Nvidia to offer parallel tech for mobile devices

Chip vendor Nvidia Corp. plans to use its Cuda parallel computing architecture in all its graphics processing units (GPU), including its Tegra system on a chip for mobile devices.

Nvidia's Cuda is a C language environment that enables developers to write software to solve complex computational problems by tapping into the multicore parallel processing power of GPUs, according to the company.

However, the first version of Tegra, scheduled to ship by the middle of next year, will not include Cuda, said Jen-Hsun Huang, co-founder, president and chief executive officer of Nvidia, in an interview on Wednesday.

Cuda is part of Nvidia's strategy to position its GPUs as general-purpose, parallel computing processors that can be used in a variety of scientific and commercial applications such as financial computing, Huang said. Nvidia's wares have traditionally been strong in high-end graphics and gaming.

"We believe that a GPU is not just for graphics anymore and can be really used for anything that involves a lot of data and mathematics," Huang added.

Nvidia on Tuesday announced a GPU-based Tesla Personal Supercomputer, which it said uses its Tesla GPUs and Cuda to deliver the power of a cluster of computers at a fraction of the cost, in the form of a standard desktop workstation. Among the computer makers offering Tesla Personal Supercomputers are Dell, Lenovo, Asustek and Western Scientific.

There is a new computer architecture emerging, and it is based on GPUs -- and other types of parallel processors -- and traditional CPUs working together, Huang said. "The CPU is excellent for sequential processing, but there are many types of problems that you can operate on in parallel," he added.

GPUs offer higher performance than CPUs because they integrate hundreds of processors, according to Huang. For example, the Tesla Personal Supercomputer has 240 processors running in parallel, he added.

The first to realize the importance of a "heterogeneous architecture" were gamers who realized that with a CPU and a GPU, their video games and 3-D graphics would run much better, Huang said.

The GPU in its new positioning is not, however, seen by Nvidia as an alternative to CPUs. "We are not trying to replace the CPU, as we believe it is necessary," Huang said.

Nvidia is working with application developers to port their software to the Cuda architecture, Huang said. The ability to program in C language will ensure that sophisticated users such as researchers can write the programs themselves for the new supercomputer, he added.

Copyright © 2008 IDG Communications, Inc.

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