static void apply( const tensor_type & tensor ,
const MatrixValue * const a ,
const VectorValue * const x ,
VectorValue * const y )
{
const size_type nDim = tensor.dimension();
// Loop over i
for ( size_type i = 0; i < nDim; ++i) {
VectorValue ytmp = 0;
// Loop over k for this i
const size_type nk = tensor.num_k(i);
const size_type kBeg = tensor.k_begin(i);
const size_type kEnd = kBeg + nk;
for (size_type kEntry = kBeg; kEntry < kEnd; ++kEntry) {
const size_type k = tensor.k_coord(kEntry);
const MatrixValue ak = a[k];
const VectorValue xk = x[k];
// Loop over j for this i,k
const size_type nj = tensor.num_j(kEntry);
const size_type jBeg = tensor.j_begin(kEntry);
const size_type jEnd = jBeg + nj;
for (size_type jEntry = jBeg; jEntry < jEnd; ++jEntry) {
const size_type j = tensor.j_coord(jEntry);
ytmp += tensor.value(jEntry) * ( a[j] * xk + ak * x[j] );
}
}
y[i] += ytmp ;
}
}
KOKKOS_INLINE_FUNCTION
static void apply( const tensor_type & tensor ,
const MatrixValue * const a ,
const VectorValue * const x ,
VectorValue * const y )
{
const size_type nk = tensor.num_k();
// Loop over k
for ( size_type k = 0; k < nk; ++k) {
const MatrixValue ak = a[k];
const VectorValue xk = x[k];
// Loop over j for this k
const size_type nj = tensor.num_j(k);
const size_type jBeg = tensor.j_begin(k);
const size_type jEnd = jBeg + nj;
for (size_type jEntry = jBeg; jEntry < jEnd; ++jEntry) {
const size_type j = tensor.j_coord(jEntry);
VectorValue tmp = a[j] * xk + ak * x[j];
// Loop over i for this k,j
const size_type ni = tensor.num_i(jEntry);
const size_type iBeg = tensor.i_begin(jEntry);
const size_type iEnd = iBeg + ni;
for (size_type iEntry = iBeg; iEntry < iEnd; ++iEntry) {
const size_type i = tensor.i_coord(iEntry);
y[i] += tensor.value(iEntry) * tmp;
}
}
}
}
static void apply( const tensor_type & tensor ,
const MatrixValue * const a ,
const VectorValue * const x ,
VectorValue * const y )
{
// const int max_size = 10;
// MatrixValue ax[max_size][max_size];
const size_type nBlock = tensor.num_coord();
// Loop over coordinate blocks
size_type value_entry = 0;
for ( size_type block = 0; block < nBlock; ++block) {
const size_type i_begin = tensor.get_i_begin(block);
const size_type j_begin = tensor.get_j_begin(block);
const size_type k_begin = tensor.get_k_begin(block);
const size_type i_size = tensor.get_i_size(block);
const size_type j_size = tensor.get_j_size(block);
const size_type k_size = tensor.get_k_size(block);
VectorValue * const y_block = y + i_begin;
const MatrixValue * const a_block = a + j_begin;
const VectorValue * const x_block = x + k_begin;
// // Precompute a*x outer product
// for (size_type j=0; j<j_size; ++j) {
// for (size_type k=0; k<k_size; ++k) {
// ax[j][k] = a_block[j]*x_block[k];
// }
// }
/*
// Compute y_i = \sum_{j,k} c_{ijk} * a_j * x_k
for (size_type i=0; i<i_size; ++i) {
VectorValue ytmp = 0;
for (size_type j=0; j<j_size; ++j) {
const size_type imj = i-j;
const size_type ipj = i+j+1;
const size_type k_beg = 0 <= imj ? imj : -imj;
const size_type k_end = k_size <= ipj ? k_size : ipj;
const size_type k0 = k_beg % 2 == (i+j) % 2 ? k_beg : k_beg+1;
for (size_type k=k0; k<k_end; ++k) {
//ytmp += tensor.value(value_entry++) * ax[j][k];
ytmp += tensor.value(value_entry++) * ( a_block[j] * x_block[k] );
}
}
y_block[i] += ytmp ;
}
*/
// Compute y_i = \sum_{j,k} c_{ijk} * a_j * x_k
for (size_type i=0; i<i_size; ++i) {
VectorValue ytmp = 0;
for (size_type j=0; j<j_size; ++j) {
for (size_type k=((i+j)%2); k<k_size; k+=2) {
ytmp += tensor.value(value_entry++) * ( a_block[j] * x_block[k] );
}
}
y_block[i] += ytmp ;
}
}
}
