// MIT License // // Copyright (c) 2020 Advanced Micro Devices, Inc. All rights reserved. // // Permission is hereby granted, free of charge, to any person obtaining a copy // of this software and associated documentation files (the "Software"), to deal // in the Software without restriction, including without limitation the rights // to use, copy, modify, merge, publish, distribute, sublicense, and/or sell // copies of the Software, and to permit persons to whom the Software is // furnished to do so, subject to the following conditions: // // The above copyright notice and this permission notice shall be included in // all copies or substantial portions of the Software. // // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE // SOFTWARE. #include "common_benchmark_header.hpp" // HIP API #include #include #include #ifndef DEFAULT_N const size_t DEFAULT_N = 1024 * 1024 * 32; #endif template __global__ __launch_bounds__(BlockSize) void kernel(const T* d_input, const unsigned int* d_ranks, T* d_output) { Runner::template run(d_input, d_ranks, d_output); } struct blocked_to_striped { template __device__ static void run(const T* d_input, const unsigned int*, T* d_output) { const unsigned int lid = hipThreadIdx_x; const unsigned int block_offset = hipBlockIdx_x * ItemsPerThread * BlockSize; T input[ItemsPerThread]; hipcub::LoadDirectBlocked(lid, d_input + block_offset, input); #pragma nounroll for(unsigned int trial = 0; trial < Trials; trial++) { hipcub::BlockExchange exchange; exchange.BlockedToStriped(input, input); __syncthreads(); // extra sync needed because of loop. In normal usage // sync with be cared for by the load and store functions // (outside the loop). } hipcub::StoreDirectStriped(lid, d_output + block_offset, input); } }; struct striped_to_blocked { template __device__ static void run(const T* d_input, const unsigned int*, T* d_output) { const unsigned int lid = hipThreadIdx_x; const unsigned int block_offset = hipBlockIdx_x * ItemsPerThread * BlockSize; T input[ItemsPerThread]; hipcub::LoadDirectStriped(lid, d_input + block_offset, input); #pragma nounroll for(unsigned int trial = 0; trial < Trials; trial++) { hipcub::BlockExchange exchange; exchange.StripedToBlocked(input, input); __syncthreads(); // extra sync needed because of loop. In normal usage // sync with be cared for by the load and store functions // (outside the loop). } hipcub::StoreDirectBlocked(lid, d_output + block_offset, input); } }; struct blocked_to_warp_striped { template __device__ static void run(const T* d_input, const unsigned int*, T* d_output) { const unsigned int lid = hipThreadIdx_x; const unsigned int block_offset = hipBlockIdx_x * ItemsPerThread * BlockSize; T input[ItemsPerThread]; hipcub::LoadDirectBlocked(lid, d_input + block_offset, input); #pragma nounroll for(unsigned int trial = 0; trial < Trials; trial++) { hipcub::BlockExchange exchange; exchange.BlockedToWarpStriped(input, input); __syncthreads(); // extra sync needed because of loop. In normal usage // sync with be cared for by the load and store functions // (outside the loop). } hipcub::StoreDirectWarpStriped(lid, d_output + block_offset, input); } }; struct warp_striped_to_blocked { template __device__ static void run(const T* d_input, const unsigned int*, T* d_output) { const unsigned int lid = hipThreadIdx_x; const unsigned int block_offset = hipBlockIdx_x * ItemsPerThread * BlockSize; T input[ItemsPerThread]; hipcub::LoadDirectWarpStriped(lid, d_input + block_offset, input); #pragma nounroll for(unsigned int trial = 0; trial < Trials; trial++) { hipcub::BlockExchange exchange; exchange.WarpStripedToBlocked(input, input); __syncthreads(); // extra sync needed because of loop. In normal usage // sync with be cared for by the load and store functions // (outside the loop). } hipcub::StoreDirectBlocked(lid, d_output + block_offset, input); } }; struct scatter_to_blocked { template __device__ static void run(const T* d_input, const unsigned int* d_ranks, T* d_output) { const unsigned int lid = hipThreadIdx_x; const unsigned int block_offset = hipBlockIdx_x * ItemsPerThread * BlockSize; T input[ItemsPerThread]; unsigned int ranks[ItemsPerThread]; hipcub::LoadDirectStriped(lid, d_input + block_offset, input); hipcub::LoadDirectStriped(lid, d_ranks + block_offset, ranks); #pragma nounroll for(unsigned int trial = 0; trial < Trials; trial++) { hipcub::BlockExchange exchange; exchange.ScatterToBlocked(input, input, ranks); __syncthreads(); // extra sync needed because of loop. In normal usage // sync with be cared for by the load and store functions // (outside the loop). } hipcub::StoreDirectBlocked(lid, d_output + block_offset, input); } }; struct scatter_to_striped { template __device__ static void run(const T* d_input, const unsigned int* d_ranks, T* d_output) { const unsigned int lid = hipThreadIdx_x; const unsigned int block_offset = hipBlockIdx_x * ItemsPerThread * BlockSize; T input[ItemsPerThread]; unsigned int ranks[ItemsPerThread]; hipcub::LoadDirectStriped(lid, d_input + block_offset, input); hipcub::LoadDirectStriped(lid, d_ranks + block_offset, ranks); #pragma nounroll for(unsigned int trial = 0; trial < Trials; trial++) { hipcub::BlockExchange exchange; exchange.ScatterToStriped(input, input, ranks); __syncthreads(); // extra sync needed because of loop. In normal usage // sync with be cared for by the load and store functions // (outside the loop). } hipcub::StoreDirectStriped(lid, d_output + block_offset, input); } }; template void run_benchmark(benchmark::State& state, hipStream_t stream, size_t N) { constexpr auto items_per_block = BlockSize * ItemsPerThread; const auto size = items_per_block * ((N + items_per_block - 1) / items_per_block); std::vector input(size); // Fill input for(size_t i = 0; i < size; i++) { input[i] = T(i); } std::vector ranks(size); // Fill ranks (for scatter operations) std::mt19937 gen; for(size_t bi = 0; bi < size / items_per_block; bi++) { auto block_ranks = ranks.begin() + bi * items_per_block; std::iota(block_ranks, block_ranks + items_per_block, 0); std::shuffle(block_ranks, block_ranks + items_per_block, gen); } T* d_input; unsigned int* d_ranks; T* d_output; HIP_CHECK(hipMalloc(&d_input, size * sizeof(T))); HIP_CHECK(hipMalloc(&d_ranks, size * sizeof(unsigned int))); HIP_CHECK(hipMalloc(&d_output, size * sizeof(T))); HIP_CHECK(hipMemcpy(d_input, input.data(), size * sizeof(T), hipMemcpyHostToDevice)); HIP_CHECK(hipMemcpy(d_ranks, ranks.data(), size * sizeof(unsigned int), hipMemcpyHostToDevice)); HIP_CHECK(hipDeviceSynchronize()); for(auto _ : state) { auto start = std::chrono::high_resolution_clock::now(); hipLaunchKernelGGL(HIP_KERNEL_NAME(kernel), dim3(size / items_per_block), dim3(BlockSize), 0, stream, d_input, d_ranks, d_output); HIP_CHECK(hipPeekAtLastError()); HIP_CHECK(hipDeviceSynchronize()); auto end = std::chrono::high_resolution_clock::now(); auto elapsed_seconds = std::chrono::duration_cast>(end - start); state.SetIterationTime(elapsed_seconds.count()); } state.SetBytesProcessed(state.iterations() * Trials * size * sizeof(T)); state.SetItemsProcessed(state.iterations() * Trials * size); HIP_CHECK(hipFree(d_input)); HIP_CHECK(hipFree(d_ranks)); HIP_CHECK(hipFree(d_output)); } #define CREATE_BENCHMARK(T, BS, IPT) \ benchmark::RegisterBenchmark(std::string("block_exchange.sub_algorithm_name:" \ + name) \ .c_str(), \ &run_benchmark, \ stream, \ size) #define BENCHMARK_TYPE(type, block) \ CREATE_BENCHMARK(type, block, 1), CREATE_BENCHMARK(type, block, 2), \ CREATE_BENCHMARK(type, block, 3), CREATE_BENCHMARK(type, block, 4), \ CREATE_BENCHMARK(type, block, 7), CREATE_BENCHMARK(type, block, 8) template void add_benchmarks(const std::string& name, std::vector& benchmarks, hipStream_t stream, size_t size) { using custom_float2 = benchmark_utils::custom_type; using custom_double2 = benchmark_utils::custom_type; std::vector bs = { BENCHMARK_TYPE(int, 256), BENCHMARK_TYPE(int8_t, 256), BENCHMARK_TYPE(long long, 256), BENCHMARK_TYPE(custom_float2, 256), BENCHMARK_TYPE(custom_double2, 256), }; benchmarks.insert(benchmarks.end(), bs.begin(), bs.end()); } int main(int argc, char* argv[]) { cli::Parser parser(argc, argv); parser.set_optional("size", "size", DEFAULT_N, "number of values"); parser.set_optional("trials", "trials", -1, "number of iterations"); parser.run_and_exit_if_error(); // Parse argv benchmark::Initialize(&argc, argv); const size_t size = parser.get("size"); const int trials = parser.get("trials"); std::cout << "benchmark_block_exchange" << std::endl; // HIP hipStream_t stream = 0; // default hipDeviceProp_t devProp; int device_id = 0; HIP_CHECK(hipGetDevice(&device_id)); HIP_CHECK(hipGetDeviceProperties(&devProp, device_id)); std::cout << "[HIP] Device name: " << devProp.name << std::endl; // Add benchmarks std::vector benchmarks; add_benchmarks("blocked_to_striped", benchmarks, stream, size); add_benchmarks("striped_to_blocked", benchmarks, stream, size); add_benchmarks("blocked_to_warp_striped", benchmarks, stream, size); add_benchmarks("warp_striped_to_blocked", benchmarks, stream, size); add_benchmarks("scatter_to_blocked", benchmarks, stream, size); add_benchmarks("scatter_to_striped", benchmarks, stream, size); // Use manual timing for(auto& b : benchmarks) { b->UseManualTime(); b->Unit(benchmark::kMillisecond); } // Force number of iterations if(trials > 0) { for(auto& b : benchmarks) { b->Iterations(trials); } } // Run benchmarks benchmark::RunSpecifiedBenchmarks(); return 0; }