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Common cleanup #1431

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merged 5 commits into from
Jun 7, 2022
Merged

Common cleanup #1431

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0104ec1
common clean-up: removing sparse and graph features from common
lucbv Jun 2, 2022
e2a88fc
Applying clang-format
lucbv Jun 2, 2022
be71d80
common cleanup: fixing issue with sparse performance tests
lucbv Jun 6, 2022
a647349
common cleanup: fixing an issue with a default template redefinition
lucbv Jun 6, 2022
142577d
common cleanup: applying clang-format
lucbv Jun 7, 2022
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4 changes: 2 additions & 2 deletions example/gmres/ex_real_A.cpp
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Expand Up @@ -43,7 +43,7 @@
*/

#include <math.h>
#include "KokkosKernels_IOUtils.hpp"
#include "KokkosSparse_IOUtils.hpp"
#include <Kokkos_Core.hpp>
#include <Kokkos_Random.hpp>
#include <KokkosBlas.hpp>
Expand Down Expand Up @@ -117,7 +117,7 @@ int main(int argc, char* argv[]) {
{
// Read in a matrix Market file and use it to test the Kokkos Operator.
KokkosSparse::CrsMatrix<ST, OT, EXSP> A =
KokkosKernels::Impl::read_kokkos_crst_matrix<
KokkosSparse::Impl::read_kokkos_crst_matrix<
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@brian-kelley brian-kelley Jun 3, 2022 •
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Since you're moving this into a different namespace anyway, maybe it should just be in KokkosSparse::? I think it's pretty common enough for external users to need a MatrixMarket reader.

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Yeah, that is how it should be, I thought I had changed the namespace but maybe I forgot...
I should really re-import everything back and mark as deprecated but that will be pretty messy : (

KokkosSparse::CrsMatrix<ST, OT, EXSP>>(filename.c_str());

int n = A.numRows();
Expand Down
3 changes: 2 additions & 1 deletion example/gmres/test_cmplx_A.cpp
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Expand Up @@ -44,6 +44,7 @@

#include <math.h>
#include "KokkosKernels_IOUtils.hpp"
#include "KokkosSparse_IOUtils.hpp"
#include <Kokkos_Core.hpp>
#include <Kokkos_Random.hpp>
#include <KokkosBlas.hpp>
Expand Down Expand Up @@ -77,7 +78,7 @@ int main(int /*argc*/, char** /*argv[]*/) {
{
// Read in a matrix Market file and use it to test the Kokkos Operator.
KokkosSparse::CrsMatrix<ST, OT, EXSP> A =
KokkosKernels::Impl::read_kokkos_crst_matrix<
KokkosSparse::Impl::read_kokkos_crst_matrix<
KokkosSparse::CrsMatrix<ST, OT, EXSP>>(filename.c_str());

int n = A.numRows();
Expand Down
5 changes: 3 additions & 2 deletions example/gmres/test_prec.cpp
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Expand Up @@ -48,6 +48,7 @@
#include <Kokkos_Random.hpp>
#include <KokkosBlas.hpp>
#include <KokkosSparse_spmv.hpp>
#include "KokkosSparse_IOUtils.hpp"

int main(int argc, char* argv[]) {
typedef double ST;
Expand Down Expand Up @@ -114,13 +115,13 @@ int main(int argc, char* argv[]) {
{
// Generate a diagonal matrix with entries 1, 2, ...., 1000 and its inverse.
KokkosSparse::CrsMatrix<ST, OT, EXSP> A =
KokkosKernels::Impl::kk_generate_diag_matrix<
KokkosSparse::Impl::kk_generate_diag_matrix<
KokkosSparse::CrsMatrix<ST, OT, EXSP>>(n);
KokkosSparse::Experimental::MatrixPrec<ST, Kokkos::LayoutLeft, EXSP, OT>*
myPrec =
new KokkosSparse::Experimental::MatrixPrec<ST, Kokkos::LayoutLeft,
EXSP, OT>(
KokkosKernels::Impl::kk_generate_diag_matrix<
KokkosSparse::Impl::kk_generate_diag_matrix<
KokkosSparse::CrsMatrix<ST, OT, EXSP>>(n, true));

ViewVectorType X(Kokkos::view_alloc(Kokkos::WithoutInitializing, "X"),
Expand Down
3 changes: 2 additions & 1 deletion example/gmres/test_real_A.cpp
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Expand Up @@ -44,6 +44,7 @@

#include <math.h>
#include "KokkosKernels_IOUtils.hpp"
#include "KokkosSparse_IOUtils.hpp"
#include <Kokkos_Core.hpp>
#include <Kokkos_Random.hpp>
#include <KokkosBlas.hpp>
Expand Down Expand Up @@ -89,7 +90,7 @@ int main(int /*argc*/, char** /*argv[]*/) {
cOT diagDominance = 1;
nnz = 10 * numRows;
sp_matrix_type A =
KokkosKernels::Impl::kk_generate_diagonally_dominant_sparse_matrix<
KokkosSparse::Impl::kk_generate_diagonally_dominant_sparse_matrix<
sp_matrix_type>(numRows, numCols, nnz, 0, ncOT(0.01 * numRows),
diagDominance);

Expand Down
100 changes: 54 additions & 46 deletions example/wiki/sparse/KokkosSparse_wiki_gauss_seidel.cpp
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Expand Up @@ -2,88 +2,96 @@
#include "KokkosKernels_default_types.hpp"
#include "KokkosKernels_Handle.hpp"
#include "KokkosKernels_IOUtils.hpp"
#include "KokkosSparse_IOUtils.hpp"
#include "KokkosSparse_spmv.hpp"
#include "KokkosSparse_CrsMatrix.hpp"
#include "KokkosSparse_gauss_seidel.hpp"
#include "KokkosBlas1_nrm2.hpp"

//Parallel Gauss-Seidel Preconditioner/Smoother
// Parallel Gauss-Seidel Preconditioner/Smoother
// -Uses graph coloring to find independent row sets,
// and applies GS to each set in parallel
// -Here, use to solve a diagonally dominant linear system directly.

//Helper to print out colors in the shape of the grid
int main()
{
using Scalar = default_scalar;
using Mag = Kokkos::ArithTraits<Scalar>::mag_type;
using Ordinal = default_lno_t;
using Offset = default_size_type;
// Helper to print out colors in the shape of the grid
int main() {
using Scalar = default_scalar;
using Mag = Kokkos::ArithTraits<Scalar>::mag_type;
using Ordinal = default_lno_t;
using Offset = default_size_type;
using ExecSpace = Kokkos::DefaultExecutionSpace;
using MemSpace = typename ExecSpace::memory_space;
using Device = Kokkos::Device<ExecSpace, MemSpace>;
using Handle = KokkosKernels::Experimental::
KokkosKernelsHandle<Offset, Ordinal, default_scalar, ExecSpace, MemSpace, MemSpace>;
using Matrix = KokkosSparse::CrsMatrix<Scalar, Ordinal, Device, void, Offset>;
using Vector = typename Matrix::values_type;
using MemSpace = typename ExecSpace::memory_space;
using Device = Kokkos::Device<ExecSpace, MemSpace>;
using Handle = KokkosKernels::Experimental::KokkosKernelsHandle<
Offset, Ordinal, default_scalar, ExecSpace, MemSpace, MemSpace>;
using Matrix = KokkosSparse::CrsMatrix<Scalar, Ordinal, Device, void, Offset>;
using Vector = typename Matrix::values_type;
constexpr Ordinal numRows = 10000;
const Scalar one = Kokkos::ArithTraits<Scalar>::one();
const Mag magOne = Kokkos::ArithTraits<Mag>::one();
//Solve tolerance
const Scalar one = Kokkos::ArithTraits<Scalar>::one();
const Mag magOne = Kokkos::ArithTraits<Mag>::one();
// Solve tolerance
const Mag tolerance = 1e-6 * magOne;
Kokkos::initialize();
{
//Generate a square, strictly diagonally dominant, but nonsymmetric matrix on which Gauss-Seidel should converge.
//Get approx. 20 entries per row
//Diagonals are 2x the absolute sum of all other entries.
// Generate a square, strictly diagonally dominant, but nonsymmetric matrix
// on which Gauss-Seidel should converge. Get approx. 20 entries per row
// Diagonals are 2x the absolute sum of all other entries.
Offset nnz = numRows * 20;
Matrix A = KokkosKernels::Impl::kk_generate_diagonally_dominant_sparse_matrix<Matrix>(numRows, numRows, nnz, 2, 100, 1.05 * one);
std::cout << "Generated a matrix with " << numRows << " rows/cols, and " << nnz << " entries.\n";
//Create a kernel handle, then a Gauss-Seidel handle with the default algorithm
Matrix A =
KokkosSparse::Impl::kk_generate_diagonally_dominant_sparse_matrix<
Matrix>(numRows, numRows, nnz, 2, 100, 1.05 * one);
std::cout << "Generated a matrix with " << numRows << " rows/cols, and "
<< nnz << " entries.\n";
// Create a kernel handle, then a Gauss-Seidel handle with the default
// algorithm
Handle handle;
handle.create_gs_handle(KokkosSparse::GS_DEFAULT);
//Set up Gauss-Seidel for the graph (matrix sparsity pattern)
KokkosSparse::Experimental::gauss_seidel_symbolic(&handle, numRows, numRows, A.graph.row_map, A.graph.entries, false);
//Set up Gauss-Seidel for the matrix values (numeric)
//Another matrix with the same sparsity pattern could re-use the handle and symbolic phase, and only call numeric.
KokkosSparse::Experimental::gauss_seidel_numeric(&handle, numRows, numRows, A.graph.row_map, A.graph.entries, A.values, false);
//Now, preconditioner is ready to use. Set up an unknown vector (uninitialized) and randomized right-hand-side vector.
// Set up Gauss-Seidel for the graph (matrix sparsity pattern)
KokkosSparse::Experimental::gauss_seidel_symbolic(
&handle, numRows, numRows, A.graph.row_map, A.graph.entries, false);
// Set up Gauss-Seidel for the matrix values (numeric)
// Another matrix with the same sparsity pattern could re-use the handle and
// symbolic phase, and only call numeric.
KokkosSparse::Experimental::gauss_seidel_numeric(
&handle, numRows, numRows, A.graph.row_map, A.graph.entries, A.values,
false);
// Now, preconditioner is ready to use. Set up an unknown vector
// (uninitialized) and randomized right-hand-side vector.
Vector x(Kokkos::view_alloc(Kokkos::WithoutInitializing, "x"), numRows);
Vector b(Kokkos::view_alloc(Kokkos::WithoutInitializing, "b"), numRows);
Vector res(Kokkos::view_alloc(Kokkos::WithoutInitializing, "res"), numRows);
auto bHost = Kokkos::create_mirror_view(b);
for(Ordinal i = 0; i < numRows; i++)
for (Ordinal i = 0; i < numRows; i++)
bHost(i) = 3 * ((one * rand()) / RAND_MAX);
Kokkos::deep_copy(b, bHost);
//Measure initial residual norm ||Ax - b||, where x is 0
Mag initialRes = KokkosBlas::nrm2(b);
// Measure initial residual norm ||Ax - b||, where x is 0
Mag initialRes = KokkosBlas::nrm2(b);
Mag scaledResNorm = magOne;
bool firstIter = true;
//Iterate until reaching the tolerance
bool firstIter = true;
// Iterate until reaching the tolerance
int numIters = 0;
while(scaledResNorm > tolerance)
{
//Run one sweep of forward Gauss-Seidel (SOR with omega = 1.0)
//If this is the first iteration, tell apply:
while (scaledResNorm > tolerance) {
// Run one sweep of forward Gauss-Seidel (SOR with omega = 1.0)
// If this is the first iteration, tell apply:
// * to zero out x (it was uninitialized)
// * that b has changed since the previous apply (since there was no previous apply)
// * that b has changed since the previous apply (since there was no
// previous apply)
KokkosSparse::Experimental::forward_sweep_gauss_seidel_apply(
&handle, numRows, numRows,
A.graph.row_map, A.graph.entries, A.values,
&handle, numRows, numRows, A.graph.row_map, A.graph.entries, A.values,
x, b, firstIter, firstIter, one, 1);
firstIter = false;
//Now, compute the new residual norm using SPMV
// Now, compute the new residual norm using SPMV
Kokkos::deep_copy(res, b);
//Compute res := Ax - res (since res is now equal to b, this is Ax - b)
// Compute res := Ax - res (since res is now equal to b, this is Ax - b)
KokkosSparse::spmv("N", one, A, x, -one, res);
//Recompute the scaled norm
// Recompute the scaled norm
scaledResNorm = KokkosBlas::nrm2(res) / initialRes;
numIters++;
std::cout << "Iteration " << numIters << " scaled residual norm: " << scaledResNorm << '\n';
std::cout << "Iteration " << numIters
<< " scaled residual norm: " << scaledResNorm << '\n';
}
std::cout << "SUCCESS: converged in " << numIters << " iterations.\n";
}
Kokkos::finalize();
return 0;
}

7 changes: 3 additions & 4 deletions perf_test/graph/KokkosGraph_color.cpp
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Expand Up @@ -55,6 +55,7 @@
#include "KokkosKernels_TestParameters.hpp"
#include "KokkosGraph_Distance1Color.hpp"
#include "KokkosKernels_TestUtils.hpp"
#include "KokkosSparse_IOUtils.hpp"

void print_options(std::ostream &os, const char *app_name,
unsigned int indent = 0) {
Expand Down Expand Up @@ -376,16 +377,14 @@ void run_multi_mem_experiment(Parameters params) {
if (params.a_mem_space == 1) {
fast_crstmat_t a_fast_crsmat;
a_fast_crsmat =
KokkosKernels::Impl::read_kokkos_crst_matrix<fast_crstmat_t>(
a_mat_file);
KokkosSparse::Impl::read_kokkos_crst_matrix<fast_crstmat_t>(a_mat_file);
a_fast_crsgraph = a_fast_crsmat.graph;
num_cols = a_fast_crsmat.numCols();

} else {
slow_crstmat_t a_slow_crsmat;
a_slow_crsmat =
KokkosKernels::Impl::read_kokkos_crst_matrix<slow_crstmat_t>(
a_mat_file);
KokkosSparse::Impl::read_kokkos_crst_matrix<slow_crstmat_t>(a_mat_file);
a_slow_crsgraph = a_slow_crsmat.graph;
num_cols = a_slow_crsmat.numCols();
}
Expand Down
3 changes: 2 additions & 1 deletion perf_test/graph/KokkosGraph_color_d2.cpp
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Expand Up @@ -65,6 +65,7 @@
#include <KokkosGraph_Distance2Color.hpp>
#include "KokkosKernels_default_types.hpp"
#include "KokkosKernels_TestUtils.hpp"
#include "KokkosSparse_IOUtils.hpp"

using namespace KokkosGraph;

Expand Down Expand Up @@ -595,7 +596,7 @@ void experiment_driver(const D2Parameters& params) {
using graph_t = typename crsMat_t::StaticCrsGraphType;

crsMat_t A =
KokkosKernels::Impl::read_kokkos_crst_matrix<crsMat_t>(params.mtx_file);
KokkosSparse::Impl::read_kokkos_crst_matrix<crsMat_t>(params.mtx_file);
graph_t Agraph = A.graph;
int num_cols = A.numCols();

Expand Down
3 changes: 2 additions & 1 deletion perf_test/graph/KokkosGraph_mis_d2.cpp
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Expand Up @@ -66,6 +66,7 @@
#include "KokkosGraph_MIS2.hpp"
#include "KokkosKernels_default_types.hpp"
#include "KokkosKernels_TestUtils.hpp"
#include "KokkosSparse_IOUtils.hpp"

using namespace KokkosGraph;

Expand Down Expand Up @@ -253,7 +254,7 @@ void run_mis2(const MIS2Parameters& params) {

Kokkos::Timer t;
crsMat_t A_in =
KokkosKernels::Impl::read_kokkos_crst_matrix<crsMat_t>(params.mtx_file);
KokkosSparse::Impl::read_kokkos_crst_matrix<crsMat_t>(params.mtx_file);
std::cout << "I/O time: " << t.seconds() << " s\n";
t.reset();
// Symmetrize the matrix just in case
Expand Down
5 changes: 3 additions & 2 deletions perf_test/sparse/KokkosSparse_gs.cpp
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Expand Up @@ -52,6 +52,7 @@
#include <KokkosBlas1_nrm2.hpp>
#include <KokkosKernels_config.h>
#include "KokkosKernels_default_types.hpp"
#include "KokkosSparse_IOUtils.hpp"
#include <iostream>
#include <random>
#include <vector>
Expand Down Expand Up @@ -177,7 +178,7 @@ crsMat_t generateLongRowMatrix(const GS_Parameters& params) {
rowmap.data(), numRows + 1));
crsMat_t A("A", numRows, numRows, totalEntries, valuesView, rowmapView,
entriesView);
A = KokkosKernels::sort_and_merge_matrix(A);
A = KokkosSparse::sort_and_merge_matrix(A);
if (params.graph_symmetric) {
// Symmetrize on host, rather than relying on the parallel versions (those
// can be tested for symmetric=false)
Expand All @@ -203,7 +204,7 @@ void runGS(const GS_Parameters& params) {
typedef typename crsMat_t::values_type::non_const_type scalar_view_t;
crsMat_t A;
if (params.matrix_path)
A = KokkosKernels::Impl::read_kokkos_crst_matrix<crsMat_t>(
A = KokkosSparse::Impl::read_kokkos_crst_matrix<crsMat_t>(
params.matrix_path);
else
A = generateLongRowMatrix<crsMat_t>(params);
Expand Down
5 changes: 3 additions & 2 deletions perf_test/sparse/KokkosSparse_kk_spmv.cpp
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Expand Up @@ -55,6 +55,7 @@
#include <Kokkos_Core.hpp>
#include <KokkosSparse_CrsMatrix.hpp>
#include <KokkosKernels_IOUtils.hpp>
#include <KokkosSparse_IOUtils.hpp>
#include <KokkosSparse_spmv.hpp>
#include "KokkosKernels_default_types.hpp"

Expand All @@ -74,11 +75,11 @@ void run_spmv(Ordinal numRows, Ordinal numCols, const char* filename, int loop,
srand(17312837);
matrix_type A;
if (filename)
A = KokkosKernels::Impl::read_kokkos_crst_matrix<matrix_type>(filename);
A = KokkosSparse::Impl::read_kokkos_crst_matrix<matrix_type>(filename);
else {
Offset nnz = 10 * numRows;
// note: the help text says the bandwidth is fixed at 0.01 * numRows
A = KokkosKernels::Impl::kk_generate_sparse_matrix<matrix_type>(
A = KokkosSparse::Impl::kk_generate_sparse_matrix<matrix_type>(
numRows, numCols, nnz, 0, 0.01 * numRows);
}
numRows = A.numRows();
Expand Down
21 changes: 9 additions & 12 deletions perf_test/sparse/KokkosSparse_multimem_spgemm.hpp
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Expand Up @@ -44,6 +44,7 @@

#include "KokkosSparse_CrsMatrix.hpp"
#include "KokkosSparse_run_spgemm.hpp"
#include "KokkosSparse_IOUtils.hpp"

namespace KokkosKernels {

Expand Down Expand Up @@ -74,12 +75,10 @@ void run_multi_mem_spgemm(Parameters params) {

if (params.a_mem_space == 1) {
a_fast_crsmat =
KokkosKernels::Impl::read_kokkos_crst_matrix<fast_crstmat_t>(
a_mat_file);
KokkosSparse::Impl::read_kokkos_crst_matrix<fast_crstmat_t>(a_mat_file);
} else {
a_slow_crsmat =
KokkosKernels::Impl::read_kokkos_crst_matrix<slow_crstmat_t>(
a_mat_file);
KokkosSparse::Impl::read_kokkos_crst_matrix<slow_crstmat_t>(a_mat_file);
}

if ((b_mat_file == NULL || strcmp(b_mat_file, a_mat_file) == 0) &&
Expand All @@ -90,13 +89,11 @@ void run_multi_mem_spgemm(Parameters params) {
} else if (params.b_mem_space == 1) {
if (b_mat_file == NULL) b_mat_file = a_mat_file;
b_fast_crsmat =
KokkosKernels::Impl::read_kokkos_crst_matrix<fast_crstmat_t>(
b_mat_file);
KokkosSparse::Impl::read_kokkos_crst_matrix<fast_crstmat_t>(b_mat_file);
} else {
if (b_mat_file == NULL) b_mat_file = a_mat_file;
b_slow_crsmat =
KokkosKernels::Impl::read_kokkos_crst_matrix<slow_crstmat_t>(
b_mat_file);
KokkosSparse::Impl::read_kokkos_crst_matrix<slow_crstmat_t>(b_mat_file);
}

if (params.a_mem_space == 1) {
Expand Down Expand Up @@ -222,18 +219,18 @@ void run_multi_mem_spgemm(Parameters params) {

if (c_mat_file != NULL) {
if (params.c_mem_space == 1) {
KokkosKernels::sort_crs_matrix(c_fast_crsmat);
KokkosSparse::sort_crs_matrix(c_fast_crsmat);

KokkosKernels::Impl::write_graph_bin(
KokkosSparse::Impl::write_graph_bin(
(lno_t)(c_fast_crsmat.numRows()),
(size_type)(c_fast_crsmat.graph.entries.extent(0)),
c_fast_crsmat.graph.row_map.data(),
c_fast_crsmat.graph.entries.data(), c_fast_crsmat.values.data(),
c_mat_file);
} else {
KokkosKernels::sort_crs_matrix(c_slow_crsmat);
KokkosSparse::sort_crs_matrix(c_slow_crsmat);

KokkosKernels::Impl::write_graph_bin(
KokkosSparse::Impl::write_graph_bin(
(lno_t)c_slow_crsmat.numRows(),
(size_type)c_slow_crsmat.graph.entries.extent(0),
c_slow_crsmat.graph.row_map.data(),
Expand Down
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