CPU and GPU dot

benchmarks/linalg_refactor_benchmark.cpp

@@ -0,0 +1,149 @@
+#include <shogun/base/init.h>
+#include <shogun/lib/SGVector.h>
+#include <shogun/mathematics/linalgrefactor/linalgRefactor.h>
+
+#include <shogun/mathematics/eigen3.h>
+#include <viennacl/linalg/inner_prod.hpp>
+#include <viennacl/vector.hpp>
+
+#include <algorithm>
+#include <memory>
+#include <hayai/hayai.hpp>
+
+#include <iostream>
+
+using namespace shogun;
+
+/**
+ * Instructions :
+ * 1. Install benchmarking toolkit "hayai" (https://github.com/nickbruun/hayai)
+ * 2. Compile against libhayai_main, e.g.
+ * 		g++ -O3 -std=c++11 linglg_refactor_benchmark.cpp -I/usr/include/eigen3 \
+ *		-lshogun -lhayai_main -lOpenCL -o benchmark
+ * 3. ./benchmark
+ */
+
+/** Generate data only once */
+typedef float32_t T;
+typedef viennacl::vector_base<T, std::size_t, std::ptrdiff_t> VCLVectorBase;
+
+template<typename value_type>
+struct Data
+{
+	typedef viennacl::backend::mem_handle VCLMemoryArray;
+
+	Data()
+	{
+		num_rows = 100;
+		init();
+	}
+
+	Data(index_t num_rows)
+	{
+		this->num_rows = num_rows;
+		init();
+	}
+
+	~Data()
+	{
+		exit_shogun();
+	}
+
+	void init()
+	{
+		A = init_sg(1);
+		B = init_sg(2);
+		Av = init_v(A);
+		Bv = init_v(B);
+		Ac = std::unique_ptr<BaseVector<value_type>> (init_c(A));
+		Bc = std::unique_ptr<BaseVector<value_type>> (init_c(B));
+		Ag = std::unique_ptr<BaseVector<value_type>> (init_g(A));
+		Bg = std::unique_ptr<BaseVector<value_type>> (init_g(B));
+
+		init_shogun_with_defaults();
+		GPUBackend viennaclBackend;
+		sg_linalg->set_gpu_backend(&viennaclBackend);
+	}
+
+	/** SGVector **/
+	SGVector<value_type> init_sg(value_type begin)
+	{
+		SGVector<value_type> m(num_rows);
+		m.range_fill(begin);
+
+		return m;
+	}
+
+	/** ViennaCL Vector for test **/
+	VCLVectorBase init_v(SGVector<value_type> m)
+	{
+		VCLVectorBase mv;
+		std::shared_ptr<VCLMemoryArray> vector(new VCLMemoryArray());
+		viennacl::backend::memory_create(*vector, sizeof(value_type)*num_rows,
+			viennacl::context());
+		viennacl::backend::memory_write(*vector, 0, num_rows*sizeof(value_type),
+			m.vector);
+		mv = VCLVectorBase(*vector, num_rows, 0, 1);
+
+		return mv;
+	}
+
+	/** CPUVector derived from BaseVector **/
+	std::unique_ptr<BaseVector<value_type>> init_c(SGVector<value_type> m)
+	{
+		std::unique_ptr<CPUVector<value_type>> mc(new CPUVector<value_type>(m));
+		return std::move(mc);
+	}
+
+	/** GPUVector derived from BaseVector **/
+	std::unique_ptr<BaseVector<value_type>> init_g(SGVector<value_type> m)
+	{
+		std::unique_ptr<GPU_Vector<value_type>> mg(new GPU_Vector<value_type>(m));
+		return std::move(mg);
+	}
+
+	SGVector<value_type> A;
+	SGVector<value_type> B;
+	VCLVectorBase Av;
+	VCLVectorBase Bv;
+	std::unique_ptr<BaseVector<value_type>> Ac;
+	std::unique_ptr<BaseVector<value_type>> Bc;
+	std::unique_ptr<BaseVector<value_type>> Ag;
+	std::unique_ptr<BaseVector<value_type>> Bg;
+
+	index_t num_rows;
+};
+
+BENCHMARK_P(CPUVector, dot_explict_eigen3, 10, 1000,
+	(const SGVector<T> &A, const SGVector<T> &B))
+{
+	typedef Eigen::Matrix<T, Eigen::Dynamic, 1> VectorXt;
+	Eigen::Map<VectorXt> vec_A(A.vector, A.vlen);
+	Eigen::Map<VectorXt> vec_B(B.vector, B.vlen);
+
+    	auto C = vec_A.dot(vec_B);
+}
+
+BENCHMARK_P(CPUVector, dot_eigen3, 10, 1000,
+	(BaseVector<T> *A, BaseVector<T> *B))
+{
+	auto C = sg_linalg->dot(A, B);
+}
+
+BENCHMARK_P(GPU_Vector, dot_explict_viennacl, 10, 1000,
+	(const VCLVectorBase &A, const VCLVectorBase &B))
+{
+	auto C = viennacl::linalg::inner_prod(A, B);
+}
+
+BENCHMARK_P(GPU_Vector, dot_viennacl, 10, 1000,
+	(BaseVector<T> *A, BaseVector<T> *B))
+{
+	auto C = sg_linalg->dot(A, B);
+}
+
+Data<T> data(1000000);
+BENCHMARK_P_INSTANCE(CPUVector, dot_explict_eigen3, (data.A, data.B));
+BENCHMARK_P_INSTANCE(CPUVector, dot_eigen3, (data.Ac.get(), data.Bc.get()));
+BENCHMARK_P_INSTANCE(GPU_Vector, dot_explict_viennacl, (data.Av, data.Bv));
+BENCHMARK_P_INSTANCE(GPU_Vector, dot_viennacl, (data.Ag.get(), data.Bg.get()));