Surprise, Surprise: Nvidia Pushes GPU Computing

Nvidia had its big boy pants on Tuesday, dedicating the entirety of an on-campus Technology Editor's Day to parallel programming on graphics processors, new hardware optimized for same and the growing number of software developers downloading the graphics chip maker's CUDA developer kit to leverage GPU multi-thread capabilities for applications in such fields as finance, medical imaging and particle accelerator modeling.

For a company generally associated with fragging pixilated super soldiers, Santa Clara, Calif.-based Nvidia wanted to talk about anything but at the event, instead trotting out a collection of parallel computing partners as far removed from gaming enthusiasts as is possible while still focusing on graphics-optimized silicon's real-world uses.

Which isn't to say that Nvidia's reputation with the gaming set doesn't have its benefits. Amitabh Varshney, a professor of computer science at the University of Maryland, was on hand Tuesday to testify that the chip maker's hipness quotient is a great recruiting tool for attracting young programmers to parallel computing.

"There's a coolness factor around GPUs," Varshney said during his presentation at Tuesday's event. "We see students get into computer programming because they want to develop games. And then CUDA draws them in to other sorts of programming for other applications. It's sort of like a bait-and-switch."

Nvidia partners like Tech-X's Peter Messmer, VP of the company's Space Applications Group, described how parallel computing and the CUDA (Compute Unified Device Architecture) programming language has helped to accelerate data analysis for particle acceleration modeling applications. It turns out that graphics chips, designed to produce and run multiple single-function threads, are much better at certain computing processes than CPUs.

That's not exactly news, of course. But according to Nvidia's Andy Keane, GM of the GPU Computing business unit, and CUDA developer Ian Buck, what is news is that a great many more types of computing than just graphics can be performed more efficiently on GPUs.

As a company that only makes graphics processors and chipsets, it's obviously in Nvidia's interest to play up the uses of the GPU. To the chip maker's credit, though, Nvidia has invested a good deal of time and money in developing its CUDA language and developer kit, as well as partnering with the likes of Tech-X to expand its reach.

CUDA is now in every graphics driver Nvidia offers and free downloads of the CUDA developer kit have been ramping steadily each month since the initial release of the beta by Nvidia last year. The chip maker is also developing a Fortran compiler for GPU computing, according to Buck, who added that numerics libraries and a C compiler are available now.

"The mantra with CUDA and its design was to start with C. We are working with Fortran as well because the HPC space is heavily invested in Fortran," Buck said. "C++ is something a lot of ISVs are interested in. CUDA is primarily C today, but we've pulled in some C++ features and that is a priority for us."

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