Learn Parallel Programming | Developing with GPUs

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PARALLEL PROGRAMMING

Get started quickly with parallel programming using the solution that best meets your needs. Your options include simply dropping in a GPU-accelerated library, adding a few OpenACC Directives in your code, or designing your own parallel algorithms. You can even combine these approaches to accelerate your applications:

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GPU-Accelerated Libraries

Drop in a GPU-accelerated library to replace MKL, IPP, FFTW and other widely-used libraries.

Library

Thrust C++ Template

Linear Algebra


cuBLAS	cuSPARSE

Signal & Image Processing


NPP	cuFFT

See all GPU-Accelerated Libraries

OpenACC Directives

Simplest way to portable computing with accelerators using OpenACC directives.

OpenACC Directives for Accelerators
OpenACC

> Open: Future-proof your codes with this
   open standard
> Simple: Easy, high-level, compiler driven
   approach to parallel computing
> Portable: Ideal for accelerating legacy
   Fortran or C codes