CPU Cards for Parallel Computation?

Question

I remember reading some time ago that there were cpu cards for systems to add additional processing power to do mass parallelization. Anyone have any experience on this and any resources to get looking into the hardware and software aspects of the project? Is this technology inferior to a traditional cluster? Is it more power conscious?

Answer 1

Have you looked at the various GPU Computing options. Nvidia (and probably others) are offering personal supercomputers based around utilising the power of graphics cards.

Answer 2

There are two cool options. one is the use of GPU's as Mitch mentions. The other is to get a PS/3, which has a multicore Cell processor.

You can also set up multiple inexpensive motherboard PCs and run Linux and Beowulf.

Answer 3

GPGPU is probably the most practical option for an enthusiast. However, DSPs are another option, such as those made by Texas Instruments, Freescale, Analog Devices, and NXP Semiconductors. Granted, most of those are probably targeted more towards industrial users, but you might look into the Storm-1 line of DSPs, some of which are supposed to go for as low as $60 a piece.

Another option for data parallelism are Physics Processing Units like the Nvidia (formerly Ageia) PhysX. The most obvious use of these coprocessors are for games, but they're also used for scientific modeling, cryptography, and other vector processing applications.

ClearSpeed Attached Processors are another possibility. These are basically SIMD co-processors designed for HPC applications, so they might be out of your price range, but I'm just guessing here.

All of these suggestions are based around data parallelism since I think that's the area with the most untapped potential. A lot of currently CPU-intensive applications could be performed much faster at much lower clock rates (and using less power) by simply taking advantage of vector processing and more specialized SIMD instruction sets.

Really, most computer users don't need more than an Intel Atom processor for the majority of their casual computing needs: e-mail, browsing the web, and playing music/video. And for the other 10% of computing tasks that actually do require lots of processing power, a general-purpose scalar processor typically isn't the best tool for the job anyway.

Even most people who do have serious processing needs only need it for a narrow range of applications; a physicist doesn't need a PC capable of playing the latest FPS; a sound engineer doesn't need to do scientific modeling or perform statistical analysis; and a graphic designer doesn't need to do digital signal processing. Domain-specific vector processors with highly specialized instruction sets (like modern GPUs for gaming) would be able to handle these tasks much more efficiently than a high power general-purpose CPU.

Cluster computing is no doubt very useful for a lot of high end industrial applications like nuclear research, but I think vector processing has much more practical uses for the average person.

Answer 4

Mercury Computing offers a Cell Accelerator Board, it's a PCIe card that has a Cell processor, and runs Yellow Dog Linux, or Mercury's flavor of YDL. Fixstars offers a more powerful Cell PCIe board called the GigaAccel. I called up Mercury, they said their board is about $5000 USD, without software. I'd guess the GigaAccel is up to twice as expensive.

I found one of the Mercury boards used, but it didn't come with a power cable, so I haven't been able to use it yet, sadly.

Answer 5

OpenCL - is an industry wide standard for doing HPC computing across different vendors and processor types, single-core, multi-core, graphics cards, cell, etc... see http://en.wikipedia.org/wiki/OpenCL.

The idea is that using a simple code base you can use all spare processing capacity on the machine regardless of type of processor.

Apple has implemented this standard in its next version Mac OS X. There will also be offerings from nVIDIA, ATI, Intel etc.