NVIDIA to Sponsor Stanford Parallel Computing Research Lab

Formed to develop new techniques, tools, and training materials to allow software engineers to harness the parallelism of the multiple processors in every new computer.

by DE Editors | Published May 6, 2008

NVIDIA Corporation (Santa Clara, CA) announced that it is a founding member of Stanford University's new Pervasive Parallelism Lab (PPL), formed to exploit the capabilities of parallel computing. NVIDIA joins with AMD, HP, IBM, Intel, and Sun Microsystems in this venture.

The PPL will develop new techniques, tools, and training materials to allow software engineers to harness the parallelism of the multiple processors that are already available in virtually every new computer, according to the release.

NVIDIA's investment complements the company's ongoing strategy to solve some of the world's most computationally intensive problems with its market-leading GPUs and world-class tools and software. The company has enjoyed success to date with its Tesla line http://www.nvidia.com/tesla of GPU computing hardware solutions and, more importantly, with CUDAT technology, its programming environment that gives developers access to the massively parallel architecture of the GPU through the industry-standard C language.

Until recently, computer installations delivering massive parallelism could only be deployed in large-scale computer centers with hundreds to thousands of separate computer systems. With the recent introduction of many-core processors such as the GPU and the multi-core CPU, most new computer systems come equipped with multiple processors that require new software techniques to exploit parallelism. Without new software techniques, computer scientists are concerned that rapid increases in the speed of computing could stall.

From fundamental hardware to new user-friendly programming languages that will allow developers to exploit parallelism automatically, the PPL will allow programmers to implement their algorithms in accessible, domain-specific languages while at deeper, more fundamental levels of software, the system would do all the work for them in optimizing the code for parallel processing.

To read six examples of how NVIDIA GPU technology, combined with the CUDA programming environment, has delivered speed increases anywhere from 8 to 50 times over conventional processing technologies, go to NVIDIA.

Sources: Press materials received from the company and additional information gleaned from the company's website.

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