mirror of
https://github.com/OPM/opm-simulators.git
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182 lines
6.3 KiB
C++
182 lines
6.3 KiB
C++
/*
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Copyright 2022-2023 SINTEF AS
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This file is part of the Open Porous Media project (OPM).
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OPM is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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OPM is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with OPM. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <config.h>
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#define BOOST_TEST_MODULE TestGpuSparseMatrix
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#include <boost/test/unit_test.hpp>
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#include <dune/istl/bcrsmatrix.hh>
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#include <memory>
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#include <opm/simulators/linalg/gpuistl/GpuSparseMatrix.hpp>
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#include <opm/simulators/linalg/gpuistl/GpuVector.hpp>
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#include <opm/simulators/linalg/gpuistl/detail/gpu_safe_call.hpp>
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#include <random>
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BOOST_AUTO_TEST_CASE(TestConstruction1D)
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{
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// Here we will test a simple 1D finite difference scheme for
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// the Laplace equation:
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//
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// -\Delta u = f on [0,1]
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//
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// Using a central difference approximation of \Delta u, this can
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// be approximated by
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//
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// -(u_{i+1}-2u_i+u_{i-1})/Dx^2 = f(x_i)
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//
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// giving rise to the matrix
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//
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// -2 1 0 0 ... 0 0
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// 1 -2 1 0 0 ... 0
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// ....
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// 0 0 0 ...1 -2 1
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// 0 0 0 ... 1 -2
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const int N = 5;
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const int nonZeroes = N * 3 - 2;
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using M = Dune::FieldMatrix<double, 1, 1>;
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using SpMatrix = Dune::BCRSMatrix<M>;
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SpMatrix B(N, N, nonZeroes, SpMatrix::row_wise);
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for (auto row = B.createbegin(); row != B.createend(); ++row) {
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// Add nonzeros for left neighbour, diagonal and right neighbour
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if (row.index() > 0) {
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row.insert(row.index() - 1);
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}
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row.insert(row.index());
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if (row.index() < B.N() - 1) {
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row.insert(row.index() + 1);
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}
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}
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// This might not be the most elegant way of filling in a Dune sparse matrix, but it works.
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for (int i = 0; i < N; ++i) {
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B[i][i] = -2;
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if (i < N - 1) {
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B[i][i + 1] = 1;
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}
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if (i > 0) {
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B[i][i - 1] = 1;
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}
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}
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auto gpuSparseMatrix = Opm::gpuistl::GpuSparseMatrix<double>::fromMatrix(B);
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const auto& nonZeroValuesCuda = gpuSparseMatrix.getNonZeroValues();
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std::vector<double> buffer(gpuSparseMatrix.nonzeroes(), 0.0);
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nonZeroValuesCuda.copyToHost(buffer.data(), buffer.size());
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const double* nonZeroElements = static_cast<const double*>(&((B[0][0][0][0])));
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BOOST_CHECK_EQUAL_COLLECTIONS(buffer.begin(), buffer.end(), nonZeroElements, nonZeroElements + B.nonzeroes());
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BOOST_CHECK_EQUAL(N * 3 - 2, gpuSparseMatrix.nonzeroes());
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std::vector<int> rowIndicesFromCUDA(N + 1);
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gpuSparseMatrix.getRowIndices().copyToHost(rowIndicesFromCUDA.data(), rowIndicesFromCUDA.size());
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BOOST_CHECK_EQUAL(rowIndicesFromCUDA[0], 0);
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BOOST_CHECK_EQUAL(rowIndicesFromCUDA[1], 2);
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for (int i = 2; i < N; ++i) {
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BOOST_CHECK_EQUAL(rowIndicesFromCUDA[i], rowIndicesFromCUDA[i - 1] + 3);
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}
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std::vector<int> columnIndicesFromCUDA(B.nonzeroes(), 0);
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gpuSparseMatrix.getColumnIndices().copyToHost(columnIndicesFromCUDA.data(), columnIndicesFromCUDA.size());
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BOOST_CHECK_EQUAL(columnIndicesFromCUDA[0], 0);
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BOOST_CHECK_EQUAL(columnIndicesFromCUDA[1], 1);
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// TODO: Check rest
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}
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BOOST_AUTO_TEST_CASE(RandomSparsityMatrix)
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{
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std::srand(0);
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double nonzeroPercent = 0.2;
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std::mt19937 generator;
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std::uniform_real_distribution<double> distribution(0.0, 1.0);
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constexpr size_t dim = 3;
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const int N = 300;
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using M = Dune::FieldMatrix<double, dim, dim>;
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using SpMatrix = Dune::BCRSMatrix<M>;
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using Vector = Dune::BlockVector<Dune::FieldVector<double, dim>>;
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std::vector<std::vector<size_t>> nonzerocols(N);
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int nonZeroes = 0;
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for (auto row = 0; row < N; ++row) {
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for (size_t col = 0; col < N; ++col) {
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if (distribution(generator) < nonzeroPercent) {
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nonzerocols.at(row).push_back(col);
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nonZeroes++;
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}
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}
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}
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SpMatrix B(N, N, nonZeroes, SpMatrix::row_wise);
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for (auto row = B.createbegin(); row != B.createend(); ++row) {
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for (size_t j = 0; j < nonzerocols[row.index()].size(); ++j) {
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row.insert(nonzerocols[row.index()][j]);
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}
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}
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// This might not be the most elegant way of filling in a Dune sparse matrix, but it works.
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for (int i = 0; i < N; ++i) {
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for (size_t j = 0; j < nonzerocols[i].size(); ++j) {
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for (size_t c1 = 0; c1 < dim; ++c1) {
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for (size_t c2 = 0; c2 < dim; ++c2) {
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B[i][nonzerocols[i][j]][c1][c2] = distribution(generator);
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}
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}
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}
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}
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auto gpuSparseMatrix = Opm::gpuistl::GpuSparseMatrix<double>::fromMatrix(B);
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// check each column
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for (size_t component = 0; component < N; ++component) {
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std::vector<double> inputDataX(N * dim, 0.0);
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inputDataX[component] = 1.0;
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std::vector<double> inputDataY(N * dim, .25);
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auto inputVectorX = Opm::gpuistl::GpuVector<double>(inputDataX.data(), inputDataX.size());
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auto inputVectorY = Opm::gpuistl::GpuVector<double>(inputDataY.data(), inputDataY.size());
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Vector xHost(N), yHost(N);
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yHost = inputDataY[0];
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inputVectorX.copyToHost(xHost);
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const double alpha = 1.42;
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gpuSparseMatrix.usmv(alpha, inputVectorX, inputVectorY);
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inputVectorY.copyToHost(inputDataY);
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B.usmv(alpha, xHost, yHost);
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for (size_t i = 0; i < N; ++i) {
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for (size_t c = 0; c < dim; ++c) {
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BOOST_CHECK_CLOSE(inputDataY[i * dim + c], yHost[i][c], 1e-7);
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}
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}
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inputVectorX.copyToHost(xHost);
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gpuSparseMatrix.mv(inputVectorX, inputVectorY);
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inputVectorY.copyToHost(inputDataY);
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B.mv(xHost, yHost);
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for (size_t i = 0; i < N; ++i) {
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for (size_t c = 0; c < dim; ++c) {
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BOOST_CHECK_CLOSE(inputDataY[i * dim + c], yHost[i][c], 1e-7);
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}
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}
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}
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}
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