ubuntu18.04 lower CUDA Three ways of using Cu in c/c + +

operating system

ubuntu 18.04


I want to be here c file using cuda's function, that is Content of cu

Installing nvcc is not the content here, but make sure that nvcc can be used, which is to ensure that it can be compiled On the premise of cu, view the version of nvcc, and the command is as follows

nvcc --version

The output is as follows

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Nov__3_21:07:56_CDT_2017
Cuda compilation tools, release 9.1, V9.1.85

The first way is to use the cuda function

The first way is to use cuda's functions in the form of lib

Five gpu threads are used in the foo file to execute the kernel function foo

The contents of the document are as follows

foo. The content of H is as follows. ifdef is added to facilitate our introduction If extern is not removed when calling c, an error will be reported

#ifndef FOO_CUH
#define FOO_CUH

#include <stdio.h>

//__global__ void foo();

#ifdef EXPORTS
extern "C"
void useCUDA();


foo. The contents are as follows:

#define EXPORTS

#include "foo.h"

#define CHECK(res) { if(res != cudaSuccess){printf("Error : %s:%d , ", __FILE__,__LINE__);   \
printf("code : %d , reason : %s \n", res,cudaGetErrorString(res));exit(-1);}}

__global__ void foo()

void useCUDA()
    printf("CUDA zeng!\n");

main.c the contents are as follows:

#include <stdio.h>
#include "foo.h"

int main()
    return 0;

Start compilation

Compile foo into with nvcc a library

nvcc -c foo.cu -o foo.o
ar cr libfoo.a foo.o

Check it out a whether the corresponding symbols have been exported

nm -g --defined-only libfoo.a

You can see the correct export

Link libfoo A and compile main c

gcc main.c -o main libfoo.a -lcudart -lcuda -lstdc++

Execute generated main


You can see the output as follows

If main is cpp, you can use the following command

g++ main.cpp -o main libfoo.a -lcudart -lcuda -lstdc++

Sort out the used sh file content

nvcc -c foo.cu -o foo.o
ar cr libfoo.a foo.o
nm -g --defined-only libfoo.a
gcc main.c -o main libfoo.a -lcudart -lcuda -lstdc++

The second way is to use the cuda function

This method does not intend to use lib to introduce direct channels o to load. This form is relatively simple, but not very flexible. If one day I need to modify the contents of foo files, I have to compile main again after recompiling

The second way is common c call mode, put the script directly

rm -rf *.o main
nvcc -c foo.cu -o foo.o
gcc -Wall -c main.c
gcc -o main foo.o main.o -lcudart -lcuda -lstdc++

The third way is to use the cuda function

Use in accordance with ffmpeg The way of cu, first of all cu compiled into ptx, and then load this in the source code ptx and use func inside, where ptx is platform independent assembly code

The following is the cubin used for the test Cu, he has three functions

#include <stdio.h>
#include <cuda_runtime.h>

extern "C"   __global__  void kernel_run(){
    printf("hello world!\n");

extern "C"   __global__  void kernel_run2(void *p1, void *p2){
    printf("p1:%c====p2:%c.\r\n", p1, p2);

extern "C"   __global__  void kernel_add(int *sum, int *p1, int *p2){
    *sum = *p1 + *p2;

main.c loads this first ptx file, then load these three functions and call

#include <stdio.h>
#include <string.h>
#include <cuda_runtime.h>
#include <cuda.h>

int main(){

    CUresult error;
    CUdevice cuDevice; 
    int deviceCount = 0;
    error = cuDeviceGetCount(&deviceCount);
    printf("device count is %d\n",deviceCount);
    error = cuDeviceGet(&cuDevice, 0); 

        printf("Error happened in get device!\n");

    CUcontext cuContext;
    error = cuCtxCreate(&cuContext, 0, cuDevice);
        printf("Error happened in create context!\n");

    // Use the compiled cubin
    CUmodule module;
    CUfunction function; // Call kernel_run
    CUfunction function2; // Call kernel_run2
    CUfunction function3; // Call kernel_add

    const char* module_file = "cubin.ptx";
    const char* kernel_name = "kernel_run";
    const char* kernel_name2 = "kernel_run2";
    const char* kernel_name3 = "kernel_add";

    error = cuModuleLoad(&module, module_file);
        printf("Error happened in load moudle %d!\n",error);

    // Test kernel_run function
    error = cuModuleGetFunction(&function, module, kernel_name);
        printf("get function error!\n");
    cuLaunchKernel(function, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0);

    // Test kernel_run2 function
    error = cuModuleGetFunction(&function2, module, kernel_name2);
        printf("get function error!\n");

    int age1 = 23;
    int age2 = 99;
    void *kernelParams[]= {(void *)&age1, (void *)&age2}; 
    cuLaunchKernel(function2, 1, 1, 1, 1, 1, 1, 0, 0, kernelParams, 0);

    // Test kernel_run3 function
    int *dev_a = 0;
    int *dev_b = 0;
    int *dev_c = 0;
    int cudaStatus = cudaMalloc((void**)&dev_a, sizeof(int));
    cudaStatus = cudaMalloc((void**)&dev_b, sizeof(int));
    cudaStatus = cudaMalloc((void**)&dev_c, sizeof(int));
    int h_a = 1;
    int h_b = 99;
    cudaMemcpy(dev_a, &h_a, sizeof(int), cudaMemcpyHostToDevice);
    cudaMemcpy(dev_b, &h_b, sizeof(int), cudaMemcpyHostToDevice);
    void *kernelParams3[]= {&dev_c, &dev_a, &dev_b};
    error = cuModuleGetFunction(&function3, module, kernel_name3);
        printf("get function error!\n");
    cuLaunchKernel(function3, 1, 1, 1, 1, 1, 1, 0, 0, kernelParams3, 0);
    // exchange data
    int sum;
    cudaMemcpy(&sum, dev_c, sizeof(int), cudaMemcpyDeviceToHost);
    printf("------%d------\r\n", sum);
    cudaFree( dev_a );
    cudaFree( dev_b );
    cudaFree( dev_c );

    return 1;

Execution sequence

  • 1. First compile with nvcc I got it ptx
  • 2. Compiling main with gcc C get the final executable program
  • 3. Execute the executable

The codes used are as follows

nvcc -ptx cubin.cu
gcc main.c -o main -lcudart -lcuda -lstdc++ && ./main

The output is as follows

device count is 1
hello world!

Keywords: Linux Ubuntu ffmpeg CUDA

Added by lhcpr on Tue, 21 Dec 2021 15:43:03 +0200