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Nvidia blows out Moore’s Law with fresh Tesla

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To fully appreciate the target market of Tesla systems, one need only look at the performance comparisons provided by Nvidia between the Tesla 8 and 10 systems. On an algorithm having to do with Dynamics of Black Holes, for example, the new unit runs four times faster than its predecessor and a whopping 84 times faster than a CPU. And, if you’re into Cholesky Factorization, then the Tesla 10 unit shows much more dramatic scaling than the Tesla 8 units while also coming close to quadrupling performance.

Now, Nvidia CEO Jen-Hsun Huang might have said that the processor is dead, but the company really seems to see regular CPUs living alongside these GPGPU systems. Nvidia talked to us an awful lot about heterogeneous computing where the CPUs handle some tasks, and GPUs take on those specialized, parallel tasks that can map well onto the weird silicon.

And it’s that mapping that is really the heart of the matter around GPGPUs.

The knock on all of the major accelerator options, including GPGPUs, FPGAs, Cell chips and specialized silicon from a company like ClearSpeed, is that they’re too funky for use by many applications or developers.

Nvidia does its best to counter the software skeptics with CUDA – a development environment based on C that helps push certain jobs onto GPGPUs.

During a recent all day meeting at Nvidia’s headquarters in Santa Clara, the company rolled out a number of customers who have faced CUDA and won. These folks did stuff like plasma and radiation modeling and oil and gas exploration. The consensus seemed to be that it takes about a month to learn the CUDA nuances and tweak code for the GPGPUs.

Moving forward, Nvidia plans to invest in Fortran, C++, GPU cluster, profiler and debugger aspects around CUDA.

It must be said that the market for Tesla and other similar accelerators appears quite limited for the foreseeable future. No matter how easy the hardware guys make it sound, coding for these things requires some software savvy, and only parts of applications will lend themselves to the accelerators.

That’s why you hear Intel banging on about Larrabee – the x86-based, many-core, GPU-like product it’s meant to ship in 2010. Intel claims that its compiler will do a lot of the dirty work, pushing the right bits of code onto the accelerator. In addition, developers start out in familiar territory with x86 instructions.

But, er, Larrabee is just slideware for the moment, and it’s hard to win developers’ hearts and minds even if you give the best slide.

So, for the time being, it’s Nvidia marching on and telling a pretty decent story. The application boosts shown with Tesla blow out anything you could expect to receive from Moore’s Law and advancing CPUs. Rather tellingly, it’s not easy to buy Tesla systems from the usual server top dogs. HP has some kind of buddy relationship with Nvidia around the boxes, and start-ups like Acceleware will offer the gear with their software. Things get a bit trickier after that. There’s more information here. ®

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