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446 lines
16 KiB
Plaintext
446 lines
16 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/*
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* This sample illustrates the usage of CUDA streams for overlapping
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* kernel execution with device/host memcopies. The kernel is used to
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* initialize an array to a specific value, after which the array is
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* copied to the host (CPU) memory. To increase performance, multiple
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* kernel/memcopy pairs are launched asynchronously, each pair in its
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* own stream. Devices with Compute Capability 1.1 can overlap a kernel
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* and a memcopy as long as they are issued in different streams. Kernels
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* are serialized. Thus, if n pairs are launched, streamed approach
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* can reduce the memcopy cost to the (1/n)th of a single copy of the entire
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* data set.
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*
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* Additionally, this sample uses CUDA events to measure elapsed time for
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* CUDA calls. Events are a part of CUDA API and provide a system independent
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* way to measure execution times on CUDA devices with approximately 0.5
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* microsecond precision.
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*
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* Elapsed times are averaged over nreps repetitions (10 by default).
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*
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*/
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const char *sSDKsample = "simpleStreams";
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const char *sEventSyncMethod[] = {"cudaEventDefault", "cudaEventBlockingSync",
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"cudaEventDisableTiming", NULL};
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const char *sDeviceSyncMethod[] = {
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"cudaDeviceScheduleAuto", "cudaDeviceScheduleSpin",
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"cudaDeviceScheduleYield", "INVALID",
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"cudaDeviceScheduleBlockingSync", NULL};
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// System includes
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#include <stdio.h>
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#include <assert.h>
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// CUDA runtime
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#include <cuda_runtime.h>
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// helper functions and utilities to work with CUDA
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#include <helper_functions.h>
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#include <helper_cuda.h>
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#ifndef WIN32
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#include <sys/mman.h> // for mmap() / munmap()
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#endif
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// Macro to aligned up to the memory size in question
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#define MEMORY_ALIGNMENT 4096
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#define ALIGN_UP(x, size) (((size_t)x + (size - 1)) & (~(size - 1)))
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__global__ void init_array(int *g_data, int *factor, int num_iterations) {
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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for (int i = 0; i < num_iterations; i++) {
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g_data[idx] += *factor; // non-coalesced on purpose, to burn time
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}
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}
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bool correct_data(int *a, const int n, const int c) {
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for (int i = 0; i < n; i++) {
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if (a[i] != c) {
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printf("%d: %d %d\n", i, a[i], c);
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return false;
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}
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}
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return true;
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}
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inline void AllocateHostMemory(bool bPinGenericMemory, int **pp_a,
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int **ppAligned_a, int nbytes) {
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#if CUDART_VERSION >= 4000
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#if !defined(__arm__) && !defined(__aarch64__)
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if (bPinGenericMemory) {
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// allocate a generic page-aligned chunk of system memory
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#ifdef WIN32
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printf(
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"> VirtualAlloc() allocating %4.2f Mbytes of (generic page-aligned "
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"system memory)\n",
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(float)nbytes / 1048576.0f);
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*pp_a = (int *)VirtualAlloc(NULL, (nbytes + MEMORY_ALIGNMENT),
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MEM_RESERVE | MEM_COMMIT, PAGE_READWRITE);
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#else
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printf(
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"> mmap() allocating %4.2f Mbytes (generic page-aligned system "
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"memory)\n",
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(float)nbytes / 1048576.0f);
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*pp_a = (int *)mmap(NULL, (nbytes + MEMORY_ALIGNMENT),
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PROT_READ | PROT_WRITE, MAP_PRIVATE | MAP_ANON, -1, 0);
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#endif
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*ppAligned_a = (int *)ALIGN_UP(*pp_a, MEMORY_ALIGNMENT);
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printf(
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"> cudaHostRegister() registering %4.2f Mbytes of generic allocated "
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"system memory\n",
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(float)nbytes / 1048576.0f);
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// pin allocate memory
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checkCudaErrors(
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cudaHostRegister(*ppAligned_a, nbytes, cudaHostRegisterMapped));
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} else
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#endif
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#endif
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{
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printf("> cudaMallocHost() allocating %4.2f Mbytes of system memory\n",
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(float)nbytes / 1048576.0f);
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// allocate host memory (pinned is required for achieve asynchronicity)
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checkCudaErrors(cudaMallocHost((void **)pp_a, nbytes));
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*ppAligned_a = *pp_a;
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}
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}
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inline void FreeHostMemory(bool bPinGenericMemory, int **pp_a,
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int **ppAligned_a, int nbytes) {
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#if CUDART_VERSION >= 4000
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#if !defined(__arm__) && !defined(__aarch64__)
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// CUDA 4.0 support pinning of generic host memory
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if (bPinGenericMemory) {
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// unpin and delete host memory
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checkCudaErrors(cudaHostUnregister(*ppAligned_a));
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#ifdef WIN32
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VirtualFree(*pp_a, 0, MEM_RELEASE);
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#else
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munmap(*pp_a, nbytes);
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#endif
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} else
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#endif
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#endif
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{
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cudaFreeHost(*pp_a);
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}
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}
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static const char *sSyncMethod[] = {
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"0 (Automatic Blocking)",
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"1 (Spin Blocking)",
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"2 (Yield Blocking)",
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"3 (Undefined Blocking Method)",
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"4 (Blocking Sync Event) = low CPU utilization",
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NULL};
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void printHelp() {
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printf("Usage: %s [options below]\n", sSDKsample);
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printf("\t--sync_method=n for CPU/GPU synchronization\n");
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printf("\t n=%s\n", sSyncMethod[0]);
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printf("\t n=%s\n", sSyncMethod[1]);
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printf("\t n=%s\n", sSyncMethod[2]);
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printf("\t <Default> n=%s\n", sSyncMethod[4]);
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printf(
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"\t--use_generic_memory (default) use generic page-aligned for system "
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"memory\n");
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printf(
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"\t--use_cuda_malloc_host (optional) use cudaMallocHost to allocate "
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"system memory\n");
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}
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#if defined(__APPLE__) || defined(MACOSX)
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#define DEFAULT_PINNED_GENERIC_MEMORY false
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#else
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#define DEFAULT_PINNED_GENERIC_MEMORY true
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#endif
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int main(int argc, char **argv) {
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int cuda_device = 0;
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int nstreams = 4; // number of streams for CUDA calls
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int nreps = 10; // number of times each experiment is repeated
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int n = 16 * 1024 * 1024; // number of ints in the data set
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int nbytes = n * sizeof(int); // number of data bytes
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dim3 threads, blocks; // kernel launch configuration
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float elapsed_time, time_memcpy, time_kernel; // timing variables
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float scale_factor = 1.0f;
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// allocate generic memory and pin it laster instead of using cudaHostAlloc()
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bool bPinGenericMemory =
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DEFAULT_PINNED_GENERIC_MEMORY; // we want this to be the default behavior
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int device_sync_method =
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cudaDeviceBlockingSync; // by default we use BlockingSync
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int niterations; // number of iterations for the loop inside the kernel
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printf("[ %s ]\n\n", sSDKsample);
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if (checkCmdLineFlag(argc, (const char **)argv, "help")) {
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printHelp();
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return EXIT_SUCCESS;
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}
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if ((device_sync_method = getCmdLineArgumentInt(argc, (const char **)argv,
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"sync_method")) >= 0) {
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if (device_sync_method == 0 || device_sync_method == 1 ||
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device_sync_method == 2 || device_sync_method == 4) {
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printf("Device synchronization method set to = %s\n",
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sSyncMethod[device_sync_method]);
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printf("Setting reps to 100 to demonstrate steady state\n");
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nreps = 100;
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} else {
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printf("Invalid command line option sync_method=\"%d\"\n",
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device_sync_method);
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return EXIT_FAILURE;
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}
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} else {
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printHelp();
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return EXIT_SUCCESS;
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "use_generic_memory")) {
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#if defined(__APPLE__) || defined(MACOSX)
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bPinGenericMemory = false; // Generic Pinning of System Paged memory not
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// currently supported on Mac OSX
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#else
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bPinGenericMemory = true;
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#endif
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}
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if (checkCmdLineFlag(argc, (const char **)argv, "use_cuda_malloc_host")) {
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bPinGenericMemory = false;
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}
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printf("\n> ");
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cuda_device = findCudaDevice(argc, (const char **)argv);
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// check the compute capability of the device
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int num_devices = 0;
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checkCudaErrors(cudaGetDeviceCount(&num_devices));
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if (0 == num_devices) {
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printf(
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"your system does not have a CUDA capable device, waiving test...\n");
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return EXIT_WAIVED;
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}
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// check if the command-line chosen device ID is within range, exit if not
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if (cuda_device >= num_devices) {
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printf(
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"cuda_device=%d is invalid, must choose device ID between 0 and %d\n",
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cuda_device, num_devices - 1);
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return EXIT_FAILURE;
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}
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checkCudaErrors(cudaSetDevice(cuda_device));
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// Checking for compute capabilities
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cudaDeviceProp deviceProp;
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, cuda_device));
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niterations = 5;
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// Check if GPU can map host memory (Generic Method), if not then we override
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// bPinGenericMemory to be false
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if (bPinGenericMemory) {
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printf("Device: <%s> canMapHostMemory: %s\n", deviceProp.name,
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deviceProp.canMapHostMemory ? "Yes" : "No");
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if (deviceProp.canMapHostMemory == 0) {
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printf(
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"Using cudaMallocHost, CUDA device does not support mapping of "
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"generic host memory\n");
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bPinGenericMemory = false;
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}
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}
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// Anything that is less than 32 Cores will have scaled down workload
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scale_factor =
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max((32.0f / (_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) *
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(float)deviceProp.multiProcessorCount)),
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1.0f);
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n = (int)rint((float)n / scale_factor);
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printf("> CUDA Capable: SM %d.%d hardware\n", deviceProp.major,
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deviceProp.minor);
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printf("> %d Multiprocessor(s) x %d (Cores/Multiprocessor) = %d (Cores)\n",
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deviceProp.multiProcessorCount,
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_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor),
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_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) *
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deviceProp.multiProcessorCount);
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printf("> scale_factor = %1.4f\n", 1.0f / scale_factor);
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printf("> array_size = %d\n\n", n);
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// enable use of blocking sync, to reduce CPU usage
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printf("> Using CPU/GPU Device Synchronization method (%s)\n",
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sDeviceSyncMethod[device_sync_method]);
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checkCudaErrors(cudaSetDeviceFlags(
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device_sync_method | (bPinGenericMemory ? cudaDeviceMapHost : 0)));
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// allocate host memory
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int c = 5; // value to which the array will be initialized
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int *h_a = 0; // pointer to the array data in host memory
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int *hAligned_a = 0; // pointer to the array data in host memory (aligned to
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// MEMORY_ALIGNMENT)
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// Allocate Host memory (could be using cudaMallocHost or VirtualAlloc/mmap if
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// using the new CUDA 4.0 features
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AllocateHostMemory(bPinGenericMemory, &h_a, &hAligned_a, nbytes);
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// allocate device memory
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int *d_a = 0,
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*d_c = 0; // pointers to data and init value in the device memory
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checkCudaErrors(cudaMalloc((void **)&d_a, nbytes));
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checkCudaErrors(cudaMemset(d_a, 0x0, nbytes));
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checkCudaErrors(cudaMalloc((void **)&d_c, sizeof(int)));
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checkCudaErrors(cudaMemcpy(d_c, &c, sizeof(int), cudaMemcpyHostToDevice));
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printf("\nStarting Test\n");
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// allocate and initialize an array of stream handles
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cudaStream_t *streams =
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(cudaStream_t *)malloc(nstreams * sizeof(cudaStream_t));
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for (int i = 0; i < nstreams; i++) {
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checkCudaErrors(cudaStreamCreate(&(streams[i])));
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}
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// create CUDA event handles
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// use blocking sync
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cudaEvent_t start_event, stop_event;
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int eventflags =
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((device_sync_method == cudaDeviceBlockingSync) ? cudaEventBlockingSync
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: cudaEventDefault);
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checkCudaErrors(cudaEventCreateWithFlags(&start_event, eventflags));
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checkCudaErrors(cudaEventCreateWithFlags(&stop_event, eventflags));
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// time memcopy from device
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checkCudaErrors(cudaEventRecord(start_event, 0)); // record in stream-0, to
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// ensure that all previous
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// CUDA calls have
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// completed
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checkCudaErrors(cudaMemcpyAsync(hAligned_a, d_a, nbytes,
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cudaMemcpyDeviceToHost, streams[0]));
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checkCudaErrors(cudaEventRecord(stop_event, 0));
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checkCudaErrors(cudaEventSynchronize(
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stop_event)); // block until the event is actually recorded
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checkCudaErrors(cudaEventElapsedTime(&time_memcpy, start_event, stop_event));
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printf("memcopy:\t%.2f\n", time_memcpy);
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// time kernel
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threads = dim3(512, 1);
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blocks = dim3(n / threads.x, 1);
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checkCudaErrors(cudaEventRecord(start_event, 0));
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init_array<<<blocks, threads, 0, streams[0]>>>(d_a, d_c, niterations);
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checkCudaErrors(cudaEventRecord(stop_event, 0));
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checkCudaErrors(cudaEventSynchronize(stop_event));
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checkCudaErrors(cudaEventElapsedTime(&time_kernel, start_event, stop_event));
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printf("kernel:\t\t%.2f\n", time_kernel);
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//////////////////////////////////////////////////////////////////////
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// time non-streamed execution for reference
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threads = dim3(512, 1);
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blocks = dim3(n / threads.x, 1);
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checkCudaErrors(cudaEventRecord(start_event, 0));
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for (int k = 0; k < nreps; k++) {
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init_array<<<blocks, threads>>>(d_a, d_c, niterations);
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checkCudaErrors(
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cudaMemcpy(hAligned_a, d_a, nbytes, cudaMemcpyDeviceToHost));
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}
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checkCudaErrors(cudaEventRecord(stop_event, 0));
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checkCudaErrors(cudaEventSynchronize(stop_event));
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checkCudaErrors(cudaEventElapsedTime(&elapsed_time, start_event, stop_event));
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printf("non-streamed:\t%.2f\n", elapsed_time / nreps);
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//////////////////////////////////////////////////////////////////////
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// time execution with nstreams streams
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threads = dim3(512, 1);
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blocks = dim3(n / (nstreams * threads.x), 1);
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memset(hAligned_a, 255,
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nbytes); // set host memory bits to all 1s, for testing correctness
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checkCudaErrors(cudaMemset(
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d_a, 0, nbytes)); // set device memory to all 0s, for testing correctness
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checkCudaErrors(cudaEventRecord(start_event, 0));
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for (int k = 0; k < nreps; k++) {
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// asynchronously launch nstreams kernels, each operating on its own portion
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// of data
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for (int i = 0; i < nstreams; i++) {
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init_array<<<blocks, threads, 0, streams[i]>>>(d_a + i * n / nstreams,
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d_c, niterations);
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}
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// asynchronously launch nstreams memcopies. Note that memcopy in stream x
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// will only
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// commence executing when all previous CUDA calls in stream x have
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// completed
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for (int i = 0; i < nstreams; i++) {
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checkCudaErrors(cudaMemcpyAsync(hAligned_a + i * n / nstreams,
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d_a + i * n / nstreams, nbytes / nstreams,
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cudaMemcpyDeviceToHost, streams[i]));
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}
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}
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checkCudaErrors(cudaEventRecord(stop_event, 0));
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checkCudaErrors(cudaEventSynchronize(stop_event));
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checkCudaErrors(cudaEventElapsedTime(&elapsed_time, start_event, stop_event));
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printf("%d streams:\t%.2f\n", nstreams, elapsed_time / nreps);
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// check whether the output is correct
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printf("-------------------------------\n");
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bool bResults = correct_data(hAligned_a, n, c * nreps * niterations);
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// release resources
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for (int i = 0; i < nstreams; i++) {
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checkCudaErrors(cudaStreamDestroy(streams[i]));
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}
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checkCudaErrors(cudaEventDestroy(start_event));
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checkCudaErrors(cudaEventDestroy(stop_event));
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// Free cudaMallocHost or Generic Host allocated memory (from CUDA 4.0)
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FreeHostMemory(bPinGenericMemory, &h_a, &hAligned_a, nbytes);
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checkCudaErrors(cudaFree(d_a));
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checkCudaErrors(cudaFree(d_c));
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return bResults ? EXIT_SUCCESS : EXIT_FAILURE;
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}
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