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// 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 "hipcub/block/block_reduce.hpp"
#include "hipcub/thread/thread_operators.hpp"
#ifndef DEFAULT_N
const size_t DEFAULT_N = 1024 * 1024 * 32;
#endif
template<class Runner,
class T,
unsigned int BlockSize,
unsigned int ItemsPerThread,
unsigned int Trials>
__global__ __launch_bounds__(BlockSize) void kernel(const T* input, T* output)
{
Runner::template run<T, BlockSize, ItemsPerThread, Trials>(input, output);
}
template<hipcub::BlockReduceAlgorithm algorithm>
struct reduce
{
template<class T, unsigned int BlockSize, unsigned int ItemsPerThread, unsigned int Trials>
__device__ static void run(const T* input, T* output)
{
const unsigned int i = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x;
T values[ItemsPerThread];
T reduced_value;
for(unsigned int k = 0; k < ItemsPerThread; k++)
{
values[k] = input[i * ItemsPerThread + k];
}
using breduce_t = hipcub::BlockReduce<T, BlockSize, algorithm>;
__shared__ typename breduce_t::TempStorage storage;
#pragma nounroll
for(unsigned int trial = 0; trial < Trials; trial++)
{
reduced_value = breduce_t(storage).Reduce(values, hipcub::Sum());
values[0] = reduced_value;
}
if(hipThreadIdx_x == 0)
{
output[hipBlockIdx_x] = reduced_value;
}
}
};
template<class Benchmark,
class T,
unsigned int BlockSize,
unsigned int ItemsPerThread,
unsigned int Trials = 100>
void run_benchmark(benchmark::State& state, hipStream_t stream, size_t N)
{
// Make sure size is a multiple of BlockSize
constexpr auto items_per_block = BlockSize * ItemsPerThread;
const auto size = items_per_block * ((N + items_per_block - 1) / items_per_block);
// Allocate and fill memory
std::vector<T> input(size, T(1));
T* d_input;
T* d_output;
HIP_CHECK(hipMalloc(&d_input, size * sizeof(T)));
HIP_CHECK(hipMalloc(&d_output, size * sizeof(T)));
HIP_CHECK(hipMemcpy(d_input, input.data(), size * sizeof(T), hipMemcpyHostToDevice));
HIP_CHECK(hipDeviceSynchronize());
for(auto _ : state)
{
auto start = std::chrono::high_resolution_clock::now();
hipLaunchKernelGGL(HIP_KERNEL_NAME(kernel<Benchmark, T, BlockSize, ItemsPerThread, Trials>),
dim3(size / items_per_block),
dim3(BlockSize),
0,
stream,
d_input,
d_output);
HIP_CHECK(hipPeekAtLastError());
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() * size * sizeof(T) * Trials);
state.SetItemsProcessed(state.iterations() * size * Trials);
HIP_CHECK(hipFree(d_input));
HIP_CHECK(hipFree(d_output));
}
// IPT - items per thread
#define CREATE_BENCHMARK(T, BS, IPT) \
benchmark::RegisterBenchmark(std::string("block_reduce<data_type:" #T ",block_size:" #BS \
",items_per_thread:" #IPT ",sub_algorithm_name:" \
+ algorithm_name + ">.method_name:" + method_name) \
.c_str(), \
&run_benchmark<Benchmark, T, BS, IPT>, \
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, 8), CREATE_BENCHMARK(type, block, 11), \
CREATE_BENCHMARK(type, block, 16)
template<class Benchmark>
void add_benchmarks(std::vector<benchmark::internal::Benchmark*>& benchmarks,
const std::string& method_name,
const std::string& algorithm_name,
hipStream_t stream,
size_t size)
{
std::vector<benchmark::internal::Benchmark*> new_benchmarks = {
// When block size is less than or equal to warp size
BENCHMARK_TYPE(int, 64),
BENCHMARK_TYPE(float, 64),
BENCHMARK_TYPE(double, 64),
BENCHMARK_TYPE(int8_t, 64),
BENCHMARK_TYPE(uint8_t, 64),
BENCHMARK_TYPE(int, 256),
BENCHMARK_TYPE(float, 256),
BENCHMARK_TYPE(double, 256),
BENCHMARK_TYPE(int8_t, 256),
BENCHMARK_TYPE(uint8_t, 256),
};
benchmarks.insert(benchmarks.end(), new_benchmarks.begin(), new_benchmarks.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_block_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;
// using_warp_scan
using reduce_uwr_t = reduce<hipcub::BlockReduceAlgorithm::BLOCK_REDUCE_WARP_REDUCTIONS>;
add_benchmarks<reduce_uwr_t>(benchmarks,
"reduce",
"BLOCK_REDUCE_WARP_REDUCTIONS",
stream,
size);
// raking reduce
using reduce_rr_t = reduce<hipcub::BlockReduceAlgorithm::BLOCK_REDUCE_RAKING>;
add_benchmarks<reduce_rr_t>(benchmarks, "reduce", "BLOCK_REDUCE_RAKING", stream, size);
// raking reduce commutative only
using reduce_rrco_t
= reduce<hipcub::BlockReduceAlgorithm::BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY>;
add_benchmarks<reduce_rrco_t>(benchmarks,
"reduce",
"BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY",
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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