mirror of
https://github.com/NVIDIA/cuda-samples.git
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408 lines
14 KiB
Plaintext
408 lines
14 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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// System includes
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#include <assert.h>
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#include <stdio.h>
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// helper functions and utilities to work with CUDA
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#include <helper_cuda.h>
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#include <helper_functions.h>
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#define NUM_GRAPHS 8
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#define THREADS_PER_BLOCK 512
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void printMemoryFootprint(int device) {
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size_t footprint;
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checkCudaErrors(cudaDeviceGetGraphMemAttribute(
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device, (cudaGraphMemAttributeType)0, &footprint));
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printf(" FOOTPRINT: %lu bytes\n", footprint);
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}
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void prepareAllocParams(cudaMemAllocNodeParams *allocParams, size_t bytes,
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int device) {
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memset(allocParams, 0, sizeof(*allocParams));
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allocParams->bytesize = bytes;
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allocParams->poolProps.allocType = cudaMemAllocationTypePinned;
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allocParams->poolProps.location.id = device;
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allocParams->poolProps.location.type = cudaMemLocationTypeDevice;
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}
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void createVirtAddrReuseGraph(cudaGraphExec_t *graphExec, size_t bytes,
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int device) {
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cudaGraph_t graph;
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cudaGraphNode_t allocNodeA, allocNodeB, freeNodeA, freeNodeB;
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cudaMemAllocNodeParams allocParams;
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float *d_a, *d_b;
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checkCudaErrors(cudaGraphCreate(&graph, 0));
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prepareAllocParams(&allocParams, bytes, device);
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checkCudaErrors(
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cudaGraphAddMemAllocNode(&allocNodeA, graph, NULL, 0, &allocParams));
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d_a = (float *)allocParams.dptr;
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checkCudaErrors(
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cudaGraphAddMemFreeNode(&freeNodeA, graph, &allocNodeA, 1, (void *)d_a));
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// The dependency between the allocation of d_b and the free of d_a allows d_b
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// to reuse the same VA.
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checkCudaErrors(cudaGraphAddMemAllocNode(&allocNodeB, graph, &freeNodeA, 1,
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&allocParams));
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d_b = (float *)allocParams.dptr;
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if (d_a == d_b) {
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printf("Check confirms that d_a and d_b share a virtual address.\n");
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} else {
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printf("Check shows that d_a and d_b DO NOT share a virtual address.\n");
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}
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checkCudaErrors(
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cudaGraphAddMemFreeNode(&freeNodeB, graph, &allocNodeB, 1, (void *)d_b));
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checkCudaErrors(cudaGraphInstantiate(graphExec, graph, NULL, NULL, 0));
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checkCudaErrors(cudaGraphDestroy(graph));
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}
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void virtualAddressReuseSingleGraph(size_t bytes, int device) {
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cudaStream_t stream;
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cudaGraphExec_t graphExec;
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printf("================================\n");
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printf("Running virtual address reuse example.\n");
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printf(
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"Sequential allocations & frees within a single graph enable CUDA to "
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"reuse virtual addresses.\n\n");
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createVirtAddrReuseGraph(&graphExec, bytes, device);
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checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
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checkCudaErrors(cudaGraphLaunch(graphExec, stream));
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checkCudaErrors(cudaStreamSynchronize(stream));
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printMemoryFootprint(device);
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checkCudaErrors(cudaGraphExecDestroy(graphExec));
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checkCudaErrors(cudaStreamDestroy(stream));
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}
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// This is a kernel that does no real work but runs at least for a specified
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// number of clocks
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__global__ void clockBlock(clock_t clock_count) {
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unsigned int start_clock = (unsigned int)clock();
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clock_t clock_offset = 0;
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while (clock_offset < clock_count) {
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unsigned int end_clock = (unsigned int)clock();
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// The code below should work like
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// this (thanks to modular arithmetics):
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//
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// clock_offset = (clock_t) (end_clock > start_clock ?
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// end_clock - start_clock :
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// end_clock + (0xffffffffu - start_clock));
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//
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// Indeed, let m = 2^32 then
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// end - start = end + m - start (mod m).
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clock_offset = (clock_t)(end_clock - start_clock);
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}
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}
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// A pointer to the allocated device buffer is returned in dPtr so the caller
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// can compare virtual addresses. The kernel node is added to increase the
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// graph's runtime.
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void createSimpleAllocFreeGraph(cudaGraphExec_t *graphExec, float **dPtr,
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size_t bytes, int device) {
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cudaGraph_t graph;
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cudaGraphNode_t allocNodeA, freeNodeA, blockDeviceNode;
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cudaMemAllocNodeParams allocParams;
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cudaKernelNodeParams blockDeviceNodeParams = {0};
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int numElements = bytes / sizeof(float);
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float kernelTime = 5; // time for each thread to run in microseconds
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checkCudaErrors(cudaGraphCreate(&graph, 0));
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prepareAllocParams(&allocParams, bytes, device);
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checkCudaErrors(
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cudaGraphAddMemAllocNode(&allocNodeA, graph, NULL, 0, &allocParams));
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*dPtr = (float *)allocParams.dptr;
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cudaDeviceProp deviceProp;
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, device));
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clock_t time_clocks = (clock_t)((kernelTime / 1000.0) * deviceProp.clockRate);
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void *blockDeviceArgs[1] = {(void *)&time_clocks};
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size_t numBlocks = numElements / (size_t)THREADS_PER_BLOCK;
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blockDeviceNodeParams.gridDim = dim3(numBlocks, 1, 1);
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blockDeviceNodeParams.blockDim = dim3(THREADS_PER_BLOCK, 1, 1);
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blockDeviceNodeParams.sharedMemBytes = 0;
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blockDeviceNodeParams.extra = NULL;
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blockDeviceNodeParams.func = (void *)clockBlock;
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blockDeviceNodeParams.kernelParams = (void **)blockDeviceArgs;
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checkCudaErrors(cudaGraphAddKernelNode(&blockDeviceNode, graph, &allocNodeA,
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1, &blockDeviceNodeParams));
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checkCudaErrors(cudaGraphAddMemFreeNode(&freeNodeA, graph, &blockDeviceNode,
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1, (void *)*dPtr));
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checkCudaErrors(cudaGraphInstantiate(graphExec, graph, NULL, NULL, 0));
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checkCudaErrors(cudaGraphDestroy(graph));
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}
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void physicalMemoryReuseSingleStream(size_t bytes, int device) {
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cudaStream_t stream;
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cudaGraphExec_t graphExecs[NUM_GRAPHS];
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float *dPtrs[NUM_GRAPHS];
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bool virtualAddrDiffer = true;
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printf("================================\n");
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printf("Running physical memory reuse example.\n");
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printf(
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"CUDA reuses the same physical memory for allocations from separate "
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"graphs when the allocation lifetimes don't overlap.\n\n");
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for (int i = 0; i < NUM_GRAPHS; i++) {
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createSimpleAllocFreeGraph(&graphExecs[i], &dPtrs[i], bytes, device);
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}
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printf("Creating the graph execs does not reserve any physical memory.\n");
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printMemoryFootprint(device);
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checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
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checkCudaErrors(cudaGraphLaunch(graphExecs[0], stream));
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printf("\nThe first graph launched reserves the memory it needs.\n");
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printMemoryFootprint(device);
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checkCudaErrors(cudaGraphLaunch(graphExecs[0], stream));
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printf(
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"A subsequent launch of the same graph in the same stream reuses the "
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"same physical memory. ");
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printf("Thus the memory footprint does not grow here.\n");
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printMemoryFootprint(device);
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printf(
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"\nSubsequent launches of other graphs in the same stream also reuse the "
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"physical memory. ");
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printf("Thus the memory footprint does not grow here.\n");
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for (int i = 1; i < NUM_GRAPHS; i++) {
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checkCudaErrors(cudaGraphLaunch(graphExecs[i], stream));
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printf("%02d: ", i);
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printMemoryFootprint(device);
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}
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checkCudaErrors(cudaStreamSynchronize(stream));
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for (int i = 0; i < NUM_GRAPHS; i++) {
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for (int j = i + 1; j < NUM_GRAPHS; j++) {
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if (dPtrs[i] == dPtrs[j]) {
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virtualAddrDiffer = false;
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printf("Error: Graph exec %d and %d have the same virtual address!\n",
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i - 1, i);
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}
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}
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checkCudaErrors(cudaGraphExecDestroy(graphExecs[i]));
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}
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if (virtualAddrDiffer) {
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printf("\nCheck confirms all graphs use a different virtual address.\n");
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} else {
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printf(
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"\nAll graphs do NOT use different virtual addresses. Exiting test.\n");
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exit(EXIT_FAILURE);
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}
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checkCudaErrors(cudaStreamDestroy(stream));
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}
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void simultaneousStreams(size_t bytes, int device) {
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cudaStream_t streams[NUM_GRAPHS];
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cudaGraphExec_t graphExecs[NUM_GRAPHS];
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float *dPtrs[NUM_GRAPHS];
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printf("================================\n");
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printf("Running simultaneous streams example.\n");
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printf("Graphs that can run concurrently need separate physical memory. ");
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printf(
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"In this example, each graph launched in a separate stream increases the "
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"total memory footprint.\n\n");
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printf(
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"When launching a new graph, CUDA may reuse physical memory from a graph "
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"whose execution has already ");
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printf(
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"finished -- even if the new graph is being launched in a different "
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"stream from the completed graph. ");
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printf(
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"Therefore, a kernel node is added to the graphs to increase "
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"runtime.\n\n");
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for (int i = 0; i < NUM_GRAPHS; i++) {
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createSimpleAllocFreeGraph(&graphExecs[i], &dPtrs[i], bytes, device);
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checkCudaErrors(
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cudaStreamCreateWithFlags(&streams[i], cudaStreamNonBlocking));
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}
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printf("Initial footprint:\n");
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printMemoryFootprint(device);
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printf(
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"\nEach graph launch in a seperate stream grows the memory footprint:\n");
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for (int i = 1; i < NUM_GRAPHS; i++) {
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checkCudaErrors(cudaGraphLaunch(graphExecs[i], streams[i]));
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printf("%02d: ", i);
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printMemoryFootprint(device);
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}
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for (int i = 0; i < NUM_GRAPHS; i++) {
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checkCudaErrors(cudaStreamSynchronize(streams[i]));
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checkCudaErrors(cudaGraphExecDestroy(graphExecs[i]));
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checkCudaErrors(cudaStreamDestroy(streams[i]));
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}
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}
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void createSimpleAllocNoFreeGraph(cudaGraphExec_t *graphExec, float **dPtr,
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size_t bytes, int device) {
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cudaGraph_t graph;
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cudaGraphNode_t allocNodeA;
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cudaMemAllocNodeParams allocParams;
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checkCudaErrors(cudaGraphCreate(&graph, 0));
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prepareAllocParams(&allocParams, bytes, device);
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checkCudaErrors(
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cudaGraphAddMemAllocNode(&allocNodeA, graph, NULL, 0, &allocParams));
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*dPtr = (float *)allocParams.dptr;
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checkCudaErrors(cudaGraphInstantiate(graphExec, graph, NULL, NULL, 0));
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checkCudaErrors(cudaGraphDestroy(graph));
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}
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void unfreedAllocations(size_t bytes, int device) {
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cudaStream_t stream;
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cudaGraphExec_t graphExecs[NUM_GRAPHS];
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float *dPtrs[NUM_GRAPHS];
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printf("================================\n");
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printf("Running unfreed streams example.\n");
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printf(
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"CUDA cannot reuse phyiscal memory from graphs which do not free their "
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"allocations.\n\n");
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for (int i = 0; i < NUM_GRAPHS; i++) {
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createSimpleAllocNoFreeGraph(&graphExecs[i], &dPtrs[i], bytes, device);
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}
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checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
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printf(
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"Despite being launched in the same stream, each graph launch grows the "
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"memory footprint. ");
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printf(
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"Since the allocation is not freed, CUDA keeps the memory valid for "
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"use.\n");
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for (int i = 0; i < NUM_GRAPHS; i++) {
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checkCudaErrors(cudaGraphLaunch(graphExecs[i], stream));
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printf("%02d: ", i);
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printMemoryFootprint(device);
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}
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checkCudaErrors(cudaStreamSynchronize(stream));
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checkCudaErrors(cudaDeviceGraphMemTrim(device));
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printf(
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"\nTrimming does not impact the memory footprint since the un-freed "
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"allocations are still holding onto the memory.\n");
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printMemoryFootprint(device);
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for (int i = 0; i < NUM_GRAPHS; i++) {
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checkCudaErrors(cudaFree(dPtrs[i]));
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}
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printf("\nFreeing the allocations does not shrink the footprint.\n");
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printMemoryFootprint(device);
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checkCudaErrors(cudaDeviceGraphMemTrim(device));
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printf(
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"\nSince the allocations are now freed, trimming does reduce the "
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"footprint even when the graph execs are not yet destroyed.\n");
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printMemoryFootprint(device);
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for (int i = 0; i < NUM_GRAPHS; i++) {
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checkCudaErrors(cudaGraphExecDestroy(graphExecs[i]));
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}
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checkCudaErrors(cudaStreamDestroy(stream));
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}
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void cleanupMemory(int device) {
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checkCudaErrors(cudaDeviceGraphMemTrim(device));
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printf("\nCleaning up example by trimming device memory.\n");
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printMemoryFootprint(device);
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printf("\n");
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}
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int main(int argc, char **argv) {
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size_t bytes = 64 * 1024 * 1024;
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int device = findCudaDevice(argc, (const char **)argv);
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int driverVersion = 0;
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int deviceSupportsMemoryPools = 0;
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cudaDriverGetVersion(&driverVersion);
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printf("Driver version is: %d.%d\n", driverVersion / 1000,
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(driverVersion % 100) / 10);
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if (driverVersion < 11040) {
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printf("Waiving execution as driver does not support Graph Memory Nodes\n");
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exit(EXIT_WAIVED);
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}
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cudaDeviceGetAttribute(&deviceSupportsMemoryPools,
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cudaDevAttrMemoryPoolsSupported, device);
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if (!deviceSupportsMemoryPools) {
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printf("Waiving execution as device does not support Memory Pools\n");
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exit(EXIT_WAIVED);
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} else {
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printf("Running sample.\n");
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}
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virtualAddressReuseSingleGraph(bytes, device);
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cleanupMemory(device);
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physicalMemoryReuseSingleStream(bytes, device);
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cleanupMemory(device);
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simultaneousStreams(bytes, device);
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cleanupMemory(device);
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unfreedAllocations(bytes, device);
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cleanupMemory(device);
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printf("================================\n");
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printf("Sample complete.\n");
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}
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