Matlab supports Nvidia GPUs via Paralell Computing Toolbox

The MathWorks has announced support for Nvidia GPUs in Matlab applications using Parallel Computing Toolbox or Matlab Distributed Computing Server. This support enables engineers and scientists to increase the speed of many of their Matlab computations without performing low-level programming.

Now more engineers and scientists can take advantage of Nvidia's Cuda-enabled GPUs, including the latest Tesla 20-series GPUs, based on the Fermi architecture, all from within Matlab. Parallel Computing Toolbox users can access the Nvidia Cuda library without having to learn Cuda programming or significantly modify their applications.

Originally designed for graphics rendering in the image-intensive video gaming industry, GPUs have evolved in recent years to become more general purpose. Researchers can program them to execute the computations and sophisticated graphical effects needed for data analysis, data visualisation, and applications such as financial modelling and biological modelling.


For functionality and security for externalised research, software providers have turned to the cloud, writes Sophia Ktori


Robert Roe investigates the growth in cloud technology which is being driven by scientific, engineering and HPC workflows through application specific hardware


Robert Roe learns that the NASA advanced supercomputing division (NAS) is optimising energy efficiency and water usage to maximise the facility’s potential to deliver computing services to its user community


Robert Roe investigates the use of technologies in HPC that could help shape the design of future supercomputers