Add benchmarks for unsupported modules and extend supported benchmarks

libeigen/eigen!2179

Closes #3036

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>

unsupported/benchmarks/MatrixFunctions/bench_matrix_power.cpp

@@ -0,0 +1,99 @@
+// Benchmarks for matrix power functions: sqrt, pow, cos, sin, cosh, sinh.
+
+#include <benchmark/benchmark.h>
+#include <Eigen/Core>
+#include <unsupported/Eigen/MatrixFunctions>
+
+using namespace Eigen;
+
+typedef double Scalar;
+typedef Matrix<Scalar, Dynamic, Dynamic> Mat;
+
+static void BM_MatrixSqrt(benchmark::State& state) {
+  int n = state.range(0);
+  // SPD matrix has well-defined sqrt.
+  Mat tmp = Mat::Random(n, n);
+  Mat A = tmp * tmp.transpose() + Mat::Identity(n, n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.sqrt();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixPow(benchmark::State& state) {
+  int n = state.range(0);
+  Mat tmp = Mat::Random(n, n);
+  Mat A = tmp * tmp.transpose() + Mat::Identity(n, n);
+  Mat result(n, n);
+  Scalar p = 2.5;
+
+  for (auto _ : state) {
+    result = A.pow(p);
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixCos(benchmark::State& state) {
+  int n = state.range(0);
+  Mat A = Mat::Random(n, n) / Scalar(n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.cos();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixSin(benchmark::State& state) {
+  int n = state.range(0);
+  Mat A = Mat::Random(n, n) / Scalar(n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.sin();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixCosh(benchmark::State& state) {
+  int n = state.range(0);
+  Mat A = Mat::Random(n, n) / Scalar(n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.cosh();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void BM_MatrixSinh(benchmark::State& state) {
+  int n = state.range(0);
+  Mat A = Mat::Random(n, n) / Scalar(n);
+  Mat result(n, n);
+
+  for (auto _ : state) {
+    result = A.sinh();
+    benchmark::DoNotOptimize(result.data());
+    benchmark::ClobberMemory();
+  }
+}
+
+static void MatPowerSizes(::benchmark::Benchmark* b) {
+  for (int n : {4, 8, 16, 32, 64}) {
+    b->Arg(n);
+  }
+}
+
+BENCHMARK(BM_MatrixSqrt)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixPow)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixCos)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixSin)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixCosh)->Apply(MatPowerSizes);
+BENCHMARK(BM_MatrixSinh)->Apply(MatPowerSizes);