Lines Matching refs:viennaclstruct

27   Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL *)A->spptr;
37           if (!viennaclstruct->compressed_mat) viennaclstruct->compressed_mat = new ViennaCLCompressedAIJMatrix();
54           viennaclstruct->compressed_mat->set(row_buffer.get(), row_indices.get(), col_buffer.get(), a->a, A->rmap->n, A->cmap->n, a->compressedrow.nrows, a->nz);
57           if (!viennaclstruct->mat) viennaclstruct->mat = new ViennaCLAIJMatrix();
70           viennaclstruct->mat->set(row_buffer.get(), col_buffer.get(), a->a, A->rmap->n, A->cmap->n, a->nz);
79       if (viennaclstruct->tempvec) {
80         if (viennaclstruct->tempvec->size() != static_cast<std::size_t>(A->rmap->n)) {
81           delete (ViennaCLVector *)viennaclstruct->tempvec;
82           viennaclstruct->tempvec = new ViennaCLVector(A->rmap->n);
84           viennaclstruct->tempvec->clear();
87         viennaclstruct->tempvec = new ViennaCLVector(A->rmap->n);
100   Mat_SeqAIJViennaCL *viennaclstruct = (Mat_SeqAIJViennaCL *)A->spptr;
161       viennacl::backend::memory_read(viennaclstruct->mat->handle(), 0, sizeof(PetscScalar) * viennaclstruct->mat->nnz(), a->a);
176   Mat_SeqAIJViennaCL   *viennaclstruct = (Mat_SeqAIJViennaCL *)A->spptr;
189         *ygpu = viennacl::linalg::prod(*viennaclstruct->compressed_mat, *xgpu);
191         *ygpu = viennacl::linalg::prod(*viennaclstruct->mat, *xgpu);
210   Mat_SeqAIJViennaCL   *viennaclstruct = (Mat_SeqAIJViennaCL *)A->spptr;
223       if (a->compressedrow.use) *viennaclstruct->tempvec = viennacl::linalg::prod(*viennaclstruct->compressed_mat, *xgpu);
224       else *viennaclstruct->tempvec = viennacl::linalg::prod(*viennaclstruct->mat, *xgpu);
225       if (zz != yy) *zgpu = *ygpu + *viennaclstruct->tempvec;
226       else *zgpu += *viennaclstruct->tempvec;