bench(spline): Implemented benchmarks for PolyEqvSpline

Author: pasabanov

.idea/runConfigurations/bench_polyeqv.xml

@@ -15,6 +15,7 @@ endmacro()
 
 add_bench_executable(bench_poly poly/bench_poly.cpp)
 add_bench_executable(bench_barycentric misc/bench_barycentric.cpp)
+add_bench_executable(bench_polyeqv spline/bench_polyeqv.cpp)
 add_bench_executable(bench_step spline/bench_step.cpp)
 add_bench_executable(bench_linear spline/bench_linear.cpp)
 add_bench_executable(bench_quadratic spline/bench_quadratic.cpp)

benches/CMakeLists.txt

@@ -0,0 +1,67 @@
+#include <benchmark/benchmark.h>
+
+#include <ALFI/spline/polyeqv.h>
+
+#include "../bench_utils.h"
+#include "../bench_data.h"
+
+const std::vector<alfi::spline::PolyEqvSpline<>::OptimizationType> polyeqv_spline_optimization_types = {
+	alfi::spline::PolyEqvSpline<>::OptimizationType::ACCURACY,
+	alfi::spline::PolyEqvSpline<>::OptimizationType::SPEED,
+};
+
+static void BM_polyeqv_spline(benchmark::State& state) {
+	const auto& [func_name, lambda] = funcs_and_ints[state.range(0)].first;
+	const auto& interval = funcs_and_ints[state.range(0)].second;
+	const auto& dist_type = dists[state.range(1)];
+	const auto& n = state.range(2);
+	const auto& optimization_type = polyeqv_spline_optimization_types[state.range(3)];
+	const std::vector<double> X = of_type<double>(dist_type, n, interval.first, interval.second);
+	const std::vector<double> Y = apply_func(X, lambda);
+	alfi::spline::PolyEqvSpline<> spline;
+	for (auto _ : state) {
+		spline = alfi::spline::PolyEqvSpline<>(X, Y, alfi::spline::PolyEqvSpline<>::PolynomialType::LAGRANGE, optimization_type);
+		benchmark::DoNotOptimize(spline);
+		benchmark::ClobberMemory();
+	}
+}
+BENCHMARK(BM_polyeqv_spline)
+	->ArgsProduct({
+		benchmark::CreateDenseRange(0, static_cast<int64_t>(funcs_and_ints.size()) - 1, 1),
+		benchmark::CreateDenseRange(0, static_cast<int64_t>(dists.size()) - 1, 1),
+		benchmark::CreateRange(8, 32, 2),
+		benchmark::CreateDenseRange(0, static_cast<int64_t>(polyeqv_spline_optimization_types.size()) - 1, 1),
+	})
+	->ArgNames({"func", "dist", "n", "type"});
+
+static void BM_polyeqv_spline_values(benchmark::State& state) {
+	const auto& [func_name, lambda] = funcs_and_ints[state.range(0)].first;
+	const auto& interval = funcs_and_ints[state.range(0)].second;
+	const auto& dist_type = dists[state.range(1)];
+	const auto& n = state.range(2);
+	const auto& nn = state.range(3);
+	const auto& optimization_type = polyeqv_spline_optimization_types[state.range(4)];
+	const std::vector<double> X = of_type<double>(dist_type, n, interval.first, interval.second);
+	const std::vector<double> Y = apply_func(X, lambda);
+	const std::vector<double> xx = of_type<double>(alfi::dist::Type::UNIFORM, nn, interval.first, interval.second);
+	const alfi::spline::PolyEqvSpline<> spline(X, Y, alfi::spline::PolyEqvSpline<>::PolynomialType::LAGRANGE, optimization_type);
+	std::vector<double> result;
+	for (auto _ : state) {
+		result = spline.eval(xx);
+		benchmark::DoNotOptimize(result);
+		benchmark::ClobberMemory();
+	}
+	const std::vector<double> expected = apply_func(xx, lambda);
+	add_error_metrics(state, result, expected);
+}
+BENCHMARK(BM_polyeqv_spline_values)
+	->ArgsProduct({
+		benchmark::CreateDenseRange(0, static_cast<int64_t>(funcs_and_ints.size()) - 1, 1),
+		benchmark::CreateDenseRange(0, static_cast<int64_t>(dists.size()) - 1, 1),
+		benchmark::CreateRange(8, 32, 2),
+		{nn},
+		benchmark::CreateDenseRange(0, static_cast<int64_t>(polyeqv_spline_optimization_types.size()) - 1, 1),
+	})
+	->ArgNames({"func", "dist", "n", "nn", "type"});
+
+BENCHMARK_MAIN();