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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/device/device_reduce.hpp"
#ifndef DEFAULT_N
const size_t DEFAULT_N = 1024 * 1024 * 128;
#endif
const unsigned int batch_size = 10;
const unsigned int warmup_size = 5;
template<class T, class OutputT, class ReduceKernel>
void run_benchmark(benchmark::State& state,
size_t size,
const hipStream_t stream,
ReduceKernel reduce)
{
std::vector<T> input = benchmark_utils::get_random_data<T>(size, T(0), T(1000));
T* d_input;
OutputT* d_output;
HIP_CHECK(hipMalloc(&d_input, size * sizeof(T)));
HIP_CHECK(hipMalloc(&d_output, sizeof(OutputT)));
HIP_CHECK(hipMemcpy(d_input, input.data(), size * sizeof(T), hipMemcpyHostToDevice));
HIP_CHECK(hipDeviceSynchronize());
// Allocate temporary storage memory
size_t temp_storage_size_bytes = 0;
void* d_temp_storage = nullptr;
// Get size of d_temp_storage
HIP_CHECK(reduce(d_temp_storage, temp_storage_size_bytes, d_input, d_output, size, stream));
HIP_CHECK(hipMalloc(&d_temp_storage, temp_storage_size_bytes));
HIP_CHECK(hipDeviceSynchronize());
for(size_t i = 0; i < warmup_size; i++)
{
HIP_CHECK(reduce(d_temp_storage, temp_storage_size_bytes, d_input, d_output, size, stream));
}
HIP_CHECK(hipDeviceSynchronize());
for(auto _ : state)
{
auto start = std::chrono::high_resolution_clock::now();
for(size_t i = 0; i < batch_size; i++)
{
HIP_CHECK(
reduce(d_temp_storage, temp_storage_size_bytes, d_input, d_output, size, stream));
}
HIP_CHECK(hipStreamSynchronize(stream));
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() * batch_size * size * sizeof(T));
state.SetItemsProcessed(state.iterations() * batch_size * size);
HIP_CHECK(hipFree(d_input));
HIP_CHECK(hipFree(d_output));
HIP_CHECK(hipFree(d_temp_storage));
}
template<typename T, typename Op>
struct Benchmark;
template<typename T>
struct Benchmark<T, hipcub::Sum>
{
static void run(benchmark::State& state, size_t size, const hipStream_t stream)
{
hipError_t (*ptr_to_sum)(void*, size_t&, T*, T*, int, hipStream_t)
= &hipcub::DeviceReduce::Sum;
run_benchmark<T, T>(state, size, stream, ptr_to_sum);
}
};
template<typename T>
struct Benchmark<T, hipcub::Min>
{
static void run(benchmark::State& state, size_t size, const hipStream_t stream)
{
hipError_t (*ptr_to_min)(void*, size_t&, T*, T*, int, hipStream_t)
= &hipcub::DeviceReduce::Min;
run_benchmark<T, T>(state, size, stream, ptr_to_min);
}
};
template<typename T>
struct Benchmark<T, hipcub::ArgMin>
{
using Difference = int;
using Iterator = typename hipcub::ArgIndexInputIterator<T*, Difference>;
using KeyValue = typename Iterator::value_type;
static void run(benchmark::State& state, size_t size, const hipStream_t stream)
{
hipError_t (*ptr_to_argmin)(void*, size_t&, T*, KeyValue*, int, hipStream_t)
= &hipcub::DeviceReduce::ArgMin;
run_benchmark<T, KeyValue>(state, size, stream, ptr_to_argmin);
}
};
#define CREATE_BENCHMARK(T, REDUCE_OP) \
benchmark::RegisterBenchmark(std::string("device_reduce" \
"<data_type:" #T ",op:" #REDUCE_OP ">.") \
.c_str(), \
&Benchmark<T, REDUCE_OP>::run, \
size, \
stream)
#define CREATE_BENCHMARKS(REDUCE_OP) \
CREATE_BENCHMARK(int, REDUCE_OP), CREATE_BENCHMARK(long long, REDUCE_OP), \
CREATE_BENCHMARK(float, REDUCE_OP), CREATE_BENCHMARK(double, REDUCE_OP), \
CREATE_BENCHMARK(int8_t, REDUCE_OP)
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_device_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;
using custom_double2 = benchmark_utils::custom_type<double, double>;
// Add benchmarks
std::vector<benchmark::internal::Benchmark*> benchmarks = {
CREATE_BENCHMARKS(hipcub::Sum),
CREATE_BENCHMARK(custom_double2, hipcub::Sum),
CREATE_BENCHMARKS(hipcub::Min),
#ifdef HIPCUB_ROCPRIM_API
CREATE_BENCHMARK(custom_double2, hipcub::Min),
#endif
CREATE_BENCHMARKS(hipcub::ArgMin),
#ifdef HIPCUB_ROCPRIM_API
CREATE_BENCHMARK(custom_double2, hipcub::ArgMin),
#endif
};
// 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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