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GPU Programming
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Get Started - Parallel Computing

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

 

Download The CUDA Toolkit

This release enables
development using GPUs using
Kepler architecture.
 

GPU-Accelerated Libraries

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

Library:

 
 

Linear Algebra:

 
 

Signal & Image Processing:

 
 

More GPU-Accelerated Libaries >

GPU Directives

Automatically parallelize loops in your Fortran or C code using OpenACC directives.

OpenACC Directives

OpenACC Directives
  • Easy: simply insert hints in your codebase.
  • Open: run on either CPU or GPU.
  • Powerful: tap into the power of GPUs within minutes.
 
 

Register for a 30-day free trial license >

Programming Languages

Develop your own parallel applications and libraries using a programming language you already know.

CUDA C/C++

CUDA C/C++
GPU Acceleration for C and C++ Apps
 
 

CUDA Fortran

CUDA Fortran
GPU Acceleration for Fortran Applications
 
 

Get Started With:

  • Python
  • .NET
  • Others
 
 

More Programming Language Solutions >

 
 
 
 
CUDA and GPU Computing

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