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fixed a mistake in the calculate_total_cols kernels. #295

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merged 1 commit into from
Apr 24, 2019
Merged

fixed a mistake in the calculate_total_cols kernels. #295

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10 changes: 6 additions & 4 deletions cuda/matrix/csr_kernels.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -40,8 +40,8 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <ginkgo/core/base/math.hpp>
#include <ginkgo/core/matrix/coo.hpp>
#include <ginkgo/core/matrix/dense.hpp>
#include <ginkgo/core/matrix/sellp.hpp>
#include <ginkgo/core/matrix/ell.hpp>
#include <ginkgo/core/matrix/sellp.hpp>


#include "core/matrix/dense_kernels.hpp"
Expand Down Expand Up @@ -1138,7 +1138,7 @@ __global__ __launch_bounds__(default_block_size) void fill_in_ell(
}


} // namespace kernel
} // namespace kernel


template <typename ValueType, typename IndexType>
Expand Down Expand Up @@ -1187,10 +1187,11 @@ __global__ __launch_bounds__(default_block_size) void reduce_max_nnz_per_slice(
constexpr auto warp_size = cuda_config::warp_size;
const auto warpid = tidx / warp_size;
const auto tid_in_warp = tidx % warp_size;
const auto slice_num = ceildiv(num_rows, slice_size);

size_type thread_result = 0;
for (auto i = tid_in_warp; i < slice_size; i += warp_size) {
if (warpid * warp_size + i < num_rows) {
if (warpid * slice_size + i < num_rows) {
thread_result =
max(thread_result, nnz_per_row[warpid * slice_size + i]);
}
Expand All @@ -1202,7 +1203,7 @@ __global__ __launch_bounds__(default_block_size) void reduce_max_nnz_per_slice(
warp_tile, thread_result,
[](const size_type &a, const size_type &b) { return max(a, b); });

if (tid_in_warp == 0) {
if (tid_in_warp == 0 && warpid < slice_num) {
result[warpid] = ceildiv(warp_result, stride_factor) * stride_factor;
}
}
Expand Down Expand Up @@ -1250,6 +1251,7 @@ void calculate_total_cols(std::shared_ptr<const CudaExecutor> exec,
as_cuda_type(nnz_per_row.get_const_data()),
as_cuda_type(max_nnz_per_slice.get_data()));

grid_dim = ceildiv(slice_num, default_block_size);
auto block_results = Array<size_type>(exec, grid_dim);

kernel::reduce_total_cols<<<grid_dim, default_block_size,
Expand Down
11 changes: 7 additions & 4 deletions cuda/matrix/dense_kernels.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -878,12 +878,13 @@ __global__ __launch_bounds__(default_block_size) void reduce_max_nnz_per_slice(
constexpr auto warp_size = cuda_config::warp_size;
const auto warpid = tidx / warp_size;
const auto tid_in_warp = tidx % warp_size;
const auto slice_num = ceildiv(num_rows, slice_size);

size_type thread_result = 0;
for (auto i = tid_in_warp; i < slice_size; i += warp_size) {
if (warpid * warp_size + i < num_rows) {
if (warpid * slice_size + i < num_rows) {
thread_result =
max(thread_result, nnz_per_row[warpid * warp_size + i]);
max(thread_result, nnz_per_row[warpid * slice_size + i]);
}
}

Expand All @@ -893,7 +894,7 @@ __global__ __launch_bounds__(default_block_size) void reduce_max_nnz_per_slice(
warp_tile, thread_result,
[](const size_type &a, const size_type &b) { return max(a, b); });

if (tid_in_warp == 0) {
if (tid_in_warp == 0 && warpid < slice_num) {
result[warpid] = ceildiv(warp_result, stride_factor) * stride_factor;
}
}
Expand Down Expand Up @@ -933,13 +934,15 @@ void calculate_total_cols(std::shared_ptr<const CudaExecutor> exec,

auto max_nnz_per_slice = Array<size_type>(exec, slice_num);

const auto grid_dim = ceildiv(slice_num, default_block_size);
auto grid_dim =
ceildiv(slice_num * cuda_config::warp_size, default_block_size);

kernel::reduce_max_nnz_per_slice<<<grid_dim, default_block_size>>>(
num_rows, slice_size, stride_factor,
as_cuda_type(nnz_per_row.get_const_data()),
as_cuda_type(max_nnz_per_slice.get_data()));

grid_dim = ceildiv(slice_num, default_block_size);
auto block_results = Array<size_type>(exec, grid_dim);

kernel::reduce_total_cols<<<grid_dim, default_block_size,
Expand Down