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

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)

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

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 (here)

We partner with our system vendors to provide optimal solutions that accelerate your workload. Buy now and enjoy all the benefits of GPU-acceleration on MATLAB.

Recommended Workstation Platforms
		HP Z800 For MATLAB customers seeking both maximum performance and sleek design, the HP Z800 supports two Tesla C2050 computing processors to bring supercomputing performance to their desk.
		Dell Precision T7500 Designed for system scalability and performance, the Dell Precision T7500 comes equipped with a single Tesla C2050 computing processor to meet your MATLAB computation needs.

Recommended Cluster Platforms
		Dell PowerEdge C410x PCIe Expansion Chassis Packed with the most computational power in a 3U form factor with 16 Tesla M2050 computing processors, the Dell PowerEdge C410x is a perfect platform to expand existing clusters for MATLAB users.

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