gaspersic/triton
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Now using events to time autotuning

Browse Source
This commit is contained in:
Philippe Tillet
2015-02-06 22:11:03 -05:00
parent 385f007c0b
commit b768e913c9
7 changed files with 163 additions and 95 deletions
Download Patch File Download Diff File Expand all files Collapse all files

167
bench/blas.cpp
View File

@@ -30,7 +30,6 @@ void bench(ad::numeric_type dtype)
while(total_time*1e-9 < 1e-1){\
cl::Event event;\
OP;\
queue.flush();\
queue.finish();\
times.push_back(event.getProfilingInfo<CL_PROFILING_COMMAND_END>() - event.getProfilingInfo<CL_PROFILING_COMMAND_SUBMIT>());\
total_time+=times.back();\
@@ -70,96 +69,114 @@ void bench(ad::numeric_type dtype)
std::cout << " " << PERF << std::flush;\
}
/*---------*/
/*--BLAS1--*/
/*---------*/
std::cout << "#AXPY" << std::endl;
for(auto N : BLAS1_N)
{
std::cout << N;
ad::array x(N, dtype), y(N, dtype);
cl::CommandQueue & queue = ad::cl_ext::queues[x.context()][0];
/* ATIDLAS */
y = x + y; queue.flush(); queue.finish();
BENCHMARK_OPENCL(y = ad::controller<atidlas::array_expression>(x + y, ad::execution_options_type(0, &event)), 3*N*dtsize/t)
/* clAmdBlas */
#ifdef BENCH_CLAMDBLAS
BENCHMARK_OPENCL(clAmdBlasSaxpy(N, 1, x.data()(), 0, 1, y.data()(), 0, 1, 1, &queue(), 0, NULL, &event()), 3*N*dtsize/t)
#endif
/* BLAS */
#ifdef BENCH_CBLAS
std::vector<float> cx(N), cy(N);
ad::copy(x, cx);
ad::copy(y, cy);
BENCHMARK_HOST(cblas_saxpy(N, 1, cx.data(), 1, cy.data(), 1), 3*N*dtsize/t);
#endif
/* CuBLAS */
#ifdef BENCH_CUBLAS
T *cux, *cuy;
cudaMalloc((void**) &cux, N * sizeof(T));
cudaMalloc((void**) &cuy, N * sizeof(T));
BENCHMARK_CUDA(cublasSaxpy(N, 2, cux, 1, cuy, 1), 3*N*dtsize/t)
cudaFree(cux);
cudaFree(cuy);
#endif
std::cout << std::endl;
}
std::cout << "\n\n" << std::flush;
// std::cout << "#DOT" << std::endl;
// for(std::vector<int_t>::const_iterator it = BLAS1_N.begin() ; it != BLAS1_N.end() ; ++it)
// /*---------*/
// /*--BLAS1--*/
// /*---------*/
// std::cout << "#AXPY" << std::endl;
// for(int_t N : create_log_range(1e3, 2e7, 50, 64))
// {
// int_t N = *it;
// std::cout << N;
// /* ATIDLAS */
// ad::array x(N, dtype), y(N, dtype);
// ad::array scratch(N, dtype);
// ad::scalar s(dtype);
// CL_BENCHMARK(s = dot(x,y), bandwidth(2*N, tres, dtsize));
// cl::CommandQueue & queue = ad::cl_ext::queues[x.context()][0];
// /* ATIDLAS */
// y = x + y; queue.flush(); queue.finish();
// BENCHMARK_OPENCL(y = ad::controller<atidlas::array_expression>(x + y, ad::execution_options_type(0, &event)), 3*N*dtsize/t)
// /* clAmdBlas */
//#ifdef BENCH_CLAMDBLAS
// CL_BENCHMARK(clAmdBlasSdot(N, s.data()(), 0, x.data()(), 0, 1, y.data()(), 0, 1, scratch.data()(), 1, &ad::cl_ext::get_queue(x.context(), 0)(), 0, NULL, NULL), bandwidth(2*N, tres, dtsize))
// BENCHMARK_OPENCL(clAmdBlasSaxpy(N, 1, x.data()(), 0, 1, y.data()(), 0, 1, 1, &queue(), 0, NULL, &event()), 3*N*dtsize/t)
//#endif
// /* BLAS */
//#ifdef BENCH_CBLAS
// std::vector<float> cx(N), cy(N);
// ad::copy(x, cx);
// ad::copy(y, cy);
// CPU_BENCHMARK(cblas_sdot(N, cx.data(), 1, cy.data(), 1), bandwidth(2*N, tres, dtsize));
// BENCHMARK_HOST(cblas_saxpy(N, 1, cx.data(), 1, cy.data(), 1), 3*N*dtsize/t);
//#endif
// /* CuBLAS */
//#ifdef BENCH_CUBLAS
// T *cux, *cuy;
// cudaMalloc((void**) &cux, N * sizeof(T));
// cudaMalloc((void**) &cuy, N * sizeof(T));
// BENCHMARK_CUDA(cublasSaxpy(N, 2, cux, 1, cuy, 1), 3*N*dtsize/t)
// cudaFree(cux);
// cudaFree(cuy);
//#endif
// std::cout << std::endl;
// }
// std::cout << "\n\n" << std::flush;
// /*---------*/
// /*--BLAS2--*/
// /*---------*/
// //T-layout
// std::cout << "#GEMV-T" << std::endl;
// for(std::vector<int>::const_iterator Mit = BLAS2_M.begin() ; Mit != BLAS2_M.end() ; ++Mit)
// for(std::vector<int_t>::const_iterator Nit = BLAS2_N.begin() ; Nit != BLAS2_N.end() ; ++Nit)
// {
// int_t M = *Mit;
// int_t N = *Nit;
// std::cout << M << "," << N;
// /* ATIDLAS */
// ad::array A(N, M, dtype), y(M, dtype), x(N, dtype);
// CL_BENCHMARK(y = dot(trans(A),x), bandwidth(M*N + M + N, tres, dtsize));
// /* clAmdBlas */
// #ifdef BENCH_CLAMDBLAS
// CL_BENCHMARK(clAmdBlasSgemv(clAmdBlasColumnMajor, clAmdBlasTrans, N, M, 1, A.data()(), A.ld(), x.data()(), 0, 1, 0, y.data()(), 0, 1, 1, &ad::cl_ext::get_queue(x.context(), 0)(),0, NULL, NULL), bandwidth(M*N + M + N, tres, dtsize))
// #endif
// /* BLAS */
// #ifdef BENCH_CBLAS
// std::vector<float> cA(N*M), cx(N), cy(M);
// ad::copy(x, cx);
// ad::copy(y, cy);
// ad::copy(A, cA);
// CPU_BENCHMARK(cblas_sgemv(CblasColMajor, CblasTrans, N, M, 1, cA.data(), N, cx.data(), 1, 0, cy.data(), 1), bandwidth(M*N + M + N, tres, dtsize));
// #endif
// std::cout << std::endl;
// }
// std::cout << "\n\n" << std::flush;
// std::cout << "#DOT" << std::endl;
// for(int_t N : create_log_range(1e3, 2e7, 50, 64))
// {
// std::cout << N;
// /* ATIDLAS */
// ad::array x(N, dtype), y(N, dtype);
// cl::CommandQueue & queue = ad::cl_ext::queues[x.context()][0];
// ad::array scratch(N, dtype);
// ad::scalar s(dtype);
// s = dot(x,y); queue.flush(); queue.finish();
// BENCHMARK_OPENCL(s = ad::controller<atidlas::array_expression>(dot(x,y), ad::execution_options_type(0, &event)), 2*N*dtsize/t)
// /* clAmdBlas */
//#ifdef BENCH_CLAMDBLAS
// 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)
//#endif
// /* BLAS */
//#ifdef BENCH_CBLAS
// std::vector<float> cx(N), cy(N);
// ad::copy(x, cx);
// ad::copy(y, cy);
// BENCHMARK_HOST(cblas_sdot(N, cx.data(), 1, cy.data(), 1), 2*N*dtsize/t);
//#endif
//#ifdef BENCH_CUBLAS
// T *cux, *cuy;
// T result;
// cudaMalloc((void**) &cux, N * sizeof(T));
// cudaMalloc((void**) &cuy, N * sizeof(T));
// BENCHMARK_CUDA(cublasSdot(N, cux, 1, cuy, 1, &result), 2*N*dtsize/t)
// cudaFree(cux);
// cudaFree(cuy);
//#endif
// std::cout << std::endl;
// }
// std::cout << "\n\n" << std::flush;
/*---------*/
/*--BLAS2--*/
/*---------*/
//T-layout
std::cout << "#GEMV-T" << std::endl;
for(int_t N: std::vector<int>{64})
for(int_t M: create_full_range(128, 10000, 64))
{
std::cout << M << "," << N;
/* ATIDLAS */
ad::array A(N, M, dtype), y(M, dtype), x(N, dtype);
cl::CommandQueue & queue = ad::cl_ext::queues[x.context()][0];
y = dot(trans(A),x); queue.flush(); queue.finish();
BENCHMARK_OPENCL(y = ad::controller<atidlas::array_expression>(dot(trans(A),x), ad::execution_options_type(0, &event)),(M*N + M + N)*dtsize/t);
#ifdef BENCH_CLAMDBLAS
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)
#endif
#ifdef BENCH_CBLAS
std::vector<float> cA(N*M), cx(N), cy(M);
ad::copy(x, cx);
ad::copy(y, cy);
ad::copy(A, cA);
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);
#endif
#ifdef BENCH_CUBLAS
T *cuA, *cux, *cuy;
cudaMalloc((void**) &cuA, N * M * sizeof(T));
cudaMalloc((void**) &cux, N * sizeof(T));
cudaMalloc((void**) &cuy, M * sizeof(T));
BENCHMARK_CUDA(cublasSgemv(cublasTrans, N, M, 1, cuA, N, cux, 1, 0, cuy, 1), (M*N + M + N)*dtsize/t)
cudaFree(cuA);
cudaFree(cux);
cudaFree(cuy);
#endif
std::cout << std::endl;
}
std::cout << "\n\n" << std::flush;
//// /*---------*/
//// /*--BLAS3--*/