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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/* Vector addition: C = A + B.
*
* This sample replaces the device allocation in the vectorAddDrvsample with
* cuMemMap-ed allocations. This sample demonstrates that the cuMemMap api
* allows the user to specify the physical properties of their memory while
* retaining the contiguos nature of their access, thus not requiring a change
* in their program structure.
*
*/
// Includes
#include <cuda.h>
#include <stdio.h>
#include <string.h>
#include <cstring>
#include <iostream>
// includes, project
#include <helper_cuda_drvapi.h>
#include <helper_functions.h>
// includes, CUDA
#include <builtin_types.h>
#include "multidevicealloc_memmap.hpp"
using namespace std;
// Variables
CUdevice cuDevice;
CUcontext cuContext;
CUmodule cuModule;
CUfunction vecAdd_kernel;
float *h_A;
float *h_B;
float *h_C;
CUdeviceptr d_A;
CUdeviceptr d_B;
CUdeviceptr d_C;
size_t allocationSize = 0;
// Functions
int CleanupNoFailure();
void RandomInit(float *, int);
//define input fatbin file
#ifndef FATBIN_FILE
#define FATBIN_FILE "vectorAdd_kernel64.fatbin"
#endif
// collect all of the devices whose memory can be mapped from cuDevice.
vector<CUdevice> getBackingDevices(CUdevice cuDevice) {
int num_devices;
checkCudaErrors(cuDeviceGetCount(&num_devices));
vector<CUdevice> backingDevices;
backingDevices.push_back(cuDevice);
for (int dev = 0; dev < num_devices; dev++) {
int capable = 0;
int attributeVal = 0;
// The mapping device is already in the backingDevices vector
if (dev == cuDevice) {
continue;
}
// Only peer capable devices can map each others memory
checkCudaErrors(cuDeviceCanAccessPeer(&capable, cuDevice, dev));
if (!capable) {
continue;
}
// The device needs to support virtual address management for the required
// apis to work
checkCudaErrors(cuDeviceGetAttribute(
&attributeVal, CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED,
cuDevice));
if (attributeVal == 0) {
continue;
}
backingDevices.push_back(dev);
}
return backingDevices;
}
// Host code
int main(int argc, char **argv) {
printf("Vector Addition (Driver API)\n");
int N = 50000;
size_t size = N * sizeof(float);
int attributeVal = 0;
// Initialize
checkCudaErrors(cuInit(0));
cuDevice = findCudaDeviceDRV(argc, (const char **)argv);
// Check that the selected device supports virtual address management
checkCudaErrors(cuDeviceGetAttribute(
&attributeVal, CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED,
cuDevice));
printf("Device %d VIRTUAL ADDRESS MANAGEMENT SUPPORTED = %d.\n", cuDevice,
attributeVal);
if (attributeVal == 0) {
printf("Device %d doesn't support VIRTUAL ADDRESS MANAGEMENT.\n", cuDevice);
exit(EXIT_WAIVED);
}
// The vector addition happens on cuDevice, so the allocations need to be
// mapped there.
vector<CUdevice> mappingDevices;
mappingDevices.push_back(cuDevice);
// Collect devices accessible by the mapping device (cuDevice) into the
// backingDevices vector.
vector<CUdevice> backingDevices = getBackingDevices(cuDevice);
// Create context
checkCudaErrors(cuCtxCreate(&cuContext, 0, cuDevice));
// first search for the module path before we load the results
string module_path;
std::ostringstream fatbin;
if (!findFatbinPath(FATBIN_FILE, module_path, argv, fatbin))
{
exit(EXIT_FAILURE);
}
else
{
printf("> initCUDA loading module: <%s>\n", module_path.c_str());
}
if (!fatbin.str().size())
{
printf("fatbin file empty. exiting..\n");
exit(EXIT_FAILURE);
}
// Create module from binary file (FATBIN)
checkCudaErrors(cuModuleLoadData(&cuModule, fatbin.str().c_str()));
// Get function handle from module
checkCudaErrors(cuModuleGetFunction(&vecAdd_kernel, cuModule, "VecAdd_kernel"));
// Allocate input vectors h_A and h_B in host memory
h_A = (float *)malloc(size);
h_B = (float *)malloc(size);
h_C = (float *)malloc(size);
// Initialize input vectors
RandomInit(h_A, N);
RandomInit(h_B, N);
// Allocate vectors in device memory
// note that a call to cuCtxEnablePeerAccess is not needed even though
// the backing devices and mapping device are not the same.
// This is because the cuMemSetAccess call explicitly specifies
// the cross device mapping.
// cuMemSetAccess is still subject to the constraints of cuDeviceCanAccessPeer
// for cross device mappings (hence why we checked cuDeviceCanAccessPeer earlier).
checkCudaErrors(simpleMallocMultiDeviceMmap(&d_A, &allocationSize, size, backingDevices, mappingDevices));
checkCudaErrors(simpleMallocMultiDeviceMmap(&d_B, NULL, size, backingDevices, mappingDevices));
checkCudaErrors(simpleMallocMultiDeviceMmap(&d_C, NULL, size, backingDevices, mappingDevices));
// Copy vectors from host memory to device memory
checkCudaErrors(cuMemcpyHtoD(d_A, h_A, size));
checkCudaErrors(cuMemcpyHtoD(d_B, h_B, size));
// This is the new CUDA 4.0 API for Kernel Parameter Passing and Kernel Launch (simpler method)
// Grid/Block configuration
int threadsPerBlock = 256;
int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
void *args[] = { &d_A, &d_B, &d_C, &N };
// Launch the CUDA kernel
checkCudaErrors(cuLaunchKernel(vecAdd_kernel, blocksPerGrid, 1, 1,
threadsPerBlock, 1, 1,
0,
NULL, args, NULL));
// Copy result from device memory to host memory
// h_C contains the result in host memory
checkCudaErrors(cuMemcpyDtoH(h_C, d_C, size));
// Verify result
int i;
for (i = 0; i < N; ++i)
{
float sum = h_A[i] + h_B[i];
if (fabs(h_C[i] - sum) > 1e-7f)
{
break;
}
}
CleanupNoFailure();
printf("%s\n", (i==N) ? "Result = PASS" : "Result = FAIL");
exit((i==N) ? EXIT_SUCCESS : EXIT_FAILURE);
}
int CleanupNoFailure()
{
// Free device memory
checkCudaErrors(simpleFreeMultiDeviceMmap(d_A, allocationSize));
checkCudaErrors(simpleFreeMultiDeviceMmap(d_B, allocationSize));
checkCudaErrors(simpleFreeMultiDeviceMmap(d_C, allocationSize));
// Free host memory
if (h_A)
{
free(h_A);
}
if (h_B)
{
free(h_B);
}
if (h_C)
{
free(h_C);
}
checkCudaErrors(cuCtxDestroy(cuContext));
return EXIT_SUCCESS;
}
// Allocates an array with random float entries.
void RandomInit(float *data, int n)
{
for (int i = 0; i < n; ++i)
{
data[i] = rand() / (float)RAND_MAX;
}
}
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