365 lines
20 KiB
Plaintext
365 lines
20 KiB
Plaintext
/*
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* Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
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*
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* NVIDIA CORPORATION and its licensors retain all intellectual property
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* and proprietary rights in and to this software, related documentation
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* and any modifications thereto. Any use, reproduction, disclosure or
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* distribution of this software and related documentation without an express
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* license agreement from NVIDIA CORPORATION is strictly prohibited.
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*
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* See COPYRIGHT.txt for license information
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*/
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#define CUMODULE_NAME "reduction_latency.cubin"
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#include "utils.h"
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#include "coll_test.h"
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#define LARGEST_DT int64_t
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#if defined __cplusplus || defined NVSHMEM_BITCODE_APPLICATION
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extern "C" {
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#endif
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#define CALL_RDXN(TG_PRE, TG, TYPENAME, TYPE, OP, THREAD_COMP, ELEM_COMP) \
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\
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void call_test_##TYPENAME##_##OP##_reduce_kern##TG##_cubin( \
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int num_blocks, int num_tpb, cudaStream_t stream, void **arglist) { \
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CUfunction test_##TYPENAME##_##OP##_reduce_kern##TG_cubin; \
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\
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init_test_case_kernel(&test_##TYPENAME##_##OP##_reduce_kern##TG_cubin, \
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NVSHMEMI_TEST_STRINGIFY(test_##TYPENAME##_##OP##_reduce_kern##TG)); \
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CU_CHECK(cuLaunchCooperativeKernel(test_##TYPENAME##_##OP##_reduce_kern##TG_cubin, \
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num_blocks, 1, 1, num_tpb, 1, 1, 0, stream, arglist)); \
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} \
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\
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__global__ void test_##TYPENAME##_##OP##_reduce_kern##TG( \
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nvshmem_team_t team, TYPE *dest, const TYPE *source, int nelems, int iter) { \
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int i; \
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\
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if (!blockIdx.x && (threadIdx.x < THREAD_COMP) && (nelems < ELEM_COMP)) { \
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for (i = 0; i < iter; i++) { \
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nvshmem##TG_PRE##_##TYPENAME##_##OP##_reduce##TG(team, dest, source, nelems); \
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} \
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} \
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}
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#define CALL_RDXN_KERNEL(TYPENAME, OP, TG, BLOCKS, THREADS, ARG_LIST, STREAM) \
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if (use_cubin) { \
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call_test_##TYPENAME##_##OP##_reduce_kern##TG##_cubin(BLOCKS, THREADS, STREAM, ARG_LIST); \
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} else { \
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status = \
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nvshmemx_collective_launch((const void *)test_##TYPENAME##_##OP##_reduce_kern##TG, \
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BLOCKS, THREADS, ARG_LIST, 0, STREAM); \
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if (status != NVSHMEMX_SUCCESS) { \
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fprintf(stderr, "shmemx_collective_launch failed %d \n", status); \
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exit(-1); \
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} \
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}
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#define CALL_RDXN_OPS_ALL_TG(TYPENAME, TYPE) \
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CALL_RDXN(x, _block, TYPENAME, TYPE, sum, INT_MAX, INT_MAX) \
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CALL_RDXN(x, _block, TYPENAME, TYPE, prod, INT_MAX, INT_MAX) \
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CALL_RDXN(x, _block, TYPENAME, TYPE, and, INT_MAX, INT_MAX) \
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CALL_RDXN(x, _block, TYPENAME, TYPE, or, INT_MAX, INT_MAX) \
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CALL_RDXN(x, _block, TYPENAME, TYPE, xor, INT_MAX, INT_MAX) \
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CALL_RDXN(x, _block, TYPENAME, TYPE, min, INT_MAX, INT_MAX) \
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CALL_RDXN(x, _block, TYPENAME, TYPE, max, INT_MAX, INT_MAX) \
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CALL_RDXN(x, _warp, TYPENAME, TYPE, sum, warpSize, 4096) \
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CALL_RDXN(x, _warp, TYPENAME, TYPE, prod, warpSize, 4096) \
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CALL_RDXN(x, _warp, TYPENAME, TYPE, and, warpSize, 4096) \
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CALL_RDXN(x, _warp, TYPENAME, TYPE, or, warpSize, 4096) \
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CALL_RDXN(x, _warp, TYPENAME, TYPE, xor, warpSize, 4096) \
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CALL_RDXN(x, _warp, TYPENAME, TYPE, min, warpSize, 4096) \
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CALL_RDXN(x, _warp, TYPENAME, TYPE, max, warpSize, 4096) \
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CALL_RDXN(, , TYPENAME, TYPE, sum, 1, 512) \
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CALL_RDXN(, , TYPENAME, TYPE, prod, 1, 512) \
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CALL_RDXN(, , TYPENAME, TYPE, and, 1, 512) \
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CALL_RDXN(, , TYPENAME, TYPE, or, 1, 512) \
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CALL_RDXN(, , TYPENAME, TYPE, xor, 1, 512) \
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CALL_RDXN(, , TYPENAME, TYPE, min, 1, 512) \
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CALL_RDXN(, , TYPENAME, TYPE, max, 1, 512)
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CALL_RDXN_OPS_ALL_TG(int32, int32_t)
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CALL_RDXN_OPS_ALL_TG(int64, int64_t)
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#if defined __cplusplus || defined NVSHMEM_BITCODE_APPLICATION
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}
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#endif
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#define SET_SIZE_ARR(TYPE, ELEM_COMP) \
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do { \
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j = 0; \
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for (num_elems = min_elems; num_elems <= max_elems; num_elems *= step_factor) { \
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if (num_elems < ELEM_COMP) { \
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size_arr[j] = num_elems * sizeof(TYPE); \
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} else { \
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size_arr[j] = 0; \
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} \
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j++; \
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} \
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} while (0)
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#define RUN_ITERS_OP(TYPENAME, TYPE, GROUP, OP, ELEM_COMP) \
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do { \
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void *skip_arg_list[] = {&team, &dest, &source, &num_elems, &skip}; \
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void *time_arg_list[] = {&team, &dest, &source, &num_elems, &iter}; \
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float milliseconds; \
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cudaEvent_t start, stop; \
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cudaEventCreate(&start); \
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cudaEventCreate(&stop); \
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SET_SIZE_ARR(TYPE, ELEM_COMP); \
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\
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nvshmem_barrier_all(); \
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j = 0; \
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for (num_elems = min_elems; num_elems < ELEM_COMP; num_elems *= 2) { \
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CALL_RDXN_KERNEL(TYPENAME, OP, GROUP, num_blocks, nvshm_test_num_tpb, skip_arg_list, \
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stream); \
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CUDA_CHECK(cudaStreamSynchronize(stream)); \
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nvshmem_barrier_all(); \
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\
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cudaEventRecord(start, stream); \
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CALL_RDXN_KERNEL(TYPENAME, OP, GROUP, num_blocks, nvshm_test_num_tpb, time_arg_list, \
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stream); \
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cudaEventRecord(stop, stream); \
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CUDA_CHECK(cudaStreamSynchronize(stream)); \
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\
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if (!mype) { \
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cudaEventElapsedTime(&milliseconds, start, stop); \
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h_##OP##_lat[j] = (milliseconds * 1000.0) / (float)iter; \
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} \
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nvshmem_barrier_all(); \
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j++; \
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} \
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} while (0)
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#define RUN_ITERS(TYPENAME, TYPE, GROUP, ELEM_COMP) \
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RUN_ITERS_OP(TYPENAME, TYPE, GROUP, sum, ELEM_COMP); \
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RUN_ITERS_OP(TYPENAME, TYPE, GROUP, prod, ELEM_COMP); \
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RUN_ITERS_OP(TYPENAME, TYPE, GROUP, and, ELEM_COMP); \
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RUN_ITERS_OP(TYPENAME, TYPE, GROUP, or, ELEM_COMP); \
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RUN_ITERS_OP(TYPENAME, TYPE, GROUP, xor, ELEM_COMP); \
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RUN_ITERS_OP(TYPENAME, TYPE, GROUP, min, ELEM_COMP); \
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RUN_ITERS_OP(TYPENAME, TYPE, GROUP, max, ELEM_COMP);
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int rdxn_calling_kernel(nvshmem_team_t team, void *dest, const void *source, int mype,
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cudaStream_t stream, run_opt_t run_options, void **h_tables) {
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int status = 0;
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int nvshm_test_num_tpb = threads_per_block;
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int num_blocks = 1;
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size_t num_elems = 1, min_elems, max_elems;
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int iter = iters;
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int skip = warmup_iters;
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int j;
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uint64_t *size_arr = (uint64_t *)h_tables[0];
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double *h_sum_lat = (double *)h_tables[1];
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double *h_prod_lat = (double *)h_tables[2];
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double *h_and_lat = (double *)h_tables[3];
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double *h_or_lat = (double *)h_tables[4];
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double *h_xor_lat = (double *)h_tables[5];
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double *h_min_lat = (double *)h_tables[6];
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double *h_max_lat = (double *)h_tables[7];
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// if (!mype) printf("Transfer size in bytes and latency of thread/warp/block variants of all
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// operations of reduction API in us\n");
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if (run_options.run_thread) {
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min_elems = max(static_cast<size_t>(1), min_size / sizeof(int32_t));
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max_elems = max(static_cast<size_t>(1), max_size / sizeof(int32_t));
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RUN_ITERS(int32, int32_t, , 512);
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if (!mype) {
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print_table_v1("device_reduction", "int32-sum-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_sum_lat, j);
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print_table_v1("device_reduction", "int32-prod-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_prod_lat, j);
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print_table_v1("device_reduction", "int32-and-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_and_lat, j);
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print_table_v1("device_reduction", "int32-or-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_or_lat, j);
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print_table_v1("device_reduction", "int32-xor-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_xor_lat, j);
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print_table_v1("device_reduction", "int32-min-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_min_lat, j);
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print_table_v1("device_reduction", "int32-max-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_max_lat, j);
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}
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min_elems = max(static_cast<size_t>(1), min_size / sizeof(int64_t));
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max_elems = max(static_cast<size_t>(1), max_size / sizeof(int64_t));
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RUN_ITERS(int64, int64_t, , 512);
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if (!mype) {
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print_table_v1("device_reduction", "int64-sum-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_sum_lat, j);
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print_table_v1("device_reduction", "int64-prod-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_prod_lat, j);
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print_table_v1("device_reduction", "int64-and-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_and_lat, j);
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print_table_v1("device_reduction", "int64-or-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_or_lat, j);
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print_table_v1("device_reduction", "int64-xor-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_xor_lat, j);
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print_table_v1("device_reduction", "int64-min-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_min_lat, j);
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print_table_v1("device_reduction", "int64-max-t", "size (Bytes)", "latency", "us", '-',
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size_arr, h_max_lat, j);
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}
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}
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if (run_options.run_warp) {
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min_elems = max(static_cast<size_t>(1), min_size / sizeof(int32_t));
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max_elems = max(static_cast<size_t>(1), max_size / sizeof(int32_t));
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RUN_ITERS(int32, int32_t, _warp, 4096);
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if (!mype) {
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print_table_v1("device_reduction", "int32-sum-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_sum_lat, j);
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print_table_v1("device_reduction", "int32-prod-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_prod_lat, j);
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print_table_v1("device_reduction", "int32-and-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_and_lat, j);
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print_table_v1("device_reduction", "int32-or-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_or_lat, j);
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print_table_v1("device_reduction", "int32-xor-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_xor_lat, j);
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print_table_v1("device_reduction", "int32-min-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_min_lat, j);
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print_table_v1("device_reduction", "int32-max-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_max_lat, j);
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}
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min_elems = max(static_cast<size_t>(1), min_size / sizeof(int64_t));
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max_elems = max(static_cast<size_t>(1), max_size / sizeof(int64_t));
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RUN_ITERS(int64, int64_t, _warp, 4096);
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if (!mype) {
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print_table_v1("device_reduction", "int64-sum-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_sum_lat, j);
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print_table_v1("device_reduction", "int64-prod-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_prod_lat, j);
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print_table_v1("device_reduction", "int64-and-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_and_lat, j);
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print_table_v1("device_reduction", "int64-or-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_or_lat, j);
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print_table_v1("device_reduction", "int64-xor-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_xor_lat, j);
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print_table_v1("device_reduction", "int64-min-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_min_lat, j);
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print_table_v1("device_reduction", "int64-max-w", "size (Bytes)", "latency", "us", '-',
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size_arr, h_max_lat, j);
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}
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}
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if (run_options.run_block) {
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min_elems = max(static_cast<size_t>(1), min_size / sizeof(int32_t));
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max_elems = max(static_cast<size_t>(1), max_size / sizeof(int32_t));
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RUN_ITERS(int32, int32_t, _block, max_elems);
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if (!mype) {
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print_table_v1("device_reduction", "int32-sum-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_sum_lat, j);
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print_table_v1("device_reduction", "int32-prod-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_prod_lat, j);
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print_table_v1("device_reduction", "int32-and-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_and_lat, j);
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print_table_v1("device_reduction", "int32-or-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_or_lat, j);
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print_table_v1("device_reduction", "int32-xor-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_xor_lat, j);
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print_table_v1("device_reduction", "int32-min-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_min_lat, j);
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print_table_v1("device_reduction", "int32-max-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_max_lat, j);
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}
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min_elems = max(static_cast<size_t>(1), min_size / sizeof(int64_t));
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max_elems = max(static_cast<size_t>(1), max_size / sizeof(int64_t));
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RUN_ITERS(int64, int64_t, _block, max_elems);
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if (!mype) {
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print_table_v1("device_reduction", "int64-sum-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_sum_lat, j);
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print_table_v1("device_reduction", "int64-prod-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_prod_lat, j);
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print_table_v1("device_reduction", "int64-and-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_and_lat, j);
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print_table_v1("device_reduction", "int64-or-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_or_lat, j);
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print_table_v1("device_reduction", "int64-xor-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_xor_lat, j);
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print_table_v1("device_reduction", "int64-min-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_min_lat, j);
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print_table_v1("device_reduction", "int64-max-b", "size (Bytes)", "latency", "us", '-',
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size_arr, h_max_lat, j);
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}
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}
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return status;
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}
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int main(int argc, char **argv) {
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int status = 0;
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int mype, array_size;
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size_t size = 0;
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read_args(argc, argv);
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int *h_buffer = NULL;
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int *d_source, *d_dest;
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int *h_source, *h_dest;
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char size_string[100];
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cudaStream_t cstrm;
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run_opt_t run_options;
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void **h_tables;
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run_options.run_thread = run_options.run_warp = run_options.run_block = 1;
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size = page_size_roundoff(max_size); // send buf
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size += page_size_roundoff(max_size); // recv buf
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DEBUG_PRINT("symmetric size requested %lu\n", size);
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sprintf(size_string, "%lu", size);
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status = setenv("NVSHMEM_SYMMETRIC_SIZE", size_string, 1);
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if (status) {
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fprintf(stderr, "setenv failed \n");
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status = -1;
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goto out;
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}
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array_size = max_size_log;
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init_wrapper(&argc, &argv);
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alloc_tables(&h_tables, 8, array_size);
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if (use_cubin) {
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init_cumodule(CUMODULE_NAME);
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}
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mype = nvshmem_my_pe();
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CUDA_CHECK(cudaStreamCreateWithFlags(&cstrm, cudaStreamNonBlocking));
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CUDA_CHECK(cudaHostAlloc(&h_buffer, max_size * 2, cudaHostAllocDefault));
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h_source = (int32_t *)h_buffer;
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h_dest = (int32_t *)&h_source[max_size / sizeof(int32_t)];
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d_source = (int32_t *)nvshmem_align(getpagesize(), max_size);
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d_dest = (int32_t *)nvshmem_align(getpagesize(), max_size);
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CUDA_CHECK(cudaMemcpyAsync(d_source, h_source, max_size, cudaMemcpyHostToDevice, cstrm));
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CUDA_CHECK(cudaMemcpyAsync(d_dest, h_dest, max_size, cudaMemcpyHostToDevice, cstrm));
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rdxn_calling_kernel(NVSHMEM_TEAM_WORLD, d_dest, d_source, mype, cstrm, run_options, h_tables);
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DEBUG_PRINT("last error = %s\n", cudaGetErrorString(cudaGetLastError()));
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CUDA_CHECK(cudaMemcpyAsync(h_source, d_source, max_size, cudaMemcpyDeviceToHost, cstrm));
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CUDA_CHECK(cudaMemcpyAsync(h_dest, d_dest, max_size, cudaMemcpyDeviceToHost, cstrm));
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nvshmem_barrier_all();
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CUDA_CHECK(cudaFreeHost(h_buffer));
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nvshmem_free(d_source);
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nvshmem_free(d_dest);
|
|
|
|
CUDA_CHECK(cudaStreamDestroy(cstrm));
|
|
|
|
finalize_wrapper();
|
|
|
|
out:
|
|
return 0;
|
|
}
|