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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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18
cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/.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/4_CUDA_Libraries/simpleCUFFT_MGPU/.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/4_CUDA_Libraries/simpleCUFFT_MGPU/CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 3.20)
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules")
project(simpleCUFFT_MGPU LANGUAGES CUDA)
# Disable response file for libraries on QNX as qcc does not support lib paths with double quotes
if(CMAKE_SYSTEM_NAME STREQUAL "QNX")
set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF)
endif()
find_package(CUDAToolkit REQUIRED)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120)
set(CMAKE_CUDA_FLAGS "${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)
# Source file
# Add target for simpleCUFFT_MGPU
add_executable(simpleCUFFT_MGPU simpleCUFFT_MGPU.cu)
target_compile_options(simpleCUFFT_MGPU PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>)
target_compile_features(simpleCUFFT_MGPU PRIVATE cxx_std_17 cuda_std_17)
set_target_properties(simpleCUFFT_MGPU PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
target_link_libraries(simpleCUFFT_MGPU PRIVATE
CUDA::cufft
)
# Include installation configuration
include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake)
setup_samples_install()

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cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/README.md Normal file
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# simpleCUFFT_MGPU - Simple CUFFT_MGPU
## Description
Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPU.
## Key Concepts
Image Processing, CUFFT Library
## Supported SM Architectures
[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus)
## 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)
cudaXtFree, cudaSetDevice, cudaGetDeviceCount, cudaDeviceSynchronize, cudaGetDeviceProperties
## Dependencies needed to build/run
[CUFFT](../../../README.md#cufft)
## 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/4_CUDA_Libraries/simpleCUFFT_MGPU/simpleCUFFT_MGPU.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.
*/
/* Example showing the use of CUFFT for fast 1D-convolution using FFT. */
// System includes
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
// CUDA runtime
#include <cuda_runtime.h>
// CUFFT Header file
#include <cufftXt.h>
// helper functions and utilities to work with CUDA
#include <helper_cuda.h>
#include <helper_functions.h>
// Complex data type
typedef float2 Complex;
static __device__ __host__ inline Complex ComplexAdd(Complex, Complex);
static __device__ __host__ inline Complex ComplexScale(Complex, float);
static __device__ __host__ inline Complex ComplexMul(Complex, Complex);
static __global__ void ComplexPointwiseMulAndScale(cufftComplex *, cufftComplex *, int, float);
// Kernel for GPU
void multiplyCoefficient(cudaLibXtDesc *, cudaLibXtDesc *, int, float, int);
// Filtering functions
void Convolve(const Complex *, int, const Complex *, int, Complex *);
// Padding functions
int PadData(const Complex *, Complex **, int, const Complex *, Complex **, int);
////////////////////////////////////////////////////////////////////////////////
// Data configuration
// The filter size is assumed to be a number smaller than the signal size
///////////////////////////////////////////////////////////////////////////////
const int SIGNAL_SIZE = 1018;
const int FILTER_KERNEL_SIZE = 11;
const int GPU_COUNT = 2;
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv)
{
printf("\n[simpleCUFFT_MGPU] is starting...\n\n");
int GPU_N;
checkCudaErrors(cudaGetDeviceCount(&GPU_N));
if (GPU_N < GPU_COUNT) {
printf("No. of GPU on node %d\n", GPU_N);
printf("Two GPUs are required to run simpleCUFFT_MGPU sample code\n");
exit(EXIT_WAIVED);
}
int *major_minor = (int *)malloc(sizeof(int) * GPU_N * 2);
int found2IdenticalGPUs = 0;
int nGPUs = 2;
int *whichGPUs;
whichGPUs = (int *)malloc(sizeof(int) * nGPUs);
for (int i = 0; i < GPU_N; i++) {
cudaDeviceProp deviceProp;
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, i));
major_minor[i * 2] = deviceProp.major;
major_minor[i * 2 + 1] = deviceProp.minor;
printf("GPU Device %d: \"%s\" with compute capability %d.%d\n",
i,
deviceProp.name,
deviceProp.major,
deviceProp.minor);
}
for (int i = 0; i < GPU_N; i++) {
for (int j = i + 1; j < GPU_N; j++) {
if ((major_minor[i * 2] == major_minor[j * 2]) && (major_minor[i * 2 + 1] == major_minor[j * 2 + 1])) {
whichGPUs[0] = i;
whichGPUs[1] = j;
found2IdenticalGPUs = 1;
break;
}
}
if (found2IdenticalGPUs) {
break;
}
}
free(major_minor);
if (!found2IdenticalGPUs) {
printf("No Two GPUs with same architecture found\nWaiving simpleCUFFT_2d_MGPU "
"sample\n");
exit(EXIT_WAIVED);
}
// Allocate host memory for the signal
Complex *h_signal = (Complex *)malloc(sizeof(Complex) * SIGNAL_SIZE);
// Initialize the memory for the signal
for (int i = 0; i < SIGNAL_SIZE; ++i) {
h_signal[i].x = rand() / (float)RAND_MAX;
h_signal[i].y = 0;
}
// Allocate host memory for the filter
Complex *h_filter_kernel = (Complex *)malloc(sizeof(Complex) * FILTER_KERNEL_SIZE);
// Initialize the memory for the filter
for (int i = 0; i < FILTER_KERNEL_SIZE; ++i) {
h_filter_kernel[i].x = rand() / (float)RAND_MAX;
h_filter_kernel[i].y = 0;
}
// Pad signal and filter kernel
Complex *h_padded_signal;
Complex *h_padded_filter_kernel;
int new_size =
PadData(h_signal, &h_padded_signal, SIGNAL_SIZE, h_filter_kernel, &h_padded_filter_kernel, FILTER_KERNEL_SIZE);
// cufftCreate() - Create an empty plan
cufftResult result;
cufftHandle plan_input;
checkCudaErrors(cufftCreate(&plan_input));
// cufftXtSetGPUs() - Define which GPUs to use
result = cufftXtSetGPUs(plan_input, nGPUs, whichGPUs);
if (result == CUFFT_INVALID_DEVICE) {
printf("This sample requires two GPUs on the same board.\n");
printf("No such board was found. Waiving sample.\n");
exit(EXIT_WAIVED);
}
else if (result != CUFFT_SUCCESS) {
printf("cufftXtSetGPUs failed\n");
exit(EXIT_FAILURE);
}
// Print the device information to run the code
printf("\nRunning on GPUs\n");
for (int i = 0; i < nGPUs; i++) {
cudaDeviceProp deviceProp;
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, whichGPUs[i]));
printf("GPU Device %d: \"%s\" with compute capability %d.%d\n",
whichGPUs[i],
deviceProp.name,
deviceProp.major,
deviceProp.minor);
}
size_t *worksize;
worksize = (size_t *)malloc(sizeof(size_t) * nGPUs);
// cufftMakePlan1d() - Create the plan
checkCudaErrors(cufftMakePlan1d(plan_input, new_size, CUFFT_C2C, 1, worksize));
// cufftXtMalloc() - Malloc data on multiple GPUs
cudaLibXtDesc *d_signal;
checkCudaErrors(cufftXtMalloc(plan_input, (cudaLibXtDesc **)&d_signal, CUFFT_XT_FORMAT_INPLACE));
cudaLibXtDesc *d_out_signal;
checkCudaErrors(cufftXtMalloc(plan_input, (cudaLibXtDesc **)&d_out_signal, CUFFT_XT_FORMAT_INPLACE));
cudaLibXtDesc *d_filter_kernel;
checkCudaErrors(cufftXtMalloc(plan_input, (cudaLibXtDesc **)&d_filter_kernel, CUFFT_XT_FORMAT_INPLACE));
cudaLibXtDesc *d_out_filter_kernel;
checkCudaErrors(cufftXtMalloc(plan_input, (cudaLibXtDesc **)&d_out_filter_kernel, CUFFT_XT_FORMAT_INPLACE));
// cufftXtMemcpy() - Copy data from host to multiple GPUs
checkCudaErrors(cufftXtMemcpy(plan_input, d_signal, h_padded_signal, CUFFT_COPY_HOST_TO_DEVICE));
checkCudaErrors(cufftXtMemcpy(plan_input, d_filter_kernel, h_padded_filter_kernel, CUFFT_COPY_HOST_TO_DEVICE));
// cufftXtExecDescriptorC2C() - Execute FFT on data on multiple GPUs
checkCudaErrors(cufftXtExecDescriptorC2C(plan_input, d_signal, d_signal, CUFFT_FORWARD));
checkCudaErrors(cufftXtExecDescriptorC2C(plan_input, d_filter_kernel, d_filter_kernel, CUFFT_FORWARD));
// cufftXtMemcpy() - Copy the data to natural order on GPUs
checkCudaErrors(cufftXtMemcpy(plan_input, d_out_signal, d_signal, CUFFT_COPY_DEVICE_TO_DEVICE));
checkCudaErrors(cufftXtMemcpy(plan_input, d_out_filter_kernel, d_filter_kernel, CUFFT_COPY_DEVICE_TO_DEVICE));
printf("\n\nValue of Library Descriptor\n");
printf("Number of GPUs %d\n", d_out_signal->descriptor->nGPUs);
printf("Device id %d %d\n", d_out_signal->descriptor->GPUs[0], d_out_signal->descriptor->GPUs[1]);
printf("Data size on GPU %ld %ld\n",
(long)(d_out_signal->descriptor->size[0] / sizeof(cufftComplex)),
(long)(d_out_signal->descriptor->size[1] / sizeof(cufftComplex)));
// Multiply the coefficients together and normalize the result
printf("Launching ComplexPointwiseMulAndScale<<< >>>\n");
multiplyCoefficient(d_out_signal, d_out_filter_kernel, new_size, 1.0f / new_size, nGPUs);
// cufftXtExecDescriptorC2C() - Execute inverse FFT on data on multiple GPUs
printf("Transforming signal back cufftExecC2C\n");
checkCudaErrors(cufftXtExecDescriptorC2C(plan_input, d_out_signal, d_out_signal, CUFFT_INVERSE));
// Create host pointer pointing to padded signal
Complex *h_convolved_signal = h_padded_signal;
// Allocate host memory for the convolution result
Complex *h_convolved_signal_ref = (Complex *)malloc(sizeof(Complex) * SIGNAL_SIZE);
// cufftXtMemcpy() - Copy data from multiple GPUs to host
checkCudaErrors(cufftXtMemcpy(plan_input, h_convolved_signal, d_out_signal, CUFFT_COPY_DEVICE_TO_HOST));
// Convolve on the host
Convolve(h_signal, SIGNAL_SIZE, h_filter_kernel, FILTER_KERNEL_SIZE, h_convolved_signal_ref);
// Compare CPU and GPU result
bool bTestResult =
sdkCompareL2fe((float *)h_convolved_signal_ref, (float *)h_convolved_signal, 2 * SIGNAL_SIZE, 1e-5f);
printf("\nvalue of TestResult %d\n", bTestResult);
// Cleanup memory
free(whichGPUs);
free(worksize);
free(h_signal);
free(h_filter_kernel);
free(h_padded_signal);
free(h_padded_filter_kernel);
free(h_convolved_signal_ref);
// cudaXtFree() - Free GPU memory
checkCudaErrors(cufftXtFree(d_signal));
checkCudaErrors(cufftXtFree(d_filter_kernel));
checkCudaErrors(cufftXtFree(d_out_signal));
checkCudaErrors(cufftXtFree(d_out_filter_kernel));
// cufftDestroy() - Destroy FFT plan
checkCudaErrors(cufftDestroy(plan_input));
exit(bTestResult ? EXIT_SUCCESS : EXIT_FAILURE);
}
///////////////////////////////////////////////////////////////////////////////////
// Function for padding original data
//////////////////////////////////////////////////////////////////////////////////
int PadData(const Complex *signal,
Complex **padded_signal,
int signal_size,
const Complex *filter_kernel,
Complex **padded_filter_kernel,
int filter_kernel_size)
{
int minRadius = filter_kernel_size / 2;
int maxRadius = filter_kernel_size - minRadius;
int new_size = signal_size + maxRadius;
// Pad signal
Complex *new_data = (Complex *)malloc(sizeof(Complex) * new_size);
memcpy(new_data + 0, signal, signal_size * sizeof(Complex));
memset(new_data + signal_size, 0, (new_size - signal_size) * sizeof(Complex));
*padded_signal = new_data;
// Pad filter
new_data = (Complex *)malloc(sizeof(Complex) * new_size);
memcpy(new_data + 0, filter_kernel + minRadius, maxRadius * sizeof(Complex));
memset(new_data + maxRadius, 0, (new_size - filter_kernel_size) * sizeof(Complex));
memcpy(new_data + new_size - minRadius, filter_kernel, minRadius * sizeof(Complex));
*padded_filter_kernel = new_data;
return new_size;
}
////////////////////////////////////////////////////////////////////////////////
// Filtering operations - Computing Convolution on the host
////////////////////////////////////////////////////////////////////////////////
void Convolve(const Complex *signal,
int signal_size,
const Complex *filter_kernel,
int filter_kernel_size,
Complex *filtered_signal)
{
int minRadius = filter_kernel_size / 2;
int maxRadius = filter_kernel_size - minRadius;
// Loop over output element indices
for (int i = 0; i < signal_size; ++i) {
filtered_signal[i].x = filtered_signal[i].y = 0;
// Loop over convolution indices
for (int j = -maxRadius + 1; j <= minRadius; ++j) {
int k = i + j;
if (k >= 0 && k < signal_size) {
filtered_signal[i] =
ComplexAdd(filtered_signal[i], ComplexMul(signal[k], filter_kernel[minRadius - j]));
}
}
}
}
////////////////////////////////////////////////////////////////////////////////
// Launch Kernel on multiple GPU
////////////////////////////////////////////////////////////////////////////////
void multiplyCoefficient(cudaLibXtDesc *d_signal, cudaLibXtDesc *d_filter_kernel, int new_size, float val, int nGPUs)
{
int device;
// Launch the ComplexPointwiseMulAndScale<<< >>> kernel on multiple GPU
for (int i = 0; i < nGPUs; i++) {
device = d_signal->descriptor->GPUs[i];
// Set device
checkCudaErrors(cudaSetDevice(device));
// Perform GPU computations
ComplexPointwiseMulAndScale<<<32, 256>>>((cufftComplex *)d_signal->descriptor->data[i],
(cufftComplex *)d_filter_kernel->descriptor->data[i],
int(d_signal->descriptor->size[i] / sizeof(cufftComplex)),
val);
}
// Wait for device to finish all operation
for (int i = 0; i < nGPUs; i++) {
device = d_signal->descriptor->GPUs[i];
checkCudaErrors(cudaSetDevice(device));
cudaDeviceSynchronize();
// Check if kernel execution generated and error
getLastCudaError("Kernel execution failed [ ComplexPointwiseMulAndScale ]");
}
}
////////////////////////////////////////////////////////////////////////////////
// Complex operations
////////////////////////////////////////////////////////////////////////////////
// Complex addition
static __device__ __host__ inline Complex ComplexAdd(Complex a, Complex b)
{
Complex c;
c.x = a.x + b.x;
c.y = a.y + b.y;
return c;
}
// Complex scale
static __device__ __host__ inline Complex ComplexScale(Complex a, float s)
{
Complex c;
c.x = s * a.x;
c.y = s * a.y;
return c;
}
// Complex multiplication
static __device__ __host__ inline Complex ComplexMul(Complex a, Complex b)
{
Complex c;
c.x = a.x * b.x - a.y * b.y;
c.y = a.x * b.y + a.y * b.x;
return c;
}
// Complex pointwise multiplication
static __global__ void ComplexPointwiseMulAndScale(cufftComplex *a, cufftComplex *b, int size, float scale)
{
const int numThreads = blockDim.x * gridDim.x;
const int threadID = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = threadID; i < size; i += numThreads) {
a[i] = ComplexScale(ComplexMul(a[i], b[i]), scale);
}
}