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CUDA 13.2 samples update (#432)

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- Added Python samples for CUDA Python 1.0 release
- Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
This commit is contained in:
Dheemanth
2026-05-13 15:13:18 -07:00
committed by GitHub
parent a4526d5229
commit aeab82ff30
2072 changed files with 15077 additions and 1970 deletions
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cpp/0_Introduction/UnifiedMemoryStreams/.vscode/c_cpp_properties.json vendored Normal file
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{
"configurations": [
{
"name": "Linux",
"includePath": [
"${workspaceFolder}/**",
"${workspaceFolder}/../../../Common"
],
"defines": [],
"compilerPath": "/usr/local/cuda/bin/nvcc",
"cStandard": "gnu17",
"cppStandard": "gnu++14",
"intelliSenseMode": "linux-gcc-x64",
"configurationProvider": "ms-vscode.makefile-tools"
}
],
"version": 4
}

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cpp/0_Introduction/UnifiedMemoryStreams/.vscode/extensions.json vendored Normal file
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{
"recommendations": [
"nvidia.nsight-vscode-edition",
"ms-vscode.cpptools",
"ms-vscode.makefile-tools"
]
}

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cpp/0_Introduction/UnifiedMemoryStreams/CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 3.20)
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules")
project(UnifiedMemoryStreams LANGUAGES C CXX CUDA)
find_package(CUDAToolkit REQUIRED)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120)
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets")
if(ENABLE_CUDA_DEBUG)
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets)
else()
set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option)
endif()
# Include directories and libraries
include_directories(../../../Common)
# This sample is not supported on QNX
if(CMAKE_SYSTEM_NAME STREQUAL "QNX")
message(STATUS "Will not build sample ${PROJECT_NAME} - not supported on QNX")
return()
endif()
# FindOpenMP: request COMPONENTS CXX only. From CMake 3.31 on, a bare find_package(OpenMP) also
# probes OpenMP for CUDA when the project enables CUDA; that check can fail even when C++ OpenMP
# works, and this sample only needs the C++ OpenMP package. Link OpenMP::OpenMP_CXX for libs/headers.
find_package(OpenMP COMPONENTS CXX)
if(OpenMP_CXX_FOUND)
# Add target for UnifiedMemoryStreams
add_executable(UnifiedMemoryStreams UnifiedMemoryStreams.cu)
target_compile_options(UnifiedMemoryStreams PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>)
target_compile_features(UnifiedMemoryStreams PRIVATE cxx_std_17 cuda_std_17)
set_target_properties(UnifiedMemoryStreams PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
target_link_libraries(UnifiedMemoryStreams PUBLIC
CUDA::cublas
OpenMP::OpenMP_CXX
)
else()
message(STATUS "OpenMP not found - will not build sample 'UnifiedMemoryStreams'")
endif()
# Include installation configuration
include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake)
setup_samples_install()

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cpp/0_Introduction/UnifiedMemoryStreams/README.md Normal file
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# UnifiedMemoryStreams - Unified Memory Streams
## Description
This sample demonstrates the use of OpenMP and streams with Unified Memory on a single GPU.
## Key Concepts
CUDA Systems Integration, OpenMP, CUBLAS, Multithreading, Unified Memory, CUDA Streams and Events
## Supported SM Architectures
## Supported OSes
Linux, Windows
## Supported CPU Architecture
x86_64, armv7l
## CUDA APIs involved
### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaStreamDestroy, cudaFree, cudaMallocManaged, cudaStreamAttachMemAsync, cudaSetDevice, cudaDeviceSynchronize, cudaStreamSynchronize, cudaStreamCreate, cudaGetDeviceProperties
## Dependencies needed to build/run
[OpenMP](../../../README.md#openmp), [UVM](../../../README.md#unified-virtual-memory), [CUBLAS](../../../README.md#cublas)
## Prerequisites
Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
## References (for more details)

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cpp/0_Introduction/UnifiedMemoryStreams/UnifiedMemoryStreams.cu Normal file
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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.
*/
/*
* This sample implements a simple task consumer using threads and streams
* with all data in Unified Memory, and tasks consumed by both host and device
*/
// system includes
#include <algorithm>
#include <cstdio>
#include <ctime>
#include <vector>
#ifdef USE_PTHREADS
#include <pthread.h>
#else
#include <omp.h>
#endif
#include <stdlib.h>
// cuBLAS
#include <cublas_v2.h>
// utilities
#include <helper_cuda.h>
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
// SRAND48 and DRAND48 don't exist on windows, but these are the equivalent
// functions
void srand48(long seed) { srand((unsigned int)seed); }
double drand48() { return double(rand()) / RAND_MAX; }
#endif
const char *sSDKname = "UnifiedMemoryStreams";
// simple task
template <typename T> struct Task
{
unsigned int size, id;
T *data;
T *result;
T *vector;
Task()
: size(0)
, id(0)
, data(NULL)
, result(NULL)
, vector(NULL) {};
Task(unsigned int s)
: size(s)
, id(0)
, data(NULL)
, result(NULL)
{
// allocate unified memory -- the operation performed in this example will
// be a DGEMV
checkCudaErrors(cudaMallocManaged(&data, sizeof(T) * size * size));
checkCudaErrors(cudaMallocManaged(&result, sizeof(T) * size));
checkCudaErrors(cudaMallocManaged(&vector, sizeof(T) * size));
checkCudaErrors(cudaDeviceSynchronize());
}
~Task()
{
// ensure all memory is deallocated
checkCudaErrors(cudaDeviceSynchronize());
checkCudaErrors(cudaFree(data));
checkCudaErrors(cudaFree(result));
checkCudaErrors(cudaFree(vector));
}
void allocate(const unsigned int s, const unsigned int unique_id)
{
// allocate unified memory outside of constructor
id = unique_id;
size = s;
checkCudaErrors(cudaMallocManaged(&data, sizeof(T) * size * size));
checkCudaErrors(cudaMallocManaged(&result, sizeof(T) * size));
checkCudaErrors(cudaMallocManaged(&vector, sizeof(T) * size));
checkCudaErrors(cudaDeviceSynchronize());
// populate data with random elements
for (unsigned int i = 0; i < size * size; i++) {
data[i] = drand48();
}
for (unsigned int i = 0; i < size; i++) {
result[i] = 0.;
vector[i] = drand48();
}
}
};
#ifdef USE_PTHREADS
struct threadData_t
{
int tid;
Task<double> *TaskListPtr;
cudaStream_t *streams;
cublasHandle_t *handles;
int taskSize;
};
typedef struct threadData_t threadData;
#endif
// simple host dgemv: assume data is in row-major format and square
template <typename T> void gemv(int m, int n, T alpha, T *A, T *x, T beta, T *result)
{
// rows
for (int i = 0; i < n; i++) {
result[i] *= beta;
for (int j = 0; j < n; j++) {
result[i] += A[i * n + j] * x[j];
}
}
}
// execute a single task on either host or device depending on size
#ifdef USE_PTHREADS
void *execute(void *inpArgs)
{
threadData *dataPtr = (threadData *)inpArgs;
cudaStream_t *stream = dataPtr->streams;
cublasHandle_t *handle = dataPtr->handles;
int tid = dataPtr->tid;
for (int i = 0; i < dataPtr->taskSize; i++) {
Task<double> &t = dataPtr->TaskListPtr[i];
if (t.size < 100) {
// perform on host
printf("Task [%d], thread [%d] executing on host (%d)\n", t.id, tid, t.size);
// attach managed memory to a (dummy) stream to allow host access while
// the device is running
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.data, 0, cudaMemAttachHost));
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.vector, 0, cudaMemAttachHost));
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.result, 0, cudaMemAttachHost));
// necessary to ensure Async cudaStreamAttachMemAsync calls have finished
checkCudaErrors(cudaStreamSynchronize(stream[0]));
// call the host operation
gemv(t.size, t.size, 1.0, t.data, t.vector, 0.0, t.result);
}
else {
// perform on device
printf("Task [%d], thread [%d] executing on device (%d)\n", t.id, tid, t.size);
double one = 1.0;
double zero = 0.0;
// attach managed memory to my stream
checkCudaErrors(cublasSetStream(handle[tid + 1], stream[tid + 1]));
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.data, 0, cudaMemAttachSingle));
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.vector, 0, cudaMemAttachSingle));
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.result, 0, cudaMemAttachSingle));
// call the device operation
checkCudaErrors(cublasDgemv(
handle[tid + 1], CUBLAS_OP_N, t.size, t.size, &one, t.data, t.size, t.vector, 1, &zero, t.result, 1));
}
}
pthread_exit(NULL);
}
#else
template <typename T> void execute(Task<T> &t, cublasHandle_t *handle, cudaStream_t *stream, int tid)
{
if (t.size < 100) {
// perform on host
printf("Task [%d], thread [%d] executing on host (%d)\n", t.id, tid, t.size);
// attach managed memory to a (dummy) stream to allow host access while the
// device is running
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.data, 0, cudaMemAttachHost));
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.vector, 0, cudaMemAttachHost));
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.result, 0, cudaMemAttachHost));
// necessary to ensure Async cudaStreamAttachMemAsync calls have finished
checkCudaErrors(cudaStreamSynchronize(stream[0]));
// call the host operation
gemv(t.size, t.size, 1.0, t.data, t.vector, 0.0, t.result);
}
else {
// perform on device
printf("Task [%d], thread [%d] executing on device (%d)\n", t.id, tid, t.size);
double one = 1.0;
double zero = 0.0;
// attach managed memory to my stream
checkCudaErrors(cublasSetStream(handle[tid + 1], stream[tid + 1]));
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.data, 0, cudaMemAttachSingle));
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.vector, 0, cudaMemAttachSingle));
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.result, 0, cudaMemAttachSingle));
// call the device operation
checkCudaErrors(cublasDgemv(
handle[tid + 1], CUBLAS_OP_N, t.size, t.size, &one, t.data, t.size, t.vector, 1, &zero, t.result, 1));
}
}
#endif
// populate a list of tasks with random sizes
template <typename T> void initialise_tasks(std::vector<Task<T>> &TaskList)
{
for (unsigned int i = 0; i < TaskList.size(); i++) {
// generate random size
int size;
size = std::max((int)(drand48() * 1000.0), 64);
TaskList[i].allocate(size, i);
}
}
int main(int argc, char **argv)
{
// set device
cudaDeviceProp device_prop;
int dev_id = findCudaDevice(argc, (const char **)argv);
checkCudaErrors(cudaGetDeviceProperties(&device_prop, dev_id));
if (!device_prop.managedMemory) {
// This samples requires being run on a device that supports Unified Memory
fprintf(stderr, "Unified Memory not supported on this device\n");
exit(EXIT_WAIVED);
}
int computeMode;
checkCudaErrors(cudaDeviceGetAttribute(&computeMode, cudaDevAttrComputeMode, dev_id));
if (computeMode == cudaComputeModeProhibited) {
// This sample requires being run with a default or process exclusive mode
fprintf(stderr,
"This sample requires a device in either default or process "
"exclusive mode\n");
exit(EXIT_WAIVED);
}
// randomise task sizes
int seed = (int)time(NULL);
srand48(seed);
// set number of threads
const int nthreads = 4;
// number of streams = number of threads
cudaStream_t *streams = new cudaStream_t[nthreads + 1];
cublasHandle_t *handles = new cublasHandle_t[nthreads + 1];
for (int i = 0; i < nthreads + 1; i++) {
checkCudaErrors(cudaStreamCreate(&streams[i]));
checkCudaErrors(cublasCreate(&handles[i]));
}
// create list of N tasks
unsigned int N = 40;
std::vector<Task<double>> TaskList(N);
initialise_tasks(TaskList);
printf("Executing tasks on host / device\n");
// run through all tasks using threads and streams
#ifdef USE_PTHREADS
pthread_t threads[nthreads];
threadData *InputToThreads = new threadData[nthreads];
for (int i = 0; i < nthreads; i++) {
checkCudaErrors(cudaSetDevice(dev_id));
InputToThreads[i].tid = i;
InputToThreads[i].streams = streams;
InputToThreads[i].handles = handles;
if ((TaskList.size() / nthreads) == 0) {
InputToThreads[i].taskSize = (TaskList.size() / nthreads);
InputToThreads[i].TaskListPtr = &TaskList[i * (TaskList.size() / nthreads)];
}
else {
if (i == nthreads - 1) {
InputToThreads[i].taskSize = (TaskList.size() / nthreads) + (TaskList.size() % nthreads);
InputToThreads[i].TaskListPtr =
&TaskList[i * (TaskList.size() / nthreads) + (TaskList.size() % nthreads)];
}
else {
InputToThreads[i].taskSize = (TaskList.size() / nthreads);
InputToThreads[i].TaskListPtr = &TaskList[i * (TaskList.size() / nthreads)];
}
}
pthread_create(&threads[i], NULL, &execute, &InputToThreads[i]);
}
for (int i = 0; i < nthreads; i++) {
pthread_join(threads[i], NULL);
}
#else
omp_set_num_threads(nthreads);
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < TaskList.size(); i++) {
checkCudaErrors(cudaSetDevice(dev_id));
int tid = omp_get_thread_num();
execute(TaskList[i], handles, streams, tid);
}
#endif
cudaDeviceSynchronize();
// Destroy CUDA Streams, cuBlas handles
for (int i = 0; i < nthreads + 1; i++) {
cudaStreamDestroy(streams[i]);
cublasDestroy(handles[i]);
}
// Free TaskList
std::vector<Task<double>>().swap(TaskList);
printf("All Done!\n");
exit(EXIT_SUCCESS);
}