Migrate Eigen benchmarks to the Google benchmark framework

libeigen/eigen!2132

Closes #3025

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

benchmarks/sparse_product.cpp

@@ -0,0 +1,49 @@
+#include <benchmark/benchmark.h>
+#include <Eigen/Sparse>
+#include <set>
+
+using namespace Eigen;
+
+typedef double Scalar;
+typedef SparseMatrix<Scalar> SpMat;
+
+static void fillMatrix(int nnzPerCol, int rows, int cols, SpMat& dst) {
+  dst.resize(rows, cols);
+  dst.reserve(VectorXi::Constant(cols, nnzPerCol));
+  for (int j = 0; j < cols; ++j) {
+    std::set<int> used;
+    for (int i = 0; i < nnzPerCol; ++i) {
+      int row;
+      do {
+        row = internal::random<int>(0, rows - 1);
+      } while (used.count(row));
+      used.insert(row);
+      dst.insert(row, j) = internal::random<Scalar>();
+    }
+  }
+  dst.makeCompressed();
+}
+
+static void BM_SparseMM(benchmark::State& state) {
+  int n = state.range(0);
+  int nnzPerCol = state.range(1);
+  SpMat sm1(n, n), sm2(n, n), sm3(n, n);
+  fillMatrix(nnzPerCol, n, n, sm1);
+  fillMatrix(nnzPerCol, n, n, sm2);
+  for (auto _ : state) {
+    sm3 = sm1 * sm2;
+    benchmark::DoNotOptimize(sm3.valuePtr());
+  }
+  state.counters["nnz_A"] = sm1.nonZeros();
+  state.counters["nnz_B"] = sm2.nonZeros();
+}
+
+static void SpMMSizes(::benchmark::Benchmark* b) {
+  for (int n : {1000, 10000}) {
+    for (int nnz : {4, 6, 10}) {
+      b->Args({n, nnz});
+    }
+  }
+}
+
+BENCHMARK(BM_SparseMM)->Apply(SpMMSizes);