How to Run CUDA C or C++ on Jupyter (Google Colab)

Last Updated: June 3, 2019

CUDA is NVIDIA’s parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU. With Colab, you can work with CUDA C/C++ on the GPU for free.

Steps

1
Create a new Notebook. Click: here.
2
Click on New Python 3 Notebook at the bottom right corner of the window.

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3
Click on Runtime > Change runtime type.
4
Select GPU from the drop down menu and click on Save.

5

Uninstall any previous versions of CUDA completely. (The '!' added at the beginning of a line allows it to be executed as a command line command.)

!apt-get --purge remove cuda nvidia* libnvidia-*
!dpkg -l | grep cuda- | awk '{print $2}' | xargs -n1 dpkg --purge
!apt-get remove cuda-*
!apt autoremove
!apt-get update

6

Install CUDA Version 9.

!wget https://developer.nvidia.com/compute/cuda/9.2/Prod/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64 -O cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!dpkg -i cuda-repo-ubuntu1604-9-2-local_9.2.88-1_amd64.deb
!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
!apt-get update
!apt-get install cuda-9.2

7

Check your version using this code:

!nvcc --version

This should print something like this:

nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2018 NVIDIA Corporation Built on Wed_Apr_11_23:16:29_CDT_2018 Cuda compilation tools, release 9.2, V9.2.88

8
Execute the given command to install a small extension to run nvcc from Notebook cells.
9
Load the extension using this code:

10

Execute the code below to check if CUDA is working. To run CUDA C/C++ code in your notebook, add the %%cu extension at the beginning of your code.

%%cu
#include <stdio.h>
#include <stdlib.h>
__global__ void add(int *a, int *b, int *c) {
*c = *a + *b;
}
int main() {
int a, b, c;
// host copies of variables a, b & c
int *d_a, *d_b, *d_c;
// device copies of variables a, b & c
int size = sizeof(int);
// Allocate space for device copies of a, b, c
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
// Setup input values  
c = 0;
a = 3;
b = 5;
// Copy inputs to device
cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice);
  cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice);
// Launch add() kernel on GPU
add<<<1,1>>>(d_a, d_b, d_c);
// Copy result back to host
cudaError err = cudaMemcpy(&c, d_c, size, cudaMemcpyDeviceToHost);
  if(err!=cudaSuccess) {
      printf("CUDA error copying to Host: %s\n", cudaGetErrorString(err));
  }
printf("result is %d\n",c);
// Cleanup
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
return 0;
}

If all went well this code should output: result is 8\n.

Community Q&A

Question

Why print ("\n") always print \n character not "enter"?

Community Answer

\n is the newline character which is simply a line break.
Question

Does it work on Windows 10?

The logo Maker

Community Answer

Yes, it will. Be sure to have all required components used in the article.
Question

How to give command line argument at runtime?

Marrium Sharif

Community Answer

The Java command-line argument is an argument i.e. passed at the time of running the Java program. The arguments passed from the console can be received in the Java program and it can be used as an input. So, it provides a convenient way to check the behavior of the program for the different values. You can pass N (1,2,3 and so on) numbers of arguments from the command prompt.