - Looks 6x faster than CPU
23: - Make plot
25: DONE 2) Solve the Neumann Poisson problem
27: 3) Multi-GPU Assembly
28: - MPIAIJCUSP: Just have two SEQAIJCUSP matrices, nothing else special
29: a) Filter rows to be sent to other procs (normally stashed)
30: b) send/recv rows, might as well do with a VecScatter
31: c) Potential to overlap this computation w/ GPU (talk to Nathan)
32: c') Just shove these rows in after the local
33: d) Have implicit rep of COO from repeated/tiled_range
34: e) Do a filtered copy, decrementing rows and remapping columns, which splits into two sets
35: f) Make two COO matrices and do separate aggregation on each one
37: 4) Solve the Neumann Poisson problem in parallel
38: - Try it on GPU machine at Brown (They need another GNU install)
40: 5) GPU FEM integration
41: - Move launch code to PETSc or - Try again now that assembly is in PETSc
42: - Move build code to PETSc
44: 6) Try out CUSP PCs
45: */
49: PetscErrorCode IntegrateCells(DM dm, PetscInt *Ne, PetscInt *Nl, PetscInt **elemRows, PetscScalar **elemMats)
50: {
51: DMDALocalInfo info;
52: PetscInt *er;
53: PetscScalar *em;
54: PetscInt X, Y, dof;
55: PetscInt nl, nxe, nye, ne;
56: PetscInt k = 0, m = 0;
57: PetscInt i, j;
58: PetscLogEvent integrationEvent;
62: PetscLogEventRegister("ElemIntegration", DM_CLASSID, &integrationEvent);
63: PetscLogEventBegin(integrationEvent,0,0,0,0);
64: DMDAGetInfo(dm, 0, &X, &Y,0,0,0,0, &dof,0,0,0,0,0);
65: DMDAGetLocalInfo(dm, &info);
66: nl = dof*3;
67: nxe = info.xm; if (info.xs+info.xm == X) nxe--;
68: nye = info.ym; if (info.ys+info.ym == Y) nye--;
69: ne = 2 * nxe * nye;
70: *Ne = ne;
71: *Nl = nl;
72: PetscMalloc2(ne*nl, PetscInt, elemRows, ne*nl*nl, PetscScalar, elemMats);
73: er = *elemRows;
74: em = *elemMats;
75: /* Proc 0 Proc 1 */
76: /* xs: 0 xm: 3 xs: 0 xm: 3 */
77: /* ys: 0 ym: 2 ys: 2 ym: 1 */
78: /* 8 elements x 3 vertices = 24 element matrix rows and 72 entries */
79: /* 6 offproc rows containing 18 element matrix entries */
80: /* 18 onproc rows containing 54 element matrix entries */
81: /* 3 offproc columns in 8 element matrix entries */
82: /* so we should have 46 diagonal matrix entries */
83: for (j = info.ys; j < info.ys+nye; ++j) {
84: for (i = info.xs; i < info.xs+nxe; ++i) {
85: PetscInt rowA = j*X + i, rowB = j*X + i+1;
86: PetscInt rowC = (j+1)*X + i, rowD = (j+1)*X + i+1;
88: /* Lower triangle */
89: er[k+0] = rowA; em[m+0*nl+0] = 1.0; em[m+0*nl+1] = -0.5; em[m+0*nl+2] = -0.5;
90: er[k+1] = rowB; em[m+1*nl+0] = -0.5; em[m+1*nl+1] = 0.5; em[m+1*nl+2] = 0.0;
91: er[k+2] = rowC; em[m+2*nl+0] = -0.5; em[m+2*nl+1] = 0.0; em[m+2*nl+2] = 0.5;
92: k += nl; m += nl*nl;
93: /* Upper triangle */
94: er[k+0] = rowD; em[m+0*nl+0] = 1.0; em[m+0*nl+1] = -0.5; em[m+0*nl+2] = -0.5;
95: er[k+1] = rowC; em[m+1*nl+0] = -0.5; em[m+1*nl+1] = 0.5; em[m+1*nl+2] = 0.0;
96: er[k+2] = rowB; em[m+2*nl+0] = -0.5; em[m+2*nl+1] = 0.0; em[m+2*nl+2] = 0.5;
97: k += nl; m += nl*nl;
98: }
99: }
100: PetscLogEventEnd(integrationEvent,0,0,0,0);
101: return(0);
102: }
106: int main(int argc, char **argv)
107: {
108: KSP ksp;
109: MatNullSpace nullsp;
110: DM dm;
111: Mat A;
112: Vec x, b;
113: PetscViewer viewer;
114: PetscInt Nl, Ne;
115: PetscInt *elemRows;
116: PetscScalar *elemMats;
117: PetscBool doGPU = PETSC_TRUE, doCPU = PETSC_TRUE, doSolve = PETSC_FALSE, doView = PETSC_TRUE;
118: PetscLogStage gpuStage, cpuStage;
121: PetscInitialize(&argc, &argv, 0, help);
122: DMDACreate2d(PETSC_COMM_WORLD, DMDA_BOUNDARY_NONE, DMDA_BOUNDARY_NONE, DMDA_STENCIL_BOX, -3, -3, PETSC_DECIDE, PETSC_DECIDE, 1, 1, NULL, NULL, &dm);
123: IntegrateCells(dm, &Ne, &Nl, &elemRows, &elemMats);
124: PetscOptionsGetBool(NULL, "-view", &doView, NULL);
125: /* Construct matrix using GPU */
126: PetscOptionsGetBool(NULL, "-gpu", &doGPU, NULL);
127: if (doGPU) {
128: PetscLogStageRegister("GPU Stage", &gpuStage);
129: PetscLogStagePush(gpuStage);
130: DMCreateMatrix(dm, MATAIJ, &A);
131: MatSetType(A, MATAIJCUSP);
132: MatSeqAIJSetPreallocation(A, 0, NULL);
133: MatMPIAIJSetPreallocation(A, 0, NULL, 0, NULL);
134: MatSetValuesBatch(A, Ne, Nl, elemRows, elemMats);
135: MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);
136: MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);
137: if (doView) {
138: PetscViewerASCIIOpen(PETSC_COMM_WORLD, NULL, &viewer);
139: if (Ne > 500) {PetscViewerPushFormat(viewer, PETSC_VIEWER_ASCII_INFO);}
140: MatView(A, viewer);
141: PetscViewerDestroy(&viewer);
142: }
143: PetscLogStagePop();
144: MatDestroy(&A);
145: }
146: /* Construct matrix using CPU */
147: PetscOptionsGetBool(NULL, "-cpu", &doCPU, NULL);
148: if (doCPU) {
149: PetscLogStageRegister("CPU Stage", &cpuStage);
150: PetscLogStagePush(cpuStage);
151: DMCreateMatrix(dm, MATAIJ, &A);
152: MatZeroEntries(A);
153: MatSetValuesBatch(A, Ne, Nl, elemRows, elemMats);
154: MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY);
155: MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY);
156: if (doView) {
157: PetscViewerASCIIOpen(PETSC_COMM_WORLD, NULL, &viewer);
158: if (Ne > 500) {PetscViewerPushFormat(viewer, PETSC_VIEWER_ASCII_INFO);}
159: MatView(A, viewer);
160: PetscViewerDestroy(&viewer);
161: }
162: PetscLogStagePop();
163: }
164: /* Solve simple system with random rhs */
165: PetscOptionsGetBool(NULL, "-solve", &doSolve, NULL);
166: if (doSolve) {
167: MatGetVecs(A, &x, &b);
168: VecSetRandom(b, NULL);
169: KSPCreate(PETSC_COMM_WORLD, &ksp);
170: KSPSetOperators(ksp, A, A, DIFFERENT_NONZERO_PATTERN);
171: MatNullSpaceCreate(PETSC_COMM_WORLD, PETSC_TRUE, 0, NULL, &nullsp);
172: KSPSetNullSpace(ksp, nullsp);
173: MatNullSpaceDestroy(&nullsp);
174: KSPSetFromOptions(ksp);
175: KSPSolve(ksp, b, x);
176: VecDestroy(&x);
177: VecDestroy(&b);
178: /* Solve physical system:
180: -\Delta u = -6 (x + y - 1)
182: where u = x^3 - 3/2 x^2 + y^3 - 3/2y^2 + 1/2,
183: so \Delta u = 6 x - 3 + 6 y - 3,
184: and \frac{\partial u}{\partial n} = {3x (x - 1), 3y (y - 1)} \cdot n
185: = \pm 3x (x - 1) at x=0,1 = 0
186: = \pm 3y (y - 1) at y=0,1 = 0
187: */
188: }
189: /* Cleanup */
190: MatDestroy(&A);
191: PetscFree2(elemRows, elemMats);
192: DMDestroy(&dm);
193: PetscFinalize();
194: return 0;
195: }