GPU Applications

MATLAB Acceleration on NVIDIA Tesla and Quadro GPUs

MATLAB® is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models in a variety of application areas such as image and video processing, signal processing and communications, computational finance, and computational biology.

With Parallel Computing Toolbox, MATLAB users can take advantage of the NVIDIA's GPU computing technology without having to learn low-level GPU computing libraries. Key features include:

Learn more about GPU computing with MATLAB.


Technical article: GPU Programming with MATLAB


In addition to using MATLAB to develop GPU accelerated applications and models, it can also be used by CUDA programmers to prototype algorithms and incrementally develop and test CUDA kernels. MATLAB can be used to:

  • Write prototype code to explore algorithms before implementing them in CUDA
  • Quickly evaluate kernels for different input data
  • Analyze and visualize kernel results
  • Write test harnesses to validate that kernels are working correctly

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

Technical Reports on CUDA for Bioinformatics
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
Other Relevant Software using CUDA
High-End Workstation
> Two Tesla K20 GPUs
> Quadro K4000
> Two quad-core CPUs
> 24 GB system memory
Mid-Range Workstation
> Tesla K20 GPU
> Quadro K2000
> Quad-core CPU
> 12 GB system memory
Entry Workstation
> Tesla K20 GPU
> Quadro K600
> 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 (here)

Buy Optimized Tesla Systems

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.

Other Featured Partners and Resellers

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