cuda Getting started with cuda Prerequisites


Example

To get started programming with CUDA, download and install the CUDA Toolkit and developer driver. The toolkit includes nvcc, the NVIDIA CUDA Compiler, and other software necessary to develop CUDA applications. The driver ensures that GPU programs run correctly on CUDA-capable hardware, which you'll also need.

You can confirm that the CUDA Toolkit is correctly installed on your machine by running nvcc --version from a command line. For example, on a Linux machine,

$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Tue_Jul_12_18:28:38_CDT_2016
Cuda compilation tools, release 8.0, V8.0.32

outputs the compiler information. If the previous command was not successful, then the CUDA Toolkit is likely not installed, or the path to nvcc (C:\CUDA\bin on Windows machines, /usr/local/cuda/bin on POSIX OSes) is not part of your PATH environment variable.

Additionally, you'll also need a host compiler which works with nvcc to compile and build CUDA programs. On Windows, this is cl.exe, the Microsoft compiler, which ships with Microsoft Visual Studio. On POSIX OSes, other compilers are available, including gcc or g++. The official CUDA Quick Start Guide can tell you which compiler versions are supported on your particular platform.

To make sure everything is set up correctly, let's compile and run a trivial CUDA program to ensure all the tools work together correctly.

__global__ void foo() {}

int main()
{
  foo<<<1,1>>>();

  cudaDeviceSynchronize();
  printf("CUDA error: %s\n", cudaGetErrorString(cudaGetLastError()));

  return 0;
}

To compile this program, copy it to a file called test.cu and compile it from the command line. For example, on a Linux system, the following should work:

$ nvcc test.cu -o test
$ ./test
CUDA error: no error

If the program succeeds without error, then let's start coding!