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// MIT License
//
// Copyright (c) 2020-2024 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 "hipcub/warp/warp_reduce.hpp"
#ifndef DEFAULT_N
const size_t DEFAULT_N = 1024 * 1024 * 32;
#endif
template<class T, unsigned int WarpSize, unsigned int Trials>
__global__ __launch_bounds__(64)
auto warp_reduce_kernel(const T* d_input, T* d_output)
-> std::enable_if_t<benchmark_utils::device_test_enabled_for_warp_size_v<WarpSize>>
{
const unsigned int i = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x;
auto value = d_input[i];
using wreduce_t = hipcub::WarpReduce<T, WarpSize>;
__shared__ typename wreduce_t::TempStorage storage;
auto reduce_op = hipcub::Sum();
#pragma nounroll
for(unsigned int trial = 0; trial < Trials; trial++)
{
value = wreduce_t(storage).Reduce(value, reduce_op);
}
d_output[i] = value;
}
template<class T, unsigned int WarpSize, unsigned int Trials>
__global__ __launch_bounds__(64)
auto warp_reduce_kernel(const T* /*d_input*/, T* /*d_output*/)
-> std::enable_if_t<!benchmark_utils::device_test_enabled_for_warp_size_v<WarpSize>>
{}
template<class T, class Flag, unsigned int WarpSize, unsigned int Trials>
__global__ __launch_bounds__(64)
auto segmented_warp_reduce_kernel(const T* d_input, Flag* d_flags, T* d_output)
-> std::enable_if_t<benchmark_utils::device_test_enabled_for_warp_size_v<WarpSize>>
{
const unsigned int i = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x;
auto value = d_input[i];
auto flag = d_flags[i];
using wreduce_t = hipcub::WarpReduce<T, WarpSize>;
__shared__ typename wreduce_t::TempStorage storage;
#pragma nounroll
for(unsigned int trial = 0; trial < Trials; trial++)
{
value = wreduce_t(storage).HeadSegmentedSum(value, flag);
}
d_output[i] = value;
}
template<class T, class Flag, unsigned int WarpSize, unsigned int Trials>
__global__ __launch_bounds__(64)
auto segmented_warp_reduce_kernel(const T* /*d_input*/, Flag* /*d_flags*/, T* /*d_output*/)
-> std::enable_if_t<!benchmark_utils::device_test_enabled_for_warp_size_v<WarpSize>>
{}
template<bool Segmented,
unsigned int WarpSize,
unsigned int BlockSize,
unsigned int Trials,
class T,
class Flag>
inline auto execute_warp_reduce_kernel(
T* input, T* output, Flag* /* flags */, size_t size, hipStream_t stream) ->
typename std::enable_if<!Segmented>::type
{
hipLaunchKernelGGL(HIP_KERNEL_NAME(warp_reduce_kernel<T, WarpSize, Trials>),
dim3(size / BlockSize),
dim3(BlockSize),
0,
stream,
input,
output);
HIP_CHECK(hipPeekAtLastError());
}
template<bool Segmented,
unsigned int WarpSize,
unsigned int BlockSize,
unsigned int Trials,
class T,
class Flag>
inline auto
execute_warp_reduce_kernel(T* input, T* output, Flag* flags, size_t size, hipStream_t stream) ->
typename std::enable_if<Segmented>::type
{
hipLaunchKernelGGL(HIP_KERNEL_NAME(segmented_warp_reduce_kernel<T, Flag, WarpSize, Trials>),
dim3(size / BlockSize),
dim3(BlockSize),
0,
stream,
input,
flags,
output);
HIP_CHECK(hipPeekAtLastError());
}
template<bool Segmented,
class T,
unsigned int WarpSize,
unsigned int BlockSize,
unsigned int Trials = 100>
void run_benchmark(benchmark::State& state, hipStream_t stream, size_t N)
{
using flag_type = unsigned char;
const auto size = BlockSize * ((N + BlockSize - 1) / BlockSize);
std::vector<T> input = benchmark_utils::get_random_data<T>(size, T(0), T(10));
std::vector<flag_type> flags = benchmark_utils::get_random_data<flag_type>(size, 0, 1);
T* d_input;
flag_type* d_flags;
T* d_output;
HIP_CHECK(hipMalloc(&d_input, size * sizeof(T)));
HIP_CHECK(hipMalloc(&d_flags, size * sizeof(flag_type)));
HIP_CHECK(hipMalloc(&d_output, size * sizeof(T)));
HIP_CHECK(hipMemcpy(d_input, input.data(), size * sizeof(T), hipMemcpyHostToDevice));
HIP_CHECK(hipMemcpy(d_flags, flags.data(), size * sizeof(flag_type), hipMemcpyHostToDevice));
HIP_CHECK(hipDeviceSynchronize());
for(auto _ : state)
{
auto start = std::chrono::high_resolution_clock::now();
execute_warp_reduce_kernel<Segmented, WarpSize, BlockSize, Trials>(d_input,
d_output,
d_flags,
size,
stream);
HIP_CHECK(hipDeviceSynchronize());
auto end = std::chrono::high_resolution_clock::now();
auto elapsed_seconds
= std::chrono::duration_cast<std::chrono::duration<double>>(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_output));
HIP_CHECK(hipFree(d_flags));
}
#define CREATE_BENCHMARK(T, WS, BS) \
benchmark::RegisterBenchmark(std::string("warp_reduce<data_type:" #T ",warp_size:" #WS \
",block_size:" #BS ">.sub_algorithm_name:" \
+ name) \
.c_str(), \
&run_benchmark<Segmented, T, WS, BS>, \
stream, \
size)
// If warp size limit is 16
#define BENCHMARK_TYPE_WS16(type) CREATE_BENCHMARK(type, 15, 32), CREATE_BENCHMARK(type, 16, 32)
// If warp size limit is 32
#define BENCHMARK_TYPE_WS32(type) \
BENCHMARK_TYPE_WS16(type), CREATE_BENCHMARK(type, 31, 32), CREATE_BENCHMARK(type, 32, 32), \
CREATE_BENCHMARK(type, 32, 64)
// If warp size limit is 64
#define BENCHMARK_TYPE_WS64(type) \
BENCHMARK_TYPE_WS32(type), CREATE_BENCHMARK(type, 37, 64), CREATE_BENCHMARK(type, 61, 64), \
CREATE_BENCHMARK(type, 64, 64)
template<bool Segmented>
void add_benchmarks(const std::string& name,
std::vector<benchmark::internal::Benchmark*>& benchmarks,
hipStream_t stream,
size_t size)
{
std::vector<benchmark::internal::Benchmark*> bs = {
#if HIPCUB_WARP_THREADS_MACRO == 16
BENCHMARK_TYPE_WS16(int),
BENCHMARK_TYPE_WS16(float),
BENCHMARK_TYPE_WS16(double),
BENCHMARK_TYPE_WS16(int8_t),
BENCHMARK_TYPE_WS16(uint8_t)
#elif HIPCUB_WARP_THREADS_MACRO == 32
BENCHMARK_TYPE_WS32(int),
BENCHMARK_TYPE_WS32(float),
BENCHMARK_TYPE_WS32(double),
BENCHMARK_TYPE_WS32(int8_t),
BENCHMARK_TYPE_WS32(uint8_t)
#else
BENCHMARK_TYPE_WS64(int),
BENCHMARK_TYPE_WS64(float),
BENCHMARK_TYPE_WS64(double),
BENCHMARK_TYPE_WS64(int8_t),
BENCHMARK_TYPE_WS64(uint8_t)
#endif
};
benchmarks.insert(benchmarks.end(), bs.begin(), bs.end());
}
int main(int argc, char* argv[])
{
cli::Parser parser(argc, argv);
parser.set_optional<size_t>("size", "size", DEFAULT_N, "number of values");
parser.set_optional<int>("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_t>("size");
const int trials = parser.get<int>("trials");
std::cout << "benchmark_warp_reduce" << 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<benchmark::internal::Benchmark*> benchmarks;
add_benchmarks<false>("reduce", benchmarks, stream, size);
add_benchmarks<true>("segmented_reduce", 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;
}
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