Rasmus Munk LarsenandRasmus Munk Larsen
633c2229fc
libeigen/eigen!2580 Closes #3084 Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
47 lines
1.4 KiB
C++
47 lines
1.4 KiB
C++
// SPDX-FileCopyrightText: The Eigen Authors
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// SPDX-License-Identifier: MPL-2.0
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#include <benchmark/benchmark.h>
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#include <Eigen/Sparse>
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#include <cstdint>
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#include <random>
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using namespace Eigen;
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typedef double Scalar;
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typedef Matrix<Scalar, Dynamic, Dynamic> DenseMat;
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typedef SparseMatrix<Scalar> SpMat;
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static DenseMat makeDense(int rows, int cols, double density, std::uint64_t seed) {
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DenseMat dense = DenseMat::Zero(rows, cols);
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std::mt19937_64 rng(seed);
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std::uniform_real_distribution<Scalar> coin(0.0, 1.0);
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std::uniform_real_distribution<Scalar> value(-1.0, 1.0);
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for (int j = 0; j < cols; ++j) {
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for (int i = 0; i < rows; ++i) {
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if (coin(rng) < density) dense(i, j) = value(rng);
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}
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}
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return dense;
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}
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static void BM_SparseViewAssign(benchmark::State& state) {
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const int rows = static_cast<int>(state.range(0));
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const int cols = static_cast<int>(state.range(1));
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const double density = static_cast<double>(state.range(2)) / 10000.0;
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const DenseMat dense = makeDense(rows, cols, density, 0xC0FFEEu);
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SpMat result;
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for (auto _ : state) {
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result = dense.sparseView();
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benchmark::DoNotOptimize(result.valuePtr());
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benchmark::ClobberMemory();
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}
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state.counters["density%"] = density * 100.0;
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state.counters["nnz"] = result.nonZeros();
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}
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// Args: {rows, cols, density*10000}: 0%, 1%, 10%, 50%, 100%.
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BENCHMARK(BM_SparseViewAssign)->ArgsProduct({{500}, {1, 8, 500}, {0, 100, 1000, 5000, 10000}});
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