// MIT License // // Copyright (c) 2025-2026 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 "primbench.hpp" #include "benchmark_utils.hpp" #include "../common/utils_custom_type.hpp" #include "../common/utils_device_ptr.hpp" #include #include #include #include #include #include #include #include #include template __global__ __launch_bounds__(BlockSize) void kernel(const T* input, T* output) { Runner::template run(input, output); } template struct inclusive_scan { template __device__ static void run(const T* input, T* output) { const unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; T values[ItemsPerThread]; for(unsigned int k = 0; k < ItemsPerThread; ++k) { values[k] = input[i * ItemsPerThread + k]; } using bscan_t = rocprim::block_scan; __shared__ typename bscan_t::storage_type storage; ROCPRIM_NO_UNROLL for(unsigned int trial = 0; trial < Trials; ++trial) { bscan_t().inclusive_scan(values, values, storage); } for(unsigned int k = 0; k < ItemsPerThread; ++k) { output[i * ItemsPerThread + k] = values[k]; } } }; template struct exclusive_scan { template __device__ static void run(const T* input, T* output) { const unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; using U = typename std::remove_reference::type; T values[ItemsPerThread]; U init = U(100); for(unsigned int k = 0; k < ItemsPerThread; ++k) { values[k] = input[i * ItemsPerThread + k]; } using bscan_t = rocprim::block_scan; __shared__ typename bscan_t::storage_type storage; ROCPRIM_NO_UNROLL for(unsigned int trial = 0; trial < Trials; ++trial) { bscan_t().exclusive_scan(values, values, init, storage); } for(unsigned int k = 0; k < ItemsPerThread; ++k) { output[i * ItemsPerThread + k] = values[k]; } } }; using inclusive_scan_uws_t = inclusive_scan; using exclusive_scan_uws_t = exclusive_scan; using inclusive_scan_rts_t = inclusive_scan; using exclusive_scan_rts_t = exclusive_scan; template std::string get_subalgo_name() { if constexpr(std::is_same_v) return "using_warp_scan"; else if constexpr(std::is_same_v) return "using_warp_scan"; else if constexpr(std::is_same_v) return "reduce_then_scan"; else if constexpr(std::is_same_v) return "reduce_then_scan"; else static_assert(sizeof(Benchmark) == 0, "Unknown subalgo"); } template std::string get_method_name() { if constexpr(std::is_same_v) return "inclusive_scan"; else if constexpr(std::is_same_v) return "inclusive_scan"; else if constexpr(std::is_same_v) return "exclusive_scan"; else if constexpr(std::is_same_v) return "exclusive_scan"; else static_assert(sizeof(Benchmark) == 0, "Unknown method"); } template struct block_scan_benchmark : public primbench::benchmark_interface { primbench::json meta() const override { return primbench::json{} .add("lvl", "block") .add("algo", "block_scan") .add("subalgo", get_subalgo_name()) .add("key_type", primbench::name()) .add("cfg", primbench::json{} .add("bs", BlockSize) .add("ipt", ItemsPerThread) .add("method", get_method_name())); } void run(primbench::state& state) override { const auto& bytes = state.size; const auto& stream = state.stream; // Ensure items is a multiple of BlockSize size_t N = bytes / sizeof(T); constexpr auto items_per_block = BlockSize * ItemsPerThread; const auto items = items_per_block * ((N + items_per_block - 1) / items_per_block); // Allocate and fill memory std::vector input(items, T(1)); common::device_ptr d_input(input); common::device_ptr d_output(items); state.set_items(items * Trials); state.add_reads(items * Trials); state.run( [&] { kernel <<>>(d_input.get(), d_output.get()); }); } };