Beyond the valley of the CPU
By Simon Bisson & Mary Branscombe in Editorial
Posted in Processors, Software, Applications, Server, Mobile on
(or “The return of the co-processor”)
The white heat of technology in the 1980s was focussed on the BBC Micro. Not only was it the heftiest 8-bit machines around, its open bus made it possible to add more processing power. With everything from music machines to Z-80s running CP/M, the BBC Micro could share its keyboard with many different CPUs.
Those days are on their way back.
Last week Toshiba announced a new range of consumer notebook PCs. Like many of Toshiba’s systems they’re designed to be media players, and in a side swipe at BluRay, they now come with an upscaling DVD drive. That’s where the coprocessor magic comes in, as Toshiba is using a derivative of the same Cell processor in Sony’s PS3 to drive its imaging software. A quad core version of the Cell sits alongside a dual core intel processor, and it’s used to handle a range of processor intensive tasks - acting as a feed to the GPU that drives the screen. Not only does it upscale DVD streams (very impressively) it also can be used to handle file transcoding (so your movies end up on your iPhone that much quicker), and also works well as a way of quickly indexing images and video.
Focused on video, Toshiba’s co-processor is also taking advantage of bundled web cams for a limited form of gesture control. Stopping a film by holding up a hand is effective, as is using a clenched fist as an in air mouse. Bill Gates’ departure reaffirmed his belief in alternative user infterfaces, and this is one approach to delivering those new ways of working.
Co-processors aren’t just for flashy graphics. Back in the 1990s I was writing mathematical simulation software, and at one point I had some electro-thermal models running on one of the MOD’s Crays. It wasn’t just any old Cray - it also had a co-processor in the shape of an additional vector processing unit. That vector co-processor made short shrift of my arrays of partial differential equations. Its direct descendent is a lot closer than an MOD research facility.
In fact, if you’ve got an NVIDIA graphics card it’s right in your PC’s GPU.
Back in January we wrote about Tesla and CUDA, and NVIDIA updated us on the next generation of the Tesla hardware earlier this week. The new G10 Tesla systems are looking very impressive, and the CUDA parallel programming language extensions are now able to work with standard multicore PCs as well as NVIDIA’s GPUs.
Memory is important when you’re using co-processors, and you need a lot if you’re signal-processing seismic data. Tesla will now support 4GB of directly attached memory per GPU, so a quad-GPU system can work with 16GB of data at a time. The numbers look good - and using Folding at Home a single Tesla 10 comes in at more than 40 times faster than a standard CPU, and more than 6 times faster than a PS3. Other demonstrations showed significant savings in space and in cost - one finance house has reduced its annual costs 9 times, replacing a 600 CPU options valuation system with a handful of front-end CPUs and 12 Tesla GPUs.
Of course with Snow Leopard around the corner, one of the obvious questions was about Apple’s support for OpenCL. It turns out that CUDA is best thought of as a personality layer on top of NVIDIA’s parallel thread execution (PTX) hardware, and it produces device-specific assembly code. There’s no reason why other GPU programming environments can’t produce the same PTX code - but CUDA will remain NVIDIA’s own route to the GPU as a processing tool, and it will be adding support for additional languages beyond C and C++ with Fortran just around the corner.
The future of the co-processor seems assured, for now at least. It’s time for software companies to start taking notice and to deliver on the promise of additional power beyond the CPU.
–Simon
Rated: 80% (1 votes)
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