Actual source code: ex96.c
2: static char help[] ="Tests sequential and parallel DAGetMatrix(), MatMatMult() and MatPtAP()\n\
3: -Mx <xg>, where <xg> = number of coarse grid points in the x-direction\n\
4: -My <yg>, where <yg> = number of coarse grid points in the y-direction\n\
5: -Mz <zg>, where <zg> = number of coarse grid points in the z-direction\n\
6: -Npx <npx>, where <npx> = number of processors in the x-direction\n\
7: -Npy <npy>, where <npy> = number of processors in the y-direction\n\
8: -Npz <npz>, where <npz> = number of processors in the z-direction\n\n";
10: /*
11: This test is modified from ~src/ksp/examples/tests/ex19.c.
12: Example of usage: mpirun -np 3 ex96 -Mx 10 -My 10 -Mz 10
13: */
15: #include petscksp.h
16: #include petscda.h
17: #include petscmg.h
18: #include src/mat/impls/aij/seq/aij.h
19: #include src/mat/impls/aij/mpi/mpiaij.h
21: /* User-defined application contexts */
22: typedef struct {
23: PetscInt mx,my,mz; /* number grid points in x, y and z direction */
24: Vec localX,localF; /* local vectors with ghost region */
25: DA da;
26: Vec x,b,r; /* global vectors */
27: Mat J; /* Jacobian on grid */
28: } GridCtx;
29: typedef struct {
30: GridCtx fine;
31: GridCtx coarse;
32: KSP ksp_coarse;
33: PetscInt ratio;
34: Mat I; /* interpolation from coarse to fine */
35: } AppCtx;
37: #define COARSE_LEVEL 0
38: #define FINE_LEVEL 1
40: /*
41: Mm_ratio - ration of grid lines between fine and coarse grids.
42: */
45: int main(int argc,char **argv)
46: {
48: AppCtx user;
49: PetscInt Npx=PETSC_DECIDE,Npy=PETSC_DECIDE,Npz=PETSC_DECIDE;
50: PetscMPIInt size,rank;
51: PetscInt m,n,M,N,i,nrows,*ia,*ja;
52: PetscScalar one = 1.0;
53: PetscReal fill=2.0;
54: Mat A,A_tmp,P,C;
55: PetscScalar *array,none = -1.0,alpha;
56: Vec x,v1,v2,v3,v4;
57: PetscReal norm,norm_tmp,norm_tmp1,tol=1.e-12;
58: PetscRandom rdm;
59: PetscTruth Test_MatMatMult=PETSC_TRUE,Test_MatPtAP=PETSC_TRUE,Test_3D=PETSC_FALSE,flg;
61: PetscInitialize(&argc,&argv,PETSC_NULL,help);
62: PetscOptionsGetReal(PETSC_NULL,"-tol",&tol,PETSC_NULL);
64: user.ratio = 2;
65: user.coarse.mx = 2; user.coarse.my = 2; user.coarse.mz = 0;
66: PetscOptionsGetInt(PETSC_NULL,"-Mx",&user.coarse.mx,PETSC_NULL);
67: PetscOptionsGetInt(PETSC_NULL,"-My",&user.coarse.my,PETSC_NULL);
68: PetscOptionsGetInt(PETSC_NULL,"-Mz",&user.coarse.mz,PETSC_NULL);
69: PetscOptionsGetInt(PETSC_NULL,"-ratio",&user.ratio,PETSC_NULL);
70: if (user.coarse.mz) Test_3D = PETSC_TRUE;
72: user.fine.mx = user.ratio*(user.coarse.mx-1)+1;
73: user.fine.my = user.ratio*(user.coarse.my-1)+1;
74: user.fine.mz = user.ratio*(user.coarse.mz-1)+1;
75: MPI_Comm_size(PETSC_COMM_WORLD,&size);
76: MPI_Comm_rank(PETSC_COMM_WORLD,&rank);
77: PetscOptionsGetInt(PETSC_NULL,"-Npx",&Npx,PETSC_NULL);
78: PetscOptionsGetInt(PETSC_NULL,"-Npy",&Npy,PETSC_NULL);
79: PetscOptionsGetInt(PETSC_NULL,"-Npz",&Npz,PETSC_NULL);
81: /* Set up distributed array for fine grid */
82: if (!Test_3D){
83: DACreate2d(PETSC_COMM_WORLD,DA_NONPERIODIC,DA_STENCIL_STAR,user.fine.mx,
84: user.fine.my,Npx,Npy,1,1,PETSC_NULL,PETSC_NULL,&user.fine.da);
85: } else {
86: DACreate3d(PETSC_COMM_WORLD,DA_NONPERIODIC,DA_STENCIL_STAR,
87: user.fine.mx,user.fine.my,user.fine.mz,Npx,Npy,Npz,
88: 1,1,PETSC_NULL,PETSC_NULL,PETSC_NULL,&user.fine.da);
89: }
91: /* Test DAGetMatrix() */
92: /*------------------------------------------------------------*/
93: DAGetMatrix(user.fine.da,MATAIJ,&A);
94: DAGetMatrix(user.fine.da,MATBAIJ,&C);
95:
96: MatConvert(C,MATAIJ,MAT_INITIAL_MATRIX,&A_tmp); /* not work for mpisbaij matrix! */
97: MatEqual(A,A_tmp,&flg);
98: if (!flg) {
99: SETERRQ(PETSC_ERR_ARG_NOTSAMETYPE,"A != C");
100: }
101: MatDestroy(C);
102: MatDestroy(A_tmp);
103:
104: /*------------------------------------------------------------*/
105:
106: MatGetLocalSize(A,&m,&n);
107: MatGetSize(A,&M,&N);
108: /* set val=one to A */
109: if (size == 1){
110: MatGetRowIJ(A,0,PETSC_FALSE,&nrows,&ia,&ja,&flg);
111: if (flg){
112: MatGetArray(A,&array);
113: for (i=0; i<ia[nrows]; i++) array[i] = one;
114: MatRestoreArray(A,&array);
115: }
116: MatRestoreRowIJ(A,0,PETSC_FALSE,&nrows,&ia,&ja,&flg);
117: } else {
118: Mat_MPIAIJ *aij = (Mat_MPIAIJ*)A->data;
119: Mat_SeqAIJ *a=(Mat_SeqAIJ*)(aij->A)->data, *b=(Mat_SeqAIJ*)(aij->B)->data;
120: /* A_part */
121: for (i=0; i<a->i[m]; i++) a->a[i] = one;
122: /* B_part */
123: for (i=0; i<b->i[m]; i++) b->a[i] = one;
124:
125: }
126: /* MatView(A, PETSC_VIEWER_STDOUT_WORLD); */
128: /* Set up distributed array for coarse grid */
129: if (!Test_3D){
130: DACreate2d(PETSC_COMM_WORLD,DA_NONPERIODIC,DA_STENCIL_STAR,user.coarse.mx,
131: user.coarse.my,Npx,Npy,1,1,PETSC_NULL,PETSC_NULL,&user.coarse.da);
132: } else {
133: DACreate3d(PETSC_COMM_WORLD,DA_NONPERIODIC,DA_STENCIL_STAR,
134: user.coarse.mx,user.coarse.my,user.coarse.mz,Npx,Npy,Npz,
135: 1,1,PETSC_NULL,PETSC_NULL,PETSC_NULL,&user.coarse.da);
136: }
138: /* Create interpolation between the levels */
139: DAGetInterpolation(user.coarse.da,user.fine.da,&P,PETSC_NULL);
140:
141: MatGetLocalSize(P,&m,&n);
142: MatGetSize(P,&M,&N);
144: /* Create vectors v1 and v2 that are compatible with A */
145: VecCreate(PETSC_COMM_WORLD,&v1);
146: MatGetLocalSize(A,&m,PETSC_NULL);
147: VecSetSizes(v1,m,PETSC_DECIDE);
148: VecSetFromOptions(v1);
149: VecDuplicate(v1,&v2);
150: PetscRandomCreate(PETSC_COMM_WORLD,RANDOM_DEFAULT,&rdm);
152: /* Test MatMatMult(): C = A*P */
153: /*----------------------------*/
154: if (Test_MatMatMult){
155: MatDuplicate(A,MAT_COPY_VALUES,&A_tmp);
156: MatMatMult(A_tmp,P,MAT_INITIAL_MATRIX,fill,&C);
157:
158: /* Test MAT_REUSE_MATRIX - reuse symbolic C */
159: alpha=1.0;
160: for (i=0; i<2; i++){
161: alpha -=0.1;
162: MatScale(A_tmp,alpha);
163: MatMatMult(A_tmp,P,MAT_REUSE_MATRIX,fill,&C);
164: }
165: /*
166: if (rank == 0) PetscPrintf(PETSC_COMM_SELF, " \nA*P: \n");
167: MatView(C, PETSC_VIEWER_STDOUT_WORLD);
168: */
170: /* Create vector x that is compatible with P */
171: VecCreate(PETSC_COMM_WORLD,&x);
172: MatGetLocalSize(P,PETSC_NULL,&n);
173: VecSetSizes(x,n,PETSC_DECIDE);
174: VecSetFromOptions(x);
176: norm = 0.0;
177: for (i=0; i<10; i++) {
178: VecSetRandom(x,rdm);
179: MatMult(P,x,v1);
180: MatMult(A_tmp,v1,v2); /* v2 = A*P*x */
181: MatMult(C,x,v1); /* v1 = C*x */
182: VecAXPY(v1,none,v2);
183: VecNorm(v1,NORM_1,&norm_tmp);
184: VecNorm(v2,NORM_1,&norm_tmp1);
185: norm_tmp /= norm_tmp1;
186: if (norm_tmp > norm) norm = norm_tmp;
187: }
188: if (norm >= tol && !rank) {
189: PetscPrintf(PETSC_COMM_SELF,"Error: MatMatMult(), |v1 - v2|/|v2|: %g\n",norm);
190: }
191:
192: VecDestroy(x);
193: MatDestroy(C);
194: MatDestroy(A_tmp);
195: }
197: /* Test P^T * A * P - MatPtAP() */
198: /*------------------------------*/
199: if (Test_MatPtAP){
200: MatPtAP(A,P,MAT_INITIAL_MATRIX,fill,&C);
201: MatGetLocalSize(C,&m,&n);
202:
203: /* Test MAT_REUSE_MATRIX - reuse symbolic C */
204: alpha=1.0;
205: for (i=0; i<1; i++){
206: alpha -=0.1;
207: MatScale(A,alpha);
208: MatPtAP(A,P,MAT_REUSE_MATRIX,fill,&C);
209: }
211: /* Create vector x that is compatible with P */
212: VecCreate(PETSC_COMM_WORLD,&x);
213: MatGetLocalSize(P,&m,&n);
214: VecSetSizes(x,n,PETSC_DECIDE);
215: VecSetFromOptions(x);
216:
217: VecCreate(PETSC_COMM_WORLD,&v3);
218: VecSetSizes(v3,n,PETSC_DECIDE);
219: VecSetFromOptions(v3);
220: VecDuplicate(v3,&v4);
222: norm = 0.0;
223: for (i=0; i<10; i++) {
224: VecSetRandom(x,rdm);
225: MatMult(P,x,v1);
226: MatMult(A,v1,v2); /* v2 = A*P*x */
228: MatMultTranspose(P,v2,v3); /* v3 = Pt*A*P*x */
229: MatMult(C,x,v4); /* v3 = C*x */
230: VecAXPY(v4,none,v3);
231: VecNorm(v4,NORM_1,&norm_tmp);
232: VecNorm(v3,NORM_1,&norm_tmp1);
233: norm_tmp /= norm_tmp1;
234: if (norm_tmp > norm) norm = norm_tmp;
235: }
236: if (norm >= tol && !rank) {
237: PetscPrintf(PETSC_COMM_SELF,"Error: MatPtAP(), |v3 - v4|/|v3|: %g\n",norm);
238: }
239:
240: MatDestroy(C);
241: VecDestroy(v3);
242: VecDestroy(v4);
243: VecDestroy(x);
244:
245: }
247: /* Clean up */
248: MatDestroy(A);
249: PetscRandomDestroy(rdm);
250: VecDestroy(v1);
251: VecDestroy(v2);
252: DADestroy(user.fine.da);
253: DADestroy(user.coarse.da);
254: MatDestroy(P);
256: PetscFinalize();
258: return 0;
259: }