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seg_wmma_16n_block.cu
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#include <benchmark/benchmark.h>
#include "init/init.hpp"
#include "reduction/args.hpp"
#include "utils/utils.hpp"
#include "kernel.cuh"
using namespace wmma_reduction;
template <size_t SEGMENT_SIZE, int WARPS_PER_BLOCK>
static void tryCUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK(benchmark::State &state) {
const size_t num_segments = state.range(0);
const size_t segment_size = state.range(1);
if (segment_size != SEGMENT_SIZE) {
state.SkipWithError(fmt::format("segment_size={} must be equal to SEGMENT_SIZE={} ",
segment_size, SEGMENT_SIZE)
.c_str());
return;
}
const int BLOCK_DIM = WARPS_PER_BLOCK * WARP_SIZE;
const size_t num_elements = num_segments * segment_size;
defer(cudaDeviceReset());
half *d_in_fp16 = nullptr;
half *d_out = nullptr;
cudaEvent_t start, stop;
try {
PRINT_IF_ERROR(cudaMalloc(&d_in_fp16, num_elements * sizeof(half)));
PRINT_IF_ERROR(cudaMalloc(&d_out, num_segments * sizeof(half)));
cuda_memory_set(d_in_fp16, 0.001f, num_elements);
dim3 gridDim, blockDim;
blockDim.x = BLOCK_DIM;
gridDim.x = num_segments / 16;
if (gridDim.x >= CUDA_MAX_GRID_SIZE) {
state.SkipWithError(
fmt::format("gridDim.x={} is greater than CUDA_MAX_GRID_SIZE", gridDim.x)
.c_str());
return;
}
if (gridDim.x == 0) {
state.SkipWithError(fmt::format("gridDim.x={} is 0", gridDim.x).c_str());
return;
}
PRINT_IF_ERROR(cudaEventCreate(&start));
PRINT_IF_ERROR(cudaEventCreate(&stop));
defer(cudaEventDestroy(start));
defer(cudaEventDestroy(stop));
for (auto _ : state) {
PRINT_IF_ERROR(cudaEventRecord(start));
compute_wmma_segmented_reduction_16n_block<SEGMENT_SIZE, WARPS_PER_BLOCK, BLOCK_DIM>
<<<gridDim, blockDim>>>(d_in_fp16, d_out, num_segments);
PRINT_IF_ERROR(cudaEventRecord(stop));
PRINT_IF_ERROR(cudaEventSynchronize(stop));
/* state.SkipWithError("break"); */
state.PauseTiming();
float msecTotal = 0.0f;
PRINT_IF_ERROR(cudaEventElapsedTime(&msecTotal, start, stop));
state.SetIterationTime(msecTotal / 1000);
state.ResumeTiming();
}
state.counters.insert({{"num_segments", num_segments},
{"segment_size", segment_size},
{"num_elements", num_segments * segment_size},
{"warps_per_block", WARPS_PER_BLOCK},
{"flops",
{state.iterations() * 1.0 * num_segments * segment_size,
benchmark::Counter::kAvgThreadsRate}}});
#if 0
half *h_out = new half[num_segments];
PRINT_IF_ERROR(cudaMemcpy(h_out, d_out, num_segments * sizeof(half),
cudaMemcpyDeviceToHost));
int errors = 0;
for (int j = 0; j < num_segments; j++) {
float correct_segment_sum = 0;
for (int i = 0; i < segment_size; i++) {
correct_segment_sum += h_in[j * segment_size + i];
}
if (fabs(half_to_float(h_out[j]) - correct_segment_sum) > 0.1) {
errors++;
printf("Expected %f, get h_out[%d] = %f\n", correct_segment_sum, j,
half_to_float(h_out[j]));
}
}
if (errors > 0) {
printf(
"CUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK does not agree with SEQUENTIAL! %d errors!\n",
errors);
} else {
printf("Results verified: they agree.\n\n");
}
delete h_out;
#endif
cudaFree(d_in_fp16);
cudaFree(d_out);
} catch (...) {
cudaFree(d_in_fp16);
cudaFree(d_out);
cudaDeviceReset();
const auto p = std::current_exception();
std::rethrow_exception(p);
}
}
template <size_t SEGMENT_SIZE, int WARPS_PER_BLOCK>
static void iCUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK(benchmark::State &state) {
cudaDeviceReset();
try {
tryCUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK<SEGMENT_SIZE, WARPS_PER_BLOCK>(state);
} catch (const std::exception &e) {
state.SkipWithError(e.what());
} catch (const std::string &e) {
state.SkipWithError(e.c_str());
} catch (...) {
state.SkipWithError("unknown exception");
}
}
template <int WARPS_PER_BLOCK>
static void CUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK(benchmark::State &state) {
const int segment_size = state.range(1);
switch (segment_size) {
#define Dispatch(N) \
case N: \
iCUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK<N, WARPS_PER_BLOCK>(state); \
break
Dispatch(16);
Dispatch(32);
Dispatch(64);
Dispatch(128);
Dispatch(256);
Dispatch(512);
Dispatch(1024);
Dispatch(2048);
Dispatch(4096);
Dispatch(8192);
Dispatch(16384);
Dispatch(32768);
Dispatch(65536);
Dispatch(131072);
Dispatch(262144);
Dispatch(524288);
Dispatch(1048576);
Dispatch(2097152);
Dispatch(4194304);
Dispatch(8388608);
Dispatch(16777216);
Dispatch(33554432);
Dispatch(67108864);
Dispatch(134217728);
Dispatch(268435456);
Dispatch(536870912);
Dispatch(1073741824);
default:
static_assert(true, "invalid segment size");
state.SkipWithError("invalid segment size");
#undef DISPATCH
}
}
template <int WARPS_PER_BLOCK>
static void CUDA_WMMA_TUNE_SEGMENTED_REDUCTION_16N_BLOCK(benchmark::State &state) {
CUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK<WARPS_PER_BLOCK>(state);
}
#define RUN_CUDA_WMMA_TUNE(TUNE_ARGS) \
BENCHMARK_TEMPLATE(CUDA_WMMA_TUNE_SEGMENTED_REDUCTION_16N_BLOCK, 1) \
->Apply(TUNE_ARGS) \
->UseManualTime(); \
BENCHMARK_TEMPLATE(CUDA_WMMA_TUNE_SEGMENTED_REDUCTION_16N_BLOCK, 2) \
->Apply(TUNE_ARGS) \
->UseManualTime(); \
BENCHMARK_TEMPLATE(CUDA_WMMA_TUNE_SEGMENTED_REDUCTION_16N_BLOCK, 4) \
->Apply(TUNE_ARGS) \
->UseManualTime(); \
BENCHMARK_TEMPLATE(CUDA_WMMA_TUNE_SEGMENTED_REDUCTION_16N_BLOCK, 8) \
->Apply(TUNE_ARGS) \
->UseManualTime(); \
BENCHMARK_TEMPLATE(CUDA_WMMA_TUNE_SEGMENTED_REDUCTION_16N_BLOCK, 16) \
->Apply(TUNE_ARGS) \
->UseManualTime();
// RUN_CUDA_WMMA_TUNE(Tuning16_x_14);
// RUN_CUDA_WMMA_TUNE(Tuning16_x_18);
// RUN_CUDA_WMMA_TUNE(Tuning16_x_22);
// RUN_CUDA_WMMA_TUNE(Tuning17_x_26);
RUN_CUDA_WMMA_TUNE(Tuning16_x_30);
#define RUN_CUDA_WMMA(Args) \
BENCHMARK_TEMPLATE(CUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK, 1) \
->Args() \
->UseManualTime(); \
BENCHMARK_TEMPLATE(CUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK, 2) \
->Args() \
->UseManualTime(); \
BENCHMARK_TEMPLATE(CUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK, 4) \
->Args() \
->UseManualTime(); \
BENCHMARK_TEMPLATE(CUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK, 8) \
->Args() \
->UseManualTime(); \
BENCHMARK_TEMPLATE(CUDA_WMMA_SEGMENTED_REDUCTION_16N_BLOCK, 16) \
->Args() \
->UseManualTime();
RUN_CUDA_WMMA(SEG_16_ARGS);
RUN_CUDA_WMMA(SEG_32_ARGS);
RUN_CUDA_WMMA(SEG_64_ARGS);
RUN_CUDA_WMMA(SEG_128_ARGS);
RUN_CUDA_WMMA(SEG_256_ARGS);
RUN_CUDA_WMMA(SEG_512_ARGS);
RUN_CUDA_WMMA(SEG_1024_ARGS);