Actual source code: baijsolvtrannat5.c

  1: #include <../src/mat/impls/baij/seq/baij.h>

  3: PetscErrorCode MatSolveTranspose_SeqBAIJ_5_NaturalOrdering_inplace(Mat A, Vec bb, Vec xx)
  4: {
  5:   Mat_SeqBAIJ     *a    = (Mat_SeqBAIJ *)A->data;
  6:   const PetscInt  *diag = a->diag, n = a->mbs, *vi, *ai = a->i, *aj = a->j;
  7:   PetscInt         i, nz, idx, idt, oidx;
  8:   const MatScalar *aa = a->a, *v;
  9:   PetscScalar      s1, s2, s3, s4, s5, x1, x2, x3, x4, x5, *x;

 11:   VecCopy(bb, xx);
 12:   VecGetArray(xx, &x);

 14:   /* forward solve the U^T */
 15:   idx = 0;
 16:   for (i = 0; i < n; i++) {
 17:     v = aa + 25 * diag[i];
 18:     /* multiply by the inverse of the block diagonal */
 19:     x1 = x[idx];
 20:     x2 = x[1 + idx];
 21:     x3 = x[2 + idx];
 22:     x4 = x[3 + idx];
 23:     x5 = x[4 + idx];
 24:     s1 = v[0] * x1 + v[1] * x2 + v[2] * x3 + v[3] * x4 + v[4] * x5;
 25:     s2 = v[5] * x1 + v[6] * x2 + v[7] * x3 + v[8] * x4 + v[9] * x5;
 26:     s3 = v[10] * x1 + v[11] * x2 + v[12] * x3 + v[13] * x4 + v[14] * x5;
 27:     s4 = v[15] * x1 + v[16] * x2 + v[17] * x3 + v[18] * x4 + v[19] * x5;
 28:     s5 = v[20] * x1 + v[21] * x2 + v[22] * x3 + v[23] * x4 + v[24] * x5;
 29:     v += 25;

 31:     vi = aj + diag[i] + 1;
 32:     nz = ai[i + 1] - diag[i] - 1;
 33:     while (nz--) {
 34:       oidx = 5 * (*vi++);
 35:       x[oidx] -= v[0] * s1 + v[1] * s2 + v[2] * s3 + v[3] * s4 + v[4] * s5;
 36:       x[oidx + 1] -= v[5] * s1 + v[6] * s2 + v[7] * s3 + v[8] * s4 + v[9] * s5;
 37:       x[oidx + 2] -= v[10] * s1 + v[11] * s2 + v[12] * s3 + v[13] * s4 + v[14] * s5;
 38:       x[oidx + 3] -= v[15] * s1 + v[16] * s2 + v[17] * s3 + v[18] * s4 + v[19] * s5;
 39:       x[oidx + 4] -= v[20] * s1 + v[21] * s2 + v[22] * s3 + v[23] * s4 + v[24] * s5;
 40:       v += 25;
 41:     }
 42:     x[idx]     = s1;
 43:     x[1 + idx] = s2;
 44:     x[2 + idx] = s3;
 45:     x[3 + idx] = s4;
 46:     x[4 + idx] = s5;
 47:     idx += 5;
 48:   }
 49:   /* backward solve the L^T */
 50:   for (i = n - 1; i >= 0; i--) {
 51:     v   = aa + 25 * diag[i] - 25;
 52:     vi  = aj + diag[i] - 1;
 53:     nz  = diag[i] - ai[i];
 54:     idt = 5 * i;
 55:     s1  = x[idt];
 56:     s2  = x[1 + idt];
 57:     s3  = x[2 + idt];
 58:     s4  = x[3 + idt];
 59:     s5  = x[4 + idt];
 60:     while (nz--) {
 61:       idx = 5 * (*vi--);
 62:       x[idx] -= v[0] * s1 + v[1] * s2 + v[2] * s3 + v[3] * s4 + v[4] * s5;
 63:       x[idx + 1] -= v[5] * s1 + v[6] * s2 + v[7] * s3 + v[8] * s4 + v[9] * s5;
 64:       x[idx + 2] -= v[10] * s1 + v[11] * s2 + v[12] * s3 + v[13] * s4 + v[14] * s5;
 65:       x[idx + 3] -= v[15] * s1 + v[16] * s2 + v[17] * s3 + v[18] * s4 + v[19] * s5;
 66:       x[idx + 4] -= v[20] * s1 + v[21] * s2 + v[22] * s3 + v[23] * s4 + v[24] * s5;
 67:       v -= 25;
 68:     }
 69:   }
 70:   VecRestoreArray(xx, &x);
 71:   PetscLogFlops(2.0 * 25 * (a->nz) - 5.0 * A->cmap->n);
 72:   return 0;
 73: }

 75: PetscErrorCode MatSolveTranspose_SeqBAIJ_5_NaturalOrdering(Mat A, Vec bb, Vec xx)
 76: {
 77:   Mat_SeqBAIJ     *a = (Mat_SeqBAIJ *)A->data;
 78:   const PetscInt   n = a->mbs, *vi, *ai = a->i, *aj = a->j, *diag = a->diag;
 79:   PetscInt         nz, idx, idt, j, i, oidx;
 80:   const PetscInt   bs = A->rmap->bs, bs2 = a->bs2;
 81:   const MatScalar *aa = a->a, *v;
 82:   PetscScalar      s1, s2, s3, s4, s5, x1, x2, x3, x4, x5, *x;

 84:   VecCopy(bb, xx);
 85:   VecGetArray(xx, &x);

 87:   /* forward solve the U^T */
 88:   idx = 0;
 89:   for (i = 0; i < n; i++) {
 90:     v = aa + bs2 * diag[i];
 91:     /* multiply by the inverse of the block diagonal */
 92:     x1 = x[idx];
 93:     x2 = x[1 + idx];
 94:     x3 = x[2 + idx];
 95:     x4 = x[3 + idx];
 96:     x5 = x[4 + idx];
 97:     s1 = v[0] * x1 + v[1] * x2 + v[2] * x3 + v[3] * x4 + v[4] * x5;
 98:     s2 = v[5] * x1 + v[6] * x2 + v[7] * x3 + v[8] * x4 + v[9] * x5;
 99:     s3 = v[10] * x1 + v[11] * x2 + v[12] * x3 + v[13] * x4 + v[14] * x5;
100:     s4 = v[15] * x1 + v[16] * x2 + v[17] * x3 + v[18] * x4 + v[19] * x5;
101:     s5 = v[20] * x1 + v[21] * x2 + v[22] * x3 + v[23] * x4 + v[24] * x5;
102:     v -= bs2;

104:     vi = aj + diag[i] - 1;
105:     nz = diag[i] - diag[i + 1] - 1;
106:     for (j = 0; j > -nz; j--) {
107:       oidx = bs * vi[j];
108:       x[oidx] -= v[0] * s1 + v[1] * s2 + v[2] * s3 + v[3] * s4 + v[4] * s5;
109:       x[oidx + 1] -= v[5] * s1 + v[6] * s2 + v[7] * s3 + v[8] * s4 + v[9] * s5;
110:       x[oidx + 2] -= v[10] * s1 + v[11] * s2 + v[12] * s3 + v[13] * s4 + v[14] * s5;
111:       x[oidx + 3] -= v[15] * s1 + v[16] * s2 + v[17] * s3 + v[18] * s4 + v[19] * s5;
112:       x[oidx + 4] -= v[20] * s1 + v[21] * s2 + v[22] * s3 + v[23] * s4 + v[24] * s5;
113:       v -= bs2;
114:     }
115:     x[idx]     = s1;
116:     x[1 + idx] = s2;
117:     x[2 + idx] = s3;
118:     x[3 + idx] = s4;
119:     x[4 + idx] = s5;
120:     idx += bs;
121:   }
122:   /* backward solve the L^T */
123:   for (i = n - 1; i >= 0; i--) {
124:     v   = aa + bs2 * ai[i];
125:     vi  = aj + ai[i];
126:     nz  = ai[i + 1] - ai[i];
127:     idt = bs * i;
128:     s1  = x[idt];
129:     s2  = x[1 + idt];
130:     s3  = x[2 + idt];
131:     s4  = x[3 + idt];
132:     s5  = x[4 + idt];
133:     for (j = 0; j < nz; j++) {
134:       idx = bs * vi[j];
135:       x[idx] -= v[0] * s1 + v[1] * s2 + v[2] * s3 + v[3] * s4 + v[4] * s5;
136:       x[idx + 1] -= v[5] * s1 + v[6] * s2 + v[7] * s3 + v[8] * s4 + v[9] * s5;
137:       x[idx + 2] -= v[10] * s1 + v[11] * s2 + v[12] * s3 + v[13] * s4 + v[14] * s5;
138:       x[idx + 3] -= v[15] * s1 + v[16] * s2 + v[17] * s3 + v[18] * s4 + v[19] * s5;
139:       x[idx + 4] -= v[20] * s1 + v[21] * s2 + v[22] * s3 + v[23] * s4 + v[24] * s5;
140:       v += bs2;
141:     }
142:   }
143:   VecRestoreArray(xx, &x);
144:   PetscLogFlops(2.0 * bs2 * (a->nz) - bs * A->cmap->n);
145:   return 0;
146: }