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Sparse Utils: fixing some spelling and alias namespaces
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Using namespace alias is preferable to importing the
content of namespaces since it avoids potential clashes.
Of course using fully specified function names is also fine.

Signed-off-by: Luc Berger-Vergiat <lberge@sandia.gov>
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@lucbv
lucbv committed Jul 14, 2022
1 parent 86ea702 commit 35e2f62
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Showing 4 changed files with 46 additions and 63 deletions.
2 changes: 1 addition & 1 deletion perf_test/sparse/KokkosSparse_block_pcg.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -322,7 +322,7 @@ void run_experiment(
// typedef typename lno_nnz_view_t::value_type lno_t;
// typedef typename lno_view_t::value_type size_type;
// typedef typename scalar_view_t::value_type scalar_t;
KokkosSparse::Impl::kk_create_blockcrs_formated_point_crsmatrix(
KokkosSparse::Impl::kk_create_blockcrs_formatted_point_crsmatrix(
block_size, crsmat.numRows(), crsmat.numCols(), crsmat.graph.row_map,
crsmat.graph.entries, crsmat.values, out_r, out_c, pf_rm, pf_e, pf_v);

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33 changes: 14 additions & 19 deletions perf_test/sparse/KokkosSparse_sptrsv_supernode.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -59,13 +59,8 @@

#include "KokkosSparse_sptrsv_aux.hpp"

using namespace KokkosKernels;
using namespace KokkosKernels::Impl;
using namespace KokkosKernels::Experimental;
using namespace KokkosSparse;
using namespace KokkosSparse::Impl;
using namespace KokkosSparse::Experimental;
using namespace KokkosSparse::PerfTest::Experimental;
namespace KSExp = KokkosSparse::Experimental;
namespace KSPTE = KokkosSparse::PerfTest::Experimental;

enum {
CUSPARSE,
Expand Down Expand Up @@ -213,23 +208,23 @@ int test_sptrsv_perf(std::vector<int> tests, bool verbose,
if (test == SUPERNODAL_NAIVE) {
std::cout << " > create handle for SUPERNODAL_NAIVE" << std::endl
<< std::endl;
khL.create_sptrsv_handle(SPTRSVAlgorithm::SUPERNODAL_NAIVE, nrows,
khL.create_sptrsv_handle(KSExp::SPTRSVAlgorithm::SUPERNODAL_NAIVE, nrows,
true);
khU.create_sptrsv_handle(SPTRSVAlgorithm::SUPERNODAL_NAIVE, nrows,
khU.create_sptrsv_handle(KSExp::SPTRSVAlgorithm::SUPERNODAL_NAIVE, nrows,
true);
} else if (test == SUPERNODAL_DAG) {
std::cout << " > create handle for SUPERNODAL_DAG" << std::endl
<< std::endl;
khL.create_sptrsv_handle(SPTRSVAlgorithm::SUPERNODAL_DAG, nrows,
khL.create_sptrsv_handle(KSExp::SPTRSVAlgorithm::SUPERNODAL_DAG, nrows,
true);
khU.create_sptrsv_handle(SPTRSVAlgorithm::SUPERNODAL_DAG, nrows,
khU.create_sptrsv_handle(KSExp::SPTRSVAlgorithm::SUPERNODAL_DAG, nrows,
true);
} else if (test == SUPERNODAL_SPMV_DAG) {
std::cout << " > create handle for SUPERNODAL_SPMV_DAG" << std::endl
<< std::endl;
khL.create_sptrsv_handle(SPTRSVAlgorithm::SUPERNODAL_SPMV_DAG,
khL.create_sptrsv_handle(KSExp::SPTRSVAlgorithm::SUPERNODAL_SPMV_DAG,
nrows, true);
khU.create_sptrsv_handle(SPTRSVAlgorithm::SUPERNODAL_SPMV_DAG,
khU.create_sptrsv_handle(KSExp::SPTRSVAlgorithm::SUPERNODAL_SPMV_DAG,
nrows, true);
}
// verbose (optional, default is false)
Expand All @@ -255,13 +250,13 @@ int test_sptrsv_perf(std::vector<int> tests, bool verbose,
// graph/dag)
khU.get_sptrsv_handle()->set_column_major(
!khL.get_sptrsv_handle()->is_column_major());
sptrsv_supernodal_symbolic(nsuper, supercols.data(), etree, L.graph,
&khL, L.graph, &khU);
KSExp::sptrsv_supernodal_symbolic(nsuper, supercols.data(), etree, L.graph,
&khL, L.graph, &khU);

// ==============================================
// do numeric compute (copy numerical values from SuperLU data
// structure to our sptrsv data structure)
sptrsv_compute(&khL, L);
KSExp::sptrsv_compute(&khL, L);

// ==============================================
// Preaparing for the first solve
Expand All @@ -285,7 +280,7 @@ int test_sptrsv_perf(std::vector<int> tests, bool verbose,
// ==============================================
// do L solve
timer.reset();
sptrsv_solve(&khL, sol, rhs);
KSExp::sptrsv_solve(&khL, sol, rhs);
Kokkos::fence();
std::cout << " > Lower-TRI: " << std::endl;
std::cout << " Solve Time : " << timer.seconds() << std::endl;
Expand All @@ -297,7 +292,7 @@ int test_sptrsv_perf(std::vector<int> tests, bool verbose,
// Error Check ** on host **
Kokkos::fence();
std::cout << std::endl;
if (!check_errors(tol, A, rhs_host, sol_host)) {
if (!KSPTE::check_errors(tol, A, rhs_host, sol_host)) {
num_failed++;
}

Expand All @@ -309,7 +304,7 @@ int test_sptrsv_perf(std::vector<int> tests, bool verbose,
Kokkos::fence();
for (int i = 0; i < loop; i++) {
timer.reset();
sptrsv_solve(&khL, sol, rhs);
KSExp::sptrsv_solve(&khL, sol, rhs);
Kokkos::fence();
double time = timer.seconds();
ave_time += time;
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6 changes: 3 additions & 3 deletions src/sparse/KokkosSparse_Utils.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@ namespace Impl {
template <typename in_row_view_t, typename in_nnz_view_t,
typename in_val_view_t, typename out_row_view_t,
typename out_nnz_view_t, typename out_val_view_t>
void kk_create_blockcrs_formated_point_crsmatrix(
void kk_create_blockcrs_formatted_point_crsmatrix(
int block_size, size_t num_rows, size_t num_cols, in_row_view_t in_xadj,
in_nnz_view_t in_adj, in_val_view_t in_vals,

Expand Down Expand Up @@ -1958,7 +1958,7 @@ struct MatrixConverter<BlockCRS> {
KokkosSparse::CrsMatrix<scalar_t, lno_t, device, void, size_type>,
typename blockCrsMat_t = KokkosSparse::Experimental::BlockCrsMatrix<
scalar_t, lno_t, device, void, size_type>>
static blockCrsMat_t from_blockcrs_formated_point_crsmatrix(
static blockCrsMat_t from_blockcrs_formatted_point_crsmatrix(
const KokkosSparse::CrsMatrix<scalar_t, lno_t, device, void, size_type>
&mtx,
lno_t block_size) {
Expand All @@ -1972,7 +1972,7 @@ struct MatrixConverter<BSR> {
typename device,
typename bsrMtx_t = KokkosSparse::Experimental::BsrMatrix<
scalar_t, lno_t, device, void, size_type>>
static bsrMtx_t from_blockcrs_formated_point_crsmatrix(
static bsrMtx_t from_blockcrs_formatted_point_crsmatrix(
const KokkosSparse::CrsMatrix<scalar_t, lno_t, device, void, size_type>
&mtx,
lno_t block_size) {
Expand Down
68 changes: 28 additions & 40 deletions unit_test/sparse/Test_Sparse_block_gauss_seidel.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -59,20 +59,10 @@
#include <complex>
#include "KokkosSparse_gauss_seidel.hpp"

// #ifndef kokkos_complex_double
// #define kokkos_complex_double Kokkos::complex<double>
// #define kokkos_complex_float Kokkos::complex<float>
// #endif
using kokkos_complex_double = Kokkos::complex<double>;
using kokkos_complex_float = Kokkos::complex<float>;

typedef Kokkos::complex<double> kokkos_complex_double;
typedef Kokkos::complex<float> kokkos_complex_float;

using namespace KokkosKernels;
using namespace KokkosKernels::Impl;
using namespace KokkosKernels::Experimental;
using namespace KokkosSparse;
using namespace KokkosSparse::Impl;
using namespace KokkosSparse::Experimental;
namespace KSExp = KokkosSparse::Experimental;

namespace Test {

Expand All @@ -92,7 +82,7 @@ struct GSTestParams {

// Note: GS_DEFAULT is same as GS_TEAM and - for blocks - as GS_PERMUTED
// Note: GS_TWOSTAGE and GS_CLUSTER are not supported for blocks
std::vector<GSAlgorithm> gs_algorithms = {GS_DEFAULT};
std::vector<KokkosSparse::GSAlgorithm> gs_algorithms = {KokkosSparse::GS_DEFAULT};
std::vector<size_t> shmem_sizes = {
32128,
2008 // make the shmem small on gpus so that it will test 2 level
Expand Down Expand Up @@ -121,12 +111,11 @@ int run_block_gauss_seidel_1(
typedef typename lno_nnz_view_t::value_type lno_t;
typedef typename scalar_view_t::value_type scalar_t;

constexpr auto format = MatrixTraits<mtx_t>::format;
constexpr auto format = KokkosSparse::Impl::MatrixTraits<mtx_t>::format;

typedef KokkosKernelsHandle<
using KernelHandle = KokkosKernels::Experimental::KokkosKernelsHandle<
size_type, lno_t, scalar_t, typename mtx_t::execution_space,
typename mtx_t::memory_space, typename mtx_t::memory_space>
KernelHandle;
typename mtx_t::memory_space, typename mtx_t::memory_space>;
KernelHandle kh;
kh.set_team_work_size(16);
kh.set_shmem_size(shmem_size);
Expand All @@ -138,33 +127,33 @@ int run_block_gauss_seidel_1(
const int apply_count = 100;

if (!skip_symbolic) {
block_gauss_seidel_symbolic(&kh, num_rows_1, num_cols_1, block_size,
KSExp::block_gauss_seidel_symbolic(&kh, num_rows_1, num_cols_1, block_size,
input_mat.graph.row_map,
input_mat.graph.entries, is_symmetric_graph);
}

if (!skip_numeric) {
block_gauss_seidel_numeric<format>(
KSExp::block_gauss_seidel_numeric<format>(
&kh, num_rows_1, num_cols_1, block_size, input_mat.graph.row_map,
input_mat.graph.entries, input_mat.values, is_symmetric_graph);
}

switch (apply_type) {
case Test::forward_sweep:
forward_sweep_block_gauss_seidel_apply<format>(
KSExp::forward_sweep_block_gauss_seidel_apply<format>(
&kh, num_rows_1, num_cols_1, block_size, input_mat.graph.row_map,
input_mat.graph.entries, input_mat.values, x_vector, y_vector, false,
true, omega, apply_count);
break;
case Test::backward_sweep:
backward_sweep_block_gauss_seidel_apply<format>(
KSExp::backward_sweep_block_gauss_seidel_apply<format>(
&kh, num_rows_1, num_cols_1, block_size, input_mat.graph.row_map,
input_mat.graph.entries, input_mat.values, x_vector, y_vector, false,
true, omega, apply_count);
break;
case Test::symmetric:
default:
symmetric_block_gauss_seidel_apply<format>(
KSExp::symmetric_block_gauss_seidel_apply<format>(
&kh, num_rows_1, num_cols_1, block_size, input_mat.graph.row_map,
input_mat.graph.entries, input_mat.values, x_vector, y_vector, false,
true, omega, apply_count);
Expand All @@ -183,9 +172,9 @@ void test_block_gauss_seidel_rank1(lno_t numRows, size_type nnz,
lno_t bandwidth, lno_t row_size_variance) {
using namespace Test;
srand(245);
typedef
typename KokkosSparse::CrsMatrix<scalar_t, lno_t, device, void, size_type>
crsMat_t;
using crsMat_t =
typename KokkosSparse::CrsMatrix<scalar_t, lno_t, device, void, size_type>;
using MatrixConverter = KokkosSparse::Impl::MatrixConverter<mtx_format>;

typedef typename device::execution_space exec_space;
typedef typename crsMat_t::StaticCrsGraphType graph_t;
Expand Down Expand Up @@ -213,15 +202,15 @@ void test_block_gauss_seidel_rank1(lno_t numRows, size_type nnz,
// this makes consecutive 5 rows to have same columns.
// it will add scalar 0's for those entries that does not exists.
// the result is still a point crs matrix.
KokkosSparse::Impl::kk_create_blockcrs_formated_point_crsmatrix(
KokkosSparse::Impl::kk_create_blockcrs_formatted_point_crsmatrix(
block_size, crsmat.numRows(), crsmat.numCols(), crsmat.graph.row_map,
crsmat.graph.entries, crsmat.values, out_r, out_c, pf_rm, pf_e, pf_v);
graph_t static_graph2(pf_e, pf_rm);
crsMat_t crsmat2("CrsMatrix2", out_c, pf_v, static_graph2);

// this converts the previous generated matrix to block matrix.
auto input_mat =
MatrixConverter<mtx_format>::from_blockcrs_formated_point_crsmatrix(
MatrixConverter::from_blockcrs_formatted_point_crsmatrix(
crsmat2, block_size);

lno_t nv = ((crsmat2.numRows() + block_size - 1) / block_size) * block_size;
Expand Down Expand Up @@ -270,9 +259,9 @@ void test_block_gauss_seidel_rank2(lno_t numRows, size_type nnz,
lno_t bandwidth, lno_t row_size_variance) {
using namespace Test;
srand(245);
typedef
typename KokkosSparse::CrsMatrix<scalar_t, lno_t, device, void, size_type>
crsMat_t;
using crsMat_t =
typename KokkosSparse::CrsMatrix<scalar_t, lno_t, device, void, size_type>;
using MatrixConverter = KokkosSparse::Impl::MatrixConverter<mtx_format>;

typedef typename device::execution_space exec_space;
typedef typename crsMat_t::StaticCrsGraphType graph_t;
Expand Down Expand Up @@ -301,14 +290,14 @@ void test_block_gauss_seidel_rank2(lno_t numRows, size_type nnz,
// this makes consecutive 5 rows to have same columns.
// it will add scalar 0's for those entries that does not exists.
// the result is still a point crs matrix.
KokkosSparse::Impl::kk_create_blockcrs_formated_point_crsmatrix(
KokkosSparse::Impl::kk_create_blockcrs_formatted_point_crsmatrix(
block_size, crsmat.numRows(), crsmat.numCols(), crsmat.graph.row_map,
crsmat.graph.entries, crsmat.values, out_r, out_c, pf_rm, pf_e, pf_v);
graph_t static_graph2(pf_e, pf_rm);
crsMat_t crsmat2("CrsMatrix2", out_c, pf_v, static_graph2);

auto input_mat =
MatrixConverter<mtx_format>::from_blockcrs_formated_point_crsmatrix(
MatrixConverter::from_blockcrs_formatted_point_crsmatrix(
crsmat2, block_size);

lno_t nv = ((crsmat2.numRows() + block_size - 1) / block_size) * block_size;
Expand Down Expand Up @@ -385,33 +374,32 @@ void test_block_gauss_seidel_empty() {
typedef typename graph_t::row_map_type::non_const_type row_map_type;
typedef typename graph_t::entries_type::non_const_type entries_type;
typedef typename crsMat_t::values_type::non_const_type scalar_view_t;
typedef KokkosKernelsHandle<
using KernelHandle = KokkosKernels::Experimental::KokkosKernelsHandle<
size_type, lno_t, scalar_t, typename device::execution_space,
typename device::memory_space, typename device::memory_space>
KernelHandle;
typename device::memory_space, typename device::memory_space>;
// The rowmap of a zero-row matrix can be length 0 or 1, so Gauss-Seidel
// should work with both (the setup and apply are essentially no-ops but they
// shouldn't crash or throw exceptions) For this test, create size-0 and
// size-1 rowmaps separately. Check also 5x5 matrix with empty rows (0-nnz),
// which can trigger different bugs.
for (const int rowmapLen : {0, 1, 5}) {
KernelHandle kh;
kh.create_gs_handle(GS_DEFAULT);
kh.create_gs_handle(KokkosSparse::GS_DEFAULT);
const auto num_rows = KOKKOSKERNELS_MACRO_MAX(0, rowmapLen - 1);
const lno_t block_size = 1; // irrelevant (no values here)
// initialized to 0
row_map_type rowmap("Rowmap", rowmapLen);
entries_type entries("Entries", 0);
scalar_view_t values("Values", 0);
// also, make sure graph symmetrization doesn't crash on zero rows
block_gauss_seidel_symbolic(&kh, num_rows, num_rows, block_size, rowmap,
KSExp::block_gauss_seidel_symbolic(&kh, num_rows, num_rows, block_size, rowmap,
entries, false);
block_gauss_seidel_numeric<mtx_format>(&kh, num_rows, num_rows, block_size,
KSExp::block_gauss_seidel_numeric<mtx_format>(&kh, num_rows, num_rows, block_size,
rowmap, entries, values, false);
scalar_view_t x("X", num_rows);
scalar_view_t y("Y", num_rows);
scalar_t omega(0.9);
symmetric_block_gauss_seidel_apply<mtx_format>(
KSExp::symmetric_block_gauss_seidel_apply<mtx_format>(
&kh, num_rows, num_rows, block_size, rowmap, entries, values, x, y,
false, true, omega, 3);
kh.destroy_gs_handle();
Expand Down

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