// ================================================================================================= // This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This // project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max- // width of 100 characters per line. // // Author(s): // Cedric Nugteren // // This file demonstrates the use of the DAXPY routine with the C++ CUDA API of CLBlast. // // Note that this example is meant for illustration purposes only. CLBlast provides other programs // for performance benchmarking ('client_xxxxx') and for correctness testing ('test_xxxxx'). // // ================================================================================================= #include #include #include // Includes the CUDA driver API #include // Includes the CLBlast library #include // ================================================================================================= // Example use of the double-precision Xaxpy routine DAXPY int main() { // CUDA device selection const auto device_id = 0; // Example DAXPY arguments const size_t n = 8192; const double alpha = 0.7; // Initializes the OpenCL device cuInit(0); CUdevice device; cuDeviceGet(&device, device_id); // Creates the OpenCL context and stream CUcontext context; cuCtxCreate(&context, 0, device); CUstream stream; cuStreamCreate(&stream, CU_STREAM_NON_BLOCKING); // Populate host matrices with some example data auto host_a = std::vector(n); auto host_b = std::vector(n); for (auto &item: host_a) { item = 12.193; } for (auto &item: host_b) { item = -8.199; } // Copy the matrices to the device CUdeviceptr device_a; CUdeviceptr device_b; cuMemAlloc(&device_a, host_a.size()*sizeof(double)); cuMemAlloc(&device_b, host_b.size()*sizeof(double)); cuMemcpyHtoDAsync(device_a, host_a.data(), host_a.size()*sizeof(double), stream); cuMemcpyHtoDAsync(device_b, host_b.data(), host_b.size()*sizeof(double), stream); // Start the timer auto start_time = std::chrono::steady_clock::now(); // Call the DAXPY routine. Note that the type of alpha (double) determines the precision. const auto status = clblast::Axpy(n, alpha, device_a, 0, 1, device_b, 0, 1, context, device); cuStreamSynchronize(stream); // Record the execution time auto elapsed_time = std::chrono::steady_clock::now() - start_time; auto time_ms = std::chrono::duration(elapsed_time).count(); // Example completed. See "clblast_cuda.h" for status codes (0 -> success). printf("Completed DAXPY in %.3lf ms with status %d\n", time_ms, static_cast(status)); // Clean-up cuMemFree(device_a); cuMemFree(device_b); cuStreamDestroy(stream); return 0; } // =================================================================================================