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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Member since:2005-09-22
For what operations?
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.
it won't help much if you're processing a large data set as the PCIe bus will become the (very small) bottle-neck.
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.
How big and how fast is the the on-card memory on a C1060
4GB, 512-bit GDDR3, 800MHz, 102 GB/sec.
What programming models does the C1060 support?
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
Member since:2005-07-06
"For what operations?
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. " [/q]
I know, but I'd be interested in seeing bench marks of high-level operations (I.e. how fast can it reduce a matrix of n*n compared to a CPU?)
"it won't help much if you're processing a large data set as the PCIe bus will become the (very small) bottle-neck.
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 " [/q]
Yes, that's why I was interested in how much on-board memory it has.
"What programming models does the C1060 support?
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 " [/q]
Ah, so you can't take your existing Fortran and recompile chunks of it for the C1060?
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Member since:
2005-07-06
For what operations? How big and how fast is the the on-card memory on a C1060? What programming models does the C1060 support?
While I have no doubt it'll do vector math much faster than a general purpose CPU, it won't help much if you're processing a large data set as the PCIe bus will become the (very small) bottle-neck.
Edited 2008-11-19 22:44 UTC