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0 pointsquickben9y ago0 comments

GPUs aren't panacea. Such generalizations are wrong and will have you rearchitecture approaches after GPU io bottlenecks.

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valarauca19y ago

>GPUs aren't panacea. Such generalizations are wrong

This is true. But if you are doing hard number crunching you are using a GPU once you exhaust what a CPU can do. And most the time before you even touch SIMD as it's only a 4-8x speed up, while a GPU is 1000-10,000x.

>will have you rearchitecture approaches after GPU io bottlenecks.

90% of these are caused by bad software. Either using legacy API's. Or by writing code that forces GPU's to talk to processor more often then is necessary.

PCIe bandwidth is ~7.88GB/s [1] on modern Intel Chips (post 3xxx series). Compute GPU's often offer >10GB of on-board RAM. With last generations flag ships over a staggering 32GB of on-board RAM.

And if you are hitting a wall with GPU compute/IO limits, CPU SIMD instructions aren't going to help you in the slightest.

[1] http://www.tested.com/tech/457440-theoretical-vs-actual-band...

tbirdz9y ago

It's not the bandwidth that's the problem, it's the latency. Many problems need more control flow and branching, and that is better done on the CPU. If you need to make decisions and take the previous iterations output as an input, then the overhead of transferring the data back and forth from cpu to gpu outweighs the benefit of the gpu speed.

GPUs are good if you have an independent data parallel algorithm you are using to transform large blocks of floating point data. For other uses, CPU is better.

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