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Add benchmarks for unsupported modules and extend supported benchmarks

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libeigen/eigen!2179

Closes #3036

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
This commit is contained in:
Rasmus Munk Larsen
2026-02-24 17:12:33 -08:00
parent fa567f6bcd
commit 16da0279f1
33 changed files with 2320 additions and 10 deletions
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80
unsupported/benchmarks/Tensor/bench_tensor_fft.cpp Normal file
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// Benchmarks for Eigen Tensor FFT.
#include <benchmark/benchmark.h>
#include <unsupported/Eigen/CXX11/Tensor>
using namespace Eigen;
#ifndef SCALAR
#define SCALAR float
#endif
typedef SCALAR Scalar;
// --- 1D FFT ---
static void BM_TensorFFT_1D(benchmark::State& state) {
const int N = state.range(0);
Tensor<Scalar, 1> input(N);
input.setRandom();
Eigen::array<int, 1> fft_dims = {0};
for (auto _ : state) {
Tensor<std::complex<Scalar>, 1> result = input.template fft<BothParts, FFT_FORWARD>(fft_dims);
benchmark::DoNotOptimize(result.data());
benchmark::ClobberMemory();
}
double mflops = 5.0 * N * std::log2(static_cast<double>(N)) / 2.0; // real->complex
state.counters["MFLOPS"] =
benchmark::Counter(mflops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
}
// --- 2D FFT ---
static void BM_TensorFFT_2D(benchmark::State& state) {
const int N = state.range(0);
Tensor<Scalar, 2> input(N, N);
input.setRandom();
Eigen::array<int, 2> fft_dims = {0, 1};
for (auto _ : state) {
Tensor<std::complex<Scalar>, 2> result = input.template fft<BothParts, FFT_FORWARD>(fft_dims);
benchmark::DoNotOptimize(result.data());
benchmark::ClobberMemory();
}
double total = N * N;
double mflops = 5.0 * total * std::log2(static_cast<double>(N));
state.counters["MFLOPS"] =
benchmark::Counter(mflops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
}
// --- 1D inverse FFT ---
static void BM_TensorIFFT_1D(benchmark::State& state) {
const int N = state.range(0);
Tensor<std::complex<Scalar>, 1> input(N);
input.setRandom();
Eigen::array<int, 1> fft_dims = {0};
for (auto _ : state) {
Tensor<std::complex<Scalar>, 1> result = input.template fft<BothParts, FFT_REVERSE>(fft_dims);
benchmark::DoNotOptimize(result.data());
benchmark::ClobberMemory();
}
double mflops = 5.0 * N * std::log2(static_cast<double>(N));
state.counters["MFLOPS"] =
benchmark::Counter(mflops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
}
static void FFTSizes(::benchmark::Benchmark* b) {
for (int n : {64, 256, 1024, 4096}) {
b->Arg(n);
}
}
BENCHMARK(BM_TensorFFT_1D)->Apply(FFTSizes);
BENCHMARK(BM_TensorFFT_2D)->Apply(FFTSizes);
BENCHMARK(BM_TensorIFFT_1D)->Apply(FFTSizes);