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GREATTerry
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re: GPU technology key to exascale says Nvidia
GREATTerry   11/16/2011 4:04:44 PM
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Using GPU as an accelerator seems interesting. Is there any simpler way to make a dedicated accelerator so that a much "simpler" architecture can be used and hopefully better energy efficiency?

markhahn0
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re: GPU technology key to exascale says Nvidia
markhahn0   11/21/2011 6:57:32 PM
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Nvidia has a compute model to push. of course they'll claim it's better than the alternatives, but is there any reason we have to keep seeing these claims parotted in the media without support? the GPU model is inherently quite data-parallel. it's ideal for certain kinds of computation, but clearly not all. what's the amount of independent computation a thread performs before it needs to interact with other threads, or to operate on general-purpose memory? if threads are basically lockstep and operating on their own state, you will be happy with GPUs. if not - if your dataflow is more complicated, if your computation is more conditional, if your problem is so large it needs many machines, then GPUs simply won't suit, and you'll use MPI. whether the MPI is on BG-Q, the K machine or Intel MIC doesn't matter that much. there is no future in which GPUs totally win, since what defines them is their restrictive data/compute model. an interesting question is whether such a restrictive model is necessary to achieve power-efficiency (and MIC is nothing less than Intel's bet against that proposition.)



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As data rates begin to move beyond 25 Gbps channels, new problems arise. Getting to 50 Gbps channels might not be possible with the traditional NRZ (2-level) signaling. PAM4 lets data rates double with only a small increase in channel bandwidth by sending two bits per symbol. But, it brings new measurement and analysis problems. Signal integrity sage Ransom Stephens will explain how PAM4 differs from NRZ and what to expect in design, measurement, and signal analysis.

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