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 #include <algorithm> #include <cassert> #include <chrono> #include <cstdio> #include <functional> #include <iomanip> #include <iostream> #include <random> #include <vector>
#include <cuda_runtime.h>
#define CHECK_CUDA_ERROR(val) check((val), #val, __FILE__, __LINE__) template <typename T> void check(T err, const char* const func, const char* const file, const int line) { if (err != cudaSuccess) { std::cerr << "CUDA Runtime Error at: " << file << ":" << line << std::endl; std::cerr << cudaGetErrorString(err) << " " << func << std::endl; std::exit(EXIT_FAILURE); } }
#define CHECK_LAST_CUDA_ERROR() checkLast(__FILE__, __LINE__) void checkLast(const char* const file, const int line) { cudaError_t err{cudaGetLastError()}; if (err != cudaSuccess) { std::cerr << "CUDA Runtime Error at: " << file << ":" << line << std::endl; std::cerr << cudaGetErrorString(err) << std::endl; std::exit(EXIT_FAILURE); } }
template <class T> float measure_performance(std::function<T(cudaStream_t)> bound_function, cudaStream_t stream, int num_repeats = 100, int num_warmups = 100) { cudaEvent_t start, stop; float time;
CHECK_CUDA_ERROR(cudaEventCreate(&start)); CHECK_CUDA_ERROR(cudaEventCreate(&stop));
for (int i{0}; i < num_warmups; ++i) { bound_function(stream); }
CHECK_CUDA_ERROR(cudaStreamSynchronize(stream));
CHECK_CUDA_ERROR(cudaEventRecord(start, stream)); for (int i{0}; i < num_repeats; ++i) { bound_function(stream); } CHECK_CUDA_ERROR(cudaEventRecord(stop, stream)); CHECK_CUDA_ERROR(cudaEventSynchronize(stop)); CHECK_LAST_CUDA_ERROR(); CHECK_CUDA_ERROR(cudaEventElapsedTime(&time, start, stop)); CHECK_CUDA_ERROR(cudaEventDestroy(start)); CHECK_CUDA_ERROR(cudaEventDestroy(stop));
float const latency{time / num_repeats};
return latency; }
constexpr size_t div_up(size_t a, size_t b) { return (a + b  1) / b; }
template <typename T> __global__ void transpose_read_coalesced(T* output_matrix, T const* input_matrix, size_t M, size_t N) { size_t const j{threadIdx.x + blockIdx.x * blockDim.x}; size_t const i{threadIdx.y + blockIdx.y * blockDim.y}; size_t const from_idx{i * N + j}; if ((i < M) && (j < N)) { size_t const to_idx{j * M + i}; output_matrix[to_idx] = input_matrix[from_idx]; } }
template <typename T> __global__ void transpose_write_coalesced(T* output_matrix, T const* input_matrix, size_t M, size_t N) { size_t const j{threadIdx.x + blockIdx.x * blockDim.x}; size_t const i{threadIdx.y + blockIdx.y * blockDim.y}; size_t const to_idx{i * M + j}; if ((i < N) && (j < M)) { size_t const from_idx{j * N + i}; output_matrix[to_idx] = input_matrix[from_idx]; } }
template <typename T> void launch_transpose_read_coalesced(T* output_matrix, T const* input_matrix, size_t M, size_t N, cudaStream_t stream) { constexpr size_t const warp_size{32}; dim3 const threads_per_block{warp_size, warp_size}; dim3 const blocks_per_grid{static_cast<unsigned int>(div_up(N, warp_size)), static_cast<unsigned int>(div_up(M, warp_size))}; transpose_read_coalesced<<<blocks_per_grid, threads_per_block, 0, stream>>>( output_matrix, input_matrix, M, N); CHECK_LAST_CUDA_ERROR(); }
template <typename T> void launch_transpose_write_coalesced(T* output_matrix, T const* input_matrix, size_t M, size_t N, cudaStream_t stream) { constexpr size_t const warp_size{32}; dim3 const threads_per_block{warp_size, warp_size}; dim3 const blocks_per_grid{static_cast<unsigned int>(div_up(M, warp_size)), static_cast<unsigned int>(div_up(N, warp_size))}; transpose_write_coalesced<<<blocks_per_grid, threads_per_block, 0, stream>>>(output_matrix, input_matrix, M, N); CHECK_LAST_CUDA_ERROR(); }
template <typename T, size_t BLOCK_SIZE = 32> __global__ void transpose_read_write_coalesced(T* output_matrix, T const* input_matrix, size_t M, size_t N) { __shared__ T buffer[BLOCK_SIZE][BLOCK_SIZE + 1];
size_t const matrix_j{threadIdx.x + blockIdx.x * blockDim.x}; size_t const matrix_i{threadIdx.y + blockIdx.y * blockDim.y}; size_t const matrix_from_idx{matrix_i * N + matrix_j};
if ((matrix_i < M) && (matrix_j < N)) { buffer[threadIdx.x][threadIdx.y] = input_matrix[matrix_from_idx]; }
__syncthreads();
size_t const matrix_transposed_j{threadIdx.x + blockIdx.y * blockDim.y}; size_t const matrix_transposed_i{threadIdx.y + blockIdx.x * blockDim.x};
if ((matrix_transposed_i < N) && (matrix_transposed_j < M)) { size_t const to_idx{matrix_transposed_i * M + matrix_transposed_j}; output_matrix[to_idx] = buffer[threadIdx.y][threadIdx.x]; } }
template <typename T> void launch_transpose_read_write_coalesced(T* output_matrix, T const* input_matrix, size_t M, size_t N, cudaStream_t stream) { constexpr size_t const warp_size{32}; dim3 const threads_per_block{warp_size, warp_size}; dim3 const blocks_per_grid{static_cast<unsigned int>(div_up(N, warp_size)), static_cast<unsigned int>(div_up(M, warp_size))}; transpose_read_write_coalesced<T, warp_size> <<<blocks_per_grid, threads_per_block, 0, stream>>>(output_matrix, input_matrix, M, N); CHECK_LAST_CUDA_ERROR(); }
template <typename T> bool is_equal(T const* data_1, T const* data_2, size_t size) { for (size_t i{0}; i < size; ++i) { if (data_1[i] != data_2[i]) { return false; } } return true; }
template <typename T> bool verify_transpose_implementation( std::function<void(T*, T const*, size_t, size_t, cudaStream_t)> transpose_function, size_t M, size_t N) { std::mt19937 gen{0}; cudaStream_t stream; size_t const matrix_size{M * N}; std::vector<T> matrix(matrix_size, 0.0f); std::vector<T> matrix_transposed(matrix_size, 1.0f); std::vector<T> matrix_transposed_reference(matrix_size, 2.0f); std::uniform_real_distribution<T> uniform_dist(256, 256); for (size_t i{0}; i < matrix_size; ++i) { matrix[i] = uniform_dist(gen); } for (size_t i{0}; i < M; ++i) { for (size_t j{0}; j < N; ++j) { size_t const from_idx{i * N + j}; size_t const to_idx{j * M + i}; matrix_transposed_reference[to_idx] = matrix[from_idx]; } } T* d_matrix; T* d_matrix_transposed; CHECK_CUDA_ERROR(cudaMalloc(&d_matrix, matrix_size * sizeof(T))); CHECK_CUDA_ERROR(cudaMalloc(&d_matrix_transposed, matrix_size * sizeof(T))); CHECK_CUDA_ERROR(cudaStreamCreate(&stream)); CHECK_CUDA_ERROR(cudaMemcpy(d_matrix, matrix.data(), matrix_size * sizeof(T), cudaMemcpyHostToDevice)); transpose_function(d_matrix_transposed, d_matrix, M, N, stream); CHECK_CUDA_ERROR(cudaStreamSynchronize(stream)); CHECK_CUDA_ERROR(cudaMemcpy(matrix_transposed.data(), d_matrix_transposed, matrix_size * sizeof(T), cudaMemcpyDeviceToHost)); bool const correctness{is_equal(matrix_transposed.data(), matrix_transposed_reference.data(), matrix_size)}; CHECK_CUDA_ERROR(cudaFree(d_matrix)); CHECK_CUDA_ERROR(cudaFree(d_matrix_transposed)); CHECK_CUDA_ERROR(cudaStreamDestroy(stream)); return correctness; }
template <typename T> void profile_transpose_implementation( std::function<void(T*, T const*, size_t, size_t, cudaStream_t)> transpose_function, size_t M, size_t N) { constexpr int const num_repeats{100}; constexpr int const num_warmups{10}; cudaStream_t stream; size_t const matrix_size{M * N}; T* d_matrix; T* d_matrix_transposed; CHECK_CUDA_ERROR(cudaMalloc(&d_matrix, matrix_size * sizeof(T))); CHECK_CUDA_ERROR(cudaMalloc(&d_matrix_transposed, matrix_size * sizeof(T))); CHECK_CUDA_ERROR(cudaStreamCreate(&stream));
std::function<void(cudaStream_t)> const transpose_function_wrapped{ std::bind(transpose_function, d_matrix_transposed, d_matrix, M, N, std::placeholders::_1)}; float const transpose_function_latency{measure_performance( transpose_function_wrapped, stream, num_repeats, num_warmups)}; std::cout << std::fixed << std::setprecision(3) << "Latency: " << transpose_function_latency << " ms" << std::endl; CHECK_CUDA_ERROR(cudaFree(d_matrix)); CHECK_CUDA_ERROR(cudaFree(d_matrix_transposed)); CHECK_CUDA_ERROR(cudaStreamDestroy(stream)); }
int main() { for (size_t m{1}; m <= 64; ++m) { for (size_t n{1}; n <= 64; ++n) { assert(verify_transpose_implementation<float>( &launch_transpose_write_coalesced<float>, m, n)); assert(verify_transpose_implementation<float>( &launch_transpose_read_coalesced<float>, m, n)); assert(verify_transpose_implementation<float>( &launch_transpose_read_write_coalesced<float>, m, n)); } }
size_t const M{12800}; size_t const N{12800}; std::cout << M << " x " << N << " Matrix" << std::endl; std::cout << "Transpose Write Coalesced" << std::endl; profile_transpose_implementation<float>( &launch_transpose_write_coalesced<float>, M, N); std::cout << "Transpose Read Coalesced" << std::endl; profile_transpose_implementation<float>( &launch_transpose_read_coalesced<float>, M, N); std::cout << "Transpose Read and Write Coalesced" << std::endl; profile_transpose_implementation<float>( &launch_transpose_read_write_coalesced<float>, M, N); }
