Linked by Amjith Ramanujam on Wed 19th Nov 2008 22:07 UTC, submitted by caffeine deprived
Nvidia and partners are offering new "personal supercomputers" for under $10,000. Nvidia, working with several partners, has developed the Tesla Personal Supercomputer, powered by a graphics processing unit based on Nvidia's Cuda parallel computing architecture. Computers using the Tesla C1060 GPU processor will have 250 times the processing power of a typical PC workstation, enabling researchers to run complicated simulations, experiments and number crunching without sharing a supercomputing cluster.
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2005-09-22
GPU units really shine in huge SIMD problems where you have a very large dataset and need to perform the same operation on each element. Examples would be simulations, visualization, medical imaging, etc.
While the PCIe bus is usually the limiting factor, it can be dealt with. Usually by transferring very large chunks of data over at once (hundreds of megabytes to several gigabytes), performing the computation on the GPU, and tranfering the results back, rinse, repeat. Even with the bandwidth limitations, the computational gains are so great, the end result is usually orders of magnitude faster.
4GB, 512-bit GDDR3, 800MHz, 102 GB/sec.
Since at it's heart it's just a GPU, the programming model is shader based. GLSL or HSL could both be used (the OpenGL and DirectX shading languages). However, NVidia's CUDA toolkit is also available (and the preferred method) which is essentially an extension to C designed with a kernel type processing model in mind (GPU kernel, not OS kernel).
Edited 2008-11-19 23:25 UTC