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Product Info
Additional Info
  • Software Development Tools

    Programming CUDA-enabled GPUs

  • NVIDIA CUDA
  • Webinars

    Learn more about GPU computing and CUDA-enabled applications through these webinars

  • Request Resource CD

    If you are new to GPU computing or would like to add to your knowledge of NVIDIA CUDA programming and Tesla hardware then this resource CD is for you.

MATLAB Acceleration on Tesla and Quadro GPUs

 
 
NVIDIA and MathWorks have collaborated to deliver the power of GPU computing for MATLAB users. Available with the latest release of MATLAB, NVIDIA GPU acceleration enables faster results for users of the Parallel Computing Toolbox and MATLAB Distributed Computing Server.

Parallel Computing Toolbox and MATLAB Distributed Computing Server enabled users to access the power of GPU computing with just a few changes to existing MATLAB code. It also enables users to call CUDA kernels directly from MATLAB.

Supported GPU capabilities in MATLAB:
> Manipulate data on NVIDIA GPUs
> Perform GPU accelerated MATLAB operations
> Integrate users’ own CUDA kernels into MATLAB applications
> Compute across multiple NVIDIA GPUs by running multiple MATLAB workers with Parallel Computing Toolbox on the desktop and MATLAB Distributed Computing Server on a compute cluster


Brief Overview of GPU Computing with MATLAB
Webinar: GPU Computing with MATLAB
Webinar: Accelerating Signal Processing and Communications Algorithms Using GPU Computing

The innovative technical development by MathWorks leverages NVIDIA’s feature-rich CUDA computing toolkit, helping to allow MathWorks to bring the benefits of GPU computing to the MATLAB community. MATLAB users can now easily enjoy the benefits of GPU computing from within MATLAB, without C/C++ or FORTRAN programming.


MATLAB Digest: GPU Programming in MATLAB
Relative Performance, A\b Benchmark (Matrix Left Division)
Recommended Professional Products
The powerful GPU computing capabilities in MATLAB were developed on Tesla and Quadro GPU computing products and require the use of recent CUDA-capable NVIDIA GPUs, such as NVIDIA Tesla 10-series or 20-series products supporting compute capability of 1.3 or above (learn more).

Tesla and Quadro GPU computing products are designed to deliver the highest computational performance with the most reliable numerical accuracy, and are available and supported by the world’s leading professional system manufacturers.

Find more GPU examples from the MATLAB community here

Tesla Benefits
Highest Computational Performance
> High-speed double precision operations
> Large dedicated memory
> High-speed bi-directional PCIe communication
> NVIDIA GPUDirect™ with InfiniBand
Most Reliable
> ECC memory
> Rigorous stress testing
Best Supported
> Professional support network
> OEM system integration
> Long-term product lifecycle
> 3 year warranty
> Cluster & system management tools
   (server products)
> Windows remote desktop support
 
Recommended Tesla & Quadro Configurations
High-End Workstation
> Two Tesla C2050 or C2070 GPUs
> Quadro 2000
> Two quad-core CPUs
> 24 GB system memory
Mid-Range Workstation
> Tesla C2050 or C2070 GPU
> Quadro 600
> Quad-core CPU
> 12 GB system memory
Entry Workstation
> Tesla C2050 or C2070 GPU
> Quadro 600
> Single quad-core CPU
> 6 GB system memory

NVIDIA Tesla and Quadro products are available from all major professional workstation OEMs. Only Tesla GPU computing products are designed and qualified for compute cluster deployment.

See MATLAB Parallel Computing Toolbox System Requirements.

Other Featured Partners and Resellers

For a complete list of Tesla Preferred Providers, click here.