Pervasive Parallelism Lab Exploits the Capabilities of Parallel Computing
NVIDIA to Sponsor New Stanford Parallel Computing Research Lab
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SANTA CLARA, CA—APRIL 30, 2008—NVIDIA Corporation has announced that it is a founding member of Stanford University’s new Pervasive Parallelism Lab (PPL). 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.
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 significant success to date with its Tesla™ line of GPU computing hardware solutions and, more importantly, with CUDA™ technology, its award-winning programming environment that gives developers access to the massively parallel architecture of the GPU through the industry-standard C language.
“Parallel programming is perhaps the largest problem in computer science today and is the major obstacle to the continued scaling of computing performance that has fueled the computing industry, and several related industries, for the last 40 years,” says Bill Dally, chair of the computer science department at Stanford.
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.
“NVIDIA has been tackling parallel computing challenges since its founding and, as a result, the GPU has evolved into an incredibly powerful processor, capable of running thousands upon thousands of concurrent operations,” said David Kirk, chief scientist at NVIDIA. “We applaud, and are proud to be a part of, Stanford University’s formation of the PPL and its mission to push the software industry to expose the inherent parallelism in today’s computers.”
NVIDIA GPU technology, combined with the CUDA programming environment have delivered speed increases anywhere from 8× to 50× over conventional processing technologies. Some examples include:
NVIDIA joins with AMD, Hewlett Packard, IBM, Intel, and Sun Microsystems in this venture.
For more information on NVIDIA GPU Computing solutions, please visit www.nvidia.com/tesla.
NVIDIA (Nasdaq: NVDA) is the world leader in visual computing technologies and the inventor of the GPU, a high-performance processor which generates breathtaking, interactive graphics on workstations, personal computers, game consoles, and mobile devices. NVIDIA serves the entertainment and consumer market with its GeForce® graphics products, the professional design and visualization market with its Quadro® graphics products, and the high-performance computing market with its Tesla computing solutions products. NVIDIA is headquartered in Santa Clara, Calif. and has offices throughout Asia, Europe, and the Americas. For more information, visit www.nvidia.com.
Certain statements in this release including, but not limited to, statements as to: the benefits, purpose and mission of the PPL; the benefits, performance, capabilities and uses of Tesla, CUDA and GPUs; new software technology; the computing industry; and our strategy are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: development of competitive technologies to increase the speed of computing; issues associated with design of technology and products; unanticipated changes in industry standards and interfaces; slower than anticipated adoption of a new industry standard or interface; manufacturing or software defects; the impact of technological development and competition; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission including its Form 10-K for the period ended January 27, 2008. Copies of reports filed with the SEC are posted on our website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.
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