262 lines
8.5 KiB
C++
262 lines
8.5 KiB
C++
#include "atidlas/array.h"
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#include "atidlas/symbolic/execute.h"
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#include "common.hpp"
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#ifdef BENCH_CLAMDBLAS
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#include "clAmdBlas.h"
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#endif
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#ifdef BENCH_CBLAS
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#include "cblas.h"
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#endif
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#ifdef BENCH_CUBLAS
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#include <cublas.h>
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#endif
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#include <iomanip>
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#include <stdlib.h>
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#include <cmath>
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#include <chrono>
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namespace ad = atidlas;
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typedef ad::int_t int_t;
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template<class T>
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void bench(ad::numeric_type dtype)
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{
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unsigned int dtsize = ad::size_of(dtype);
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cl::CommandQueue & queue = ad::cl_ext::queues[ad::cl_ext::default_context()][0];
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#define BENCHMARK_ATIDLAS(OP, PERF) \
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{\
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std::vector<long> times;\
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double total_time = 0;\
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while(total_time*1e-9 < 1e-1){\
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std::list<cl::Event> events;\
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OP;\
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queue.finish();\
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times.push_back(std::accumulate(events.begin(), events.end(), 0, \
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[](unsigned long sum, cl::Event const & e){ return sum + e.getProfilingInfo<CL_PROFILING_COMMAND_END>() - e.getProfilingInfo<CL_PROFILING_COMMAND_SUBMIT>();}));\
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total_time+=times.back();\
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}\
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double t = median(times);\
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std::cout << " " << PERF << std::flush;\
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}
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#define BENCHMARK_CLAMDBLAS(OP, PERF) \
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{\
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std::vector<long> times;\
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double total_time = 0;\
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while(total_time*1e-9 < 1e-1){\
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cl::Event event;\
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OP;\
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queue.finish();\
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times.push_back(event.getProfilingInfo<CL_PROFILING_COMMAND_END>() - event.getProfilingInfo<CL_PROFILING_COMMAND_SUBMIT>());\
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total_time+=times.back();\
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}\
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double t = median(times);\
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std::cout << " " << PERF << std::flush;\
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}
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#define BENCHMARK_HOST(OP, PERF) \
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{\
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std::vector<int> cache_flusher(10000000, 0);\
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auto start = std::chrono::steady_clock::now();\
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OP;\
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auto end = std::chrono::steady_clock::now();\
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double t = std::chrono::duration<double, std::nano>(end - start).count();\
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std::cout << " " << PERF << std::flush;\
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}
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#define BENCHMARK_CUDA(OP, PERF) \
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{\
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std::vector<long> times;\
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double total_time = 0;\
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float time;\
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cudaEvent_t start, stop;\
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cudaEventCreate(&start);\
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cudaEventCreate(&stop);\
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while(total_time*1e-3 < 1e-1){\
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cudaEventRecord(start,0);\
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OP;\
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cudaEventRecord(stop,0);\
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cudaEventSynchronize(stop);\
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cudaEventElapsedTime(&time, start, stop);\
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times.push_back(time*1e6);\
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total_time+=time;\
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}\
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double t = median(times);\
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std::cout << " " << PERF << std::flush;\
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}
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/*---------*/
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/*--BLAS1--*/
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/*---------*/
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std::cout << "#AXPY" << std::endl;
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for(int_t N : create_log_range(1e3, 2e7, 50, 64))
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{
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std::cout << N;
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ad::array x(N, dtype), y(N, dtype);
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/* ATIDLAS */
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y = x + y; queue.flush(); queue.finish();
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BENCHMARK_ATIDLAS(y = ad::controller<atidlas::array_expression>(x + y, ad::execution_options_type(0, &events)), 3*N*dtsize/t)
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/* clAmdBlas */
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#ifdef BENCH_CLAMDBLAS
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BENCHMARK_CLAMDBLAS(clAmdBlasSaxpy(N, 1, x.data()(), 0, 1, y.data()(), 0, 1, 1, &queue(), 0, NULL, &event()), 3*N*dtsize/t)
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#endif
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/* BLAS */
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#ifdef BENCH_CBLAS
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std::vector<float> cx(N), cy(N);
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ad::copy(x, cx);
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ad::copy(y, cy);
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BENCHMARK_HOST(cblas_saxpy(N, 1, cx.data(), 1, cy.data(), 1), 3*N*dtsize/t);
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#endif
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/* CuBLAS */
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#ifdef BENCH_CUBLAS
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T *cux, *cuy;
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cudaMalloc((void**) &cux, N * sizeof(T));
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cudaMalloc((void**) &cuy, N * sizeof(T));
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BENCHMARK_CUDA(cublasSaxpy(N, 2, cux, 1, cuy, 1), 3*N*dtsize/t)
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cudaFree(cux);
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cudaFree(cuy);
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#endif
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std::cout << std::endl;
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}
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std::cout << "\n\n" << std::flush;
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// std::cout << "#DOT" << std::endl;
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// for(int_t N : create_log_range(1e3, 2e7, 50, 64))
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// {
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// std::cout << N;
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// /* ATIDLAS */
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// ad::array x(N, dtype), y(N, dtype);
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// ad::array scratch(N, dtype);
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// ad::scalar s(dtype);
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// s = dot(x,y); queue.flush(); queue.finish();
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// BENCHMARK_OPENCL(s = ad::controller<atidlas::array_expression>(dot(x,y), ad::execution_options_type(0, &event)), 2*N*dtsize/t)
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// /* clAmdBlas */
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//#ifdef BENCH_CLAMDBLAS
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// BENCHMARK_OPENCL(clAmdBlasSdot(N, s.data()(), 0, x.data()(), 0, 1, y.data()(), 0, 1, scratch.data()(), 1, &queue(), 0, NULL, &event()), 2*N*dtsize/t)
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//#endif
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// /* BLAS */
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//#ifdef BENCH_CBLAS
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// std::vector<float> cx(N), cy(N);
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// ad::copy(x, cx);
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// ad::copy(y, cy);
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// BENCHMARK_HOST(cblas_sdot(N, cx.data(), 1, cy.data(), 1), 2*N*dtsize/t);
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//#endif
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//#ifdef BENCH_CUBLAS
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// T *cux, *cuy;
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// T result;
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// cudaMalloc((void**) &cux, N * sizeof(T));
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// cudaMalloc((void**) &cuy, N * sizeof(T));
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// BENCHMARK_CUDA(cublasSdot(N, cux, 1, cuy, 1, &result), 2*N*dtsize/t)
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// cudaFree(cux);
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// cudaFree(cuy);
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//#endif
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// std::cout << std::endl;
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// }
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// std::cout << "\n\n" << std::flush;
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// /*---------*/
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// /*--BLAS2--*/
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// /*---------*/
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// //T-layout
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// std::cout << "#GEMV-T" << std::endl;
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// for(int_t N: std::vector<int>{64})
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// for(int_t M: create_full_range(128, 10000, 64))
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// {
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// std::cout << M << "," << N;
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// /* ATIDLAS */
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// ad::array A(N, M, dtype), y(M, dtype), x(N, dtype);
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// y = dot(trans(A),x); queue.flush(); queue.finish();
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// BENCHMARK_OPENCL(y = ad::controller<atidlas::array_expression>(dot(trans(A),x), ad::execution_options_type(0, &event)),(M*N + M + N)*dtsize/t);
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// #ifdef BENCH_CLAMDBLAS
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// BENCHMARK_OPENCL(clAmdBlasSgemv(clAmdBlasColumnMajor, clAmdBlasTrans, N, M, 1, A.data()(), A.ld(), x.data()(), 0, 1, 0, y.data()(), 0, 1, 1, &queue(),0, NULL, &event()), (M*N + M + N)*dtsize/t)
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// #endif
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// #ifdef BENCH_CBLAS
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// std::vector<float> cA(N*M), cx(N), cy(M);
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// ad::copy(x, cx);
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// ad::copy(y, cy);
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// ad::copy(A, cA);
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// BENCHMARK_HOST(cblas_sgemv(CblasColMajor, CblasTrans, N, M, 1, cA.data(), N, cx.data(), 1, 0, cy.data(), 1), (M*N + M + N)*dtsize/t);
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// #endif
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// #ifdef BENCH_CUBLAS
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// T *cuA, *cux, *cuy;
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// cudaMalloc((void**) &cuA, N * M * sizeof(T));
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// cudaMalloc((void**) &cux, N * sizeof(T));
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// cudaMalloc((void**) &cuy, M * sizeof(T));
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// BENCHMARK_CUDA(cublasSgemv(cublasTrans, N, M, 1, cuA, N, cux, 1, 0, cuy, 1), (M*N + M + N)*dtsize/t)
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// cudaFree(cuA);
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// cudaFree(cux);
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// cudaFree(cuy);
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// #endif
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// std::cout << std::endl;
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// }
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// std::cout << "\n\n" << std::flush;
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//// /*---------*/
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//// /*--BLAS3--*/
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//// /*---------*/
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// std::cout << "#GEMM-NT" << std::endl;
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// for(std::vector<int_t>::const_iterator Mit = BLAS3_M.begin() ; Mit != BLAS3_M.end() ; ++Mit)
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// for(std::vector<int_t>::const_iterator Nit = BLAS3_N.begin() ; Nit != BLAS3_N.end() ; ++Nit)
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// for(std::vector<int_t>::const_iterator Kit = BLAS3_K.begin() ; Kit != BLAS3_K.end() ; ++Kit)
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// {
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// int_t M = *Kit, N = *Kit, K = *Kit;
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// std::cout << M << "," << N << "," << K;
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// /* ATIDLAS */
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// ad::array C(M, N, dtype), A(M, K, dtype), B(N, K, dtype);
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// CL_BENCHMARK(C = dot(A,trans(B)), gflops((double)2*M*N*K, tres));
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// /* clAmdBlas */
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// #ifdef BENCH_CLAMDBLAS
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// CL_BENCHMARK(clAmdBlasSgemm(clAmdBlasColumnMajor, clAmdBlasNoTrans, clAmdBlasTrans, M, N, K, 1, A.data()(), A.ld(), B.data()(), B.ld(),
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// 0, C.data()(), C.ld(), 1, &ad::cl_ext::get_queue(C.context(), 0)(),0, NULL, NULL), gflops((double)2*M*N*K, tres))
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// #endif
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// /* BLAS */
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// #ifdef BENCH_CBLAS
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// std::vector<float> cC(M*N), cA(M*K), cB(N*K);
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// ad::copy(C, cC);
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// ad::copy(A, cA);
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// ad::copy(B, cB);
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// CPU_BENCHMARK(cblas_sgemm(CblasColMajor, CblasNoTrans, CblasTrans, M, N, K, 1, cA.data(), M, cB.data(), N, 1, cC.data(), M), gflops((double)2*M*N*K, tres));
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// #endif
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// std::cout << std::endl;
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// }
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}
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int main(int argc, char* argv[])
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{
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#ifdef BENCH_CLAMDBLAS
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clAmdBlasSetup();
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#endif
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ad::cl_ext::queue_properties = CL_QUEUE_PROFILING_ENABLE;
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int device_idx = 0;
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ad::cl_ext::queues_type::data_type const & queues = ad::cl_ext::queues.data();
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if(queues.size()>1){
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if(argc!=2)
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{
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std::cerr << "usage : blas-bench [DEVICE_IDX]" << std::endl;
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std::cout << "Devices available: " << std::endl;
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unsigned int current=0;
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for(const auto & queue : queues){
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cl::Device device = queue.first.getInfo<CL_CONTEXT_DEVICES>()[0];
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std::cout << current++ << ": " << device.getInfo<CL_DEVICE_NAME>() << "(" << cl::Platform(device.getInfo<CL_DEVICE_PLATFORM>()).getInfo<CL_PLATFORM_NAME>() << ")" << std::endl;
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}
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exit(EXIT_FAILURE);
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}
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else if(argc==2)
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device_idx = atoi(argv[1]);
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}
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ad::cl_ext::default_context_idx = device_idx;
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std::cout << "#Benchmark : BLAS" << std::endl;
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std::cout << "#----------------" << std::endl;
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bench<float>(ad::FLOAT_TYPE);
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#ifdef BENCH_CLAMDBLAS
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clAmdBlasTeardown();
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#endif
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}
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