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104 lines
3.8 KiB
C++
104 lines
3.8 KiB
C++
// Benchmarks for Map and Ref with various strides.
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//
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// Compares contiguous Map vs strided Map vs owned matrix for basic
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// operations (GEMV and vector sum).
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#include <benchmark/benchmark.h>
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#include <Eigen/Core>
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using namespace Eigen;
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// Sum a contiguous Map<VectorX>.
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template <typename Scalar>
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static void BM_MapContiguousSum(benchmark::State& state) {
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const Index n = state.range(0);
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std::vector<Scalar> buf(n);
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Map<Matrix<Scalar, Dynamic, 1>> v(buf.data(), n);
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v.setRandom();
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for (auto _ : state) {
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Scalar s = v.sum();
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benchmark::DoNotOptimize(s);
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}
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state.SetBytesProcessed(state.iterations() * n * sizeof(Scalar));
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}
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// Sum a strided Map (InnerStride).
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template <typename Scalar>
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static void BM_MapStridedSum(benchmark::State& state) {
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const Index n = state.range(0);
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const Index stride = 3;
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std::vector<Scalar> buf(n * stride);
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Map<Matrix<Scalar, Dynamic, 1>, 0, InnerStride<>> v(buf.data(), n, InnerStride<>(stride));
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v.setRandom();
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for (auto _ : state) {
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Scalar s = v.sum();
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benchmark::DoNotOptimize(s);
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}
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state.SetBytesProcessed(state.iterations() * n * sizeof(Scalar));
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}
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// Sum an owned VectorX (baseline).
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template <typename Scalar>
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static void BM_OwnedSum(benchmark::State& state) {
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const Index n = state.range(0);
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Matrix<Scalar, Dynamic, 1> v = Matrix<Scalar, Dynamic, 1>::Random(n);
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for (auto _ : state) {
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Scalar s = v.sum();
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benchmark::DoNotOptimize(s);
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}
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state.SetBytesProcessed(state.iterations() * n * sizeof(Scalar));
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}
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// GEMV through contiguous Map<MatrixX>.
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template <typename Scalar>
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static void BM_MapGemv(benchmark::State& state) {
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const Index n = state.range(0);
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std::vector<Scalar> buf(n * n);
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Map<Matrix<Scalar, Dynamic, Dynamic>> A(buf.data(), n, n);
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A.setRandom();
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Matrix<Scalar, Dynamic, 1> x = Matrix<Scalar, Dynamic, 1>::Random(n);
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Matrix<Scalar, Dynamic, 1> y = Matrix<Scalar, Dynamic, 1>::Random(n);
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for (auto _ : state) {
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y.noalias() += A * x;
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benchmark::DoNotOptimize(y.data());
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benchmark::ClobberMemory();
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}
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state.counters["GFLOPS"] =
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benchmark::Counter(2.0 * n * n, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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}
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// GEMV with owned matrix (baseline).
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template <typename Scalar>
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static void BM_OwnedGemv(benchmark::State& state) {
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const Index n = state.range(0);
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Matrix<Scalar, Dynamic, Dynamic> A = Matrix<Scalar, Dynamic, Dynamic>::Random(n, n);
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Matrix<Scalar, Dynamic, 1> x = Matrix<Scalar, Dynamic, 1>::Random(n);
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Matrix<Scalar, Dynamic, 1> y = Matrix<Scalar, Dynamic, 1>::Random(n);
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for (auto _ : state) {
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y.noalias() += A * x;
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benchmark::DoNotOptimize(y.data());
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benchmark::ClobberMemory();
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}
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state.counters["GFLOPS"] =
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benchmark::Counter(2.0 * n * n, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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}
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static void SumSizes(::benchmark::Benchmark* b) {
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for (int n : {256, 1024, 4096, 16384, 65536, 262144, 1048576}) b->Arg(n);
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}
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static void GemvSizes(::benchmark::Benchmark* b) {
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for (int n : {32, 128, 512, 1024}) b->Arg(n);
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}
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BENCHMARK(BM_MapContiguousSum<float>)->Apply(SumSizes)->Name("MapContiguousSum_float");
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BENCHMARK(BM_MapStridedSum<float>)->Apply(SumSizes)->Name("MapStridedSum_float");
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BENCHMARK(BM_OwnedSum<float>)->Apply(SumSizes)->Name("OwnedSum_float");
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BENCHMARK(BM_MapContiguousSum<double>)->Apply(SumSizes)->Name("MapContiguousSum_double");
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BENCHMARK(BM_MapStridedSum<double>)->Apply(SumSizes)->Name("MapStridedSum_double");
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BENCHMARK(BM_OwnedSum<double>)->Apply(SumSizes)->Name("OwnedSum_double");
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BENCHMARK(BM_MapGemv<float>)->Apply(GemvSizes)->Name("MapGemv_float");
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BENCHMARK(BM_OwnedGemv<float>)->Apply(GemvSizes)->Name("OwnedGemv_float");
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BENCHMARK(BM_MapGemv<double>)->Apply(GemvSizes)->Name("MapGemv_double");
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BENCHMARK(BM_OwnedGemv<double>)->Apply(GemvSizes)->Name("OwnedGemv_double");
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