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Old 05-16-10, 10:14 PM   #1
Intel17
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Default What's the catch?

Okay, so help me understand this:

If a top end, $1000 Intel CPU can only do, say, 100 GFLOPS but a $500 nVidia or AMD graphics board can do >500 GFLOPS (in dp) and >1TFLOP (in sp), why is anything computationally expensive (compression, physics simulation, CADD, etc.) done on a CPU any more? What is the catch? Why haven't GPUs basically taken over with CPUs doing minimal work?
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Old 05-16-10, 11:21 PM   #2
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Default Re: What's the catch?

english? thanks
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Old 05-16-10, 11:38 PM   #3
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Default Re: What's the catch?

1. Not all problems can be parallelized to the degree of benefit from gpgpu computing
2. So far the language has been proprietary. CUDA, Stream, from nvidia and ATi respecitvely. OpenCL stands to change that.

Basically only a small subset of the computing problems can leverage this technology. The ones that can have been, but development is hard and slow because of the fact that no commercial developer, and most open-source coders for that matter, don't want to develop for a shifting target as far as language is concerned.

Highly parallel problem solving is really only good in.

Graphics
Physics simulation
Encryption (sometimes)
Molecular simulation (sometimes)

TLDR; It has little impact on programs used by consumers, so don't expect to see much anytime soon.
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Old 05-16-10, 11:39 PM   #4
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Default Re: What's the catch?

Because GPUs can't run general purpose code, essentially. To really explain it more than that would take longer than I want to type.
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