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I mean Intel is comparing their publicly unavailable product against NVIDIA's publicly available product. Now NVIDIA is replying with the benchmarks on their (a

by pavanky 10y ago

I mean Intel is comparing their publicly unavailable product against NVIDIA's publicly available product. Now NVIDIA is replying with the benchmarks on their (as of yet) publicly unavailable product. I think this blog post is fair game.

vegabook 10y ago
yet we should keep in mind that Intel makes its own chips, while Nvidia clear has an availability problem[1] which it cannot fix on its own. Therefore to the extent that Intel controls its own destiny better, we may find that to be a major factor in adoption rates for large installations. [1] http://semiaccurate.com/2016/08/01/nvidia-finally-shows-off-working-gp100s/ http://semiaccurate.com/2016/08/01/nvidia-finally-shows-off-...
scottlegrand 10y ago
Not even wrong. I have two PCs with 4 Titan X (Maxwell) GPUs and a third PC with 4 Titan X (Pascal) GPUs. Both of these systems are available today (I built them myself, total BOM about $7K), and both will destroy 4 Xeon Phi servers at Deep Learning. The benchmark Intel presented here is as disingenuous as their infamous white paper from 2010: http://pcl.intel-research.net/publications/isca319-lee.pdf http://pcl.intel-research.net/publications/isca319-lee.pdf In comparison, a single Knights Landing Xeon Phi will be ~$7K. I know where I put my money. Caveat Emptor. But Xeon Phi and I go way back here. They've been trying to beat my AMBER GPU code since 2013 or so. Many man years later I believe that a Knight's Corner is now ~35% faster than 2 Xeon CPUs with 1M atoms or more (source: http://adsabs.harvard.edu/abs/2016CoPhC.201...95N http://adsabs.harvard.edu/abs/2016CoPhC.201...95N) Meanwhile, the CUDA code has continued to scale with the GPU roadmap and a Titan XP is arguably 9-10x faster than 2 Xeon CPUs. No data is supplied at the low-end for Xeon Phi and I think we can safely assume it's because performance there sucks. (source: http://ambermd.org/gpus/benchmarks.htm http://ambermd.org/gpus/benchmarks.htm) Xeon Phi? IMO avoid avoid avoid until they start winning head to head 3rd party benchmarking fights like Soumith Chintala's fantastic convnet benchmark data: https://github.com/soumith/convnet-benchmarks https://github.com/soumith/convnet-benchmarks
- throwaway287391 10y ago
  Yes to all of this. I'm really surprised they didn't compare costs in this blog post. Ignore the DGX-1 row of their table; the really damning comparison is between the 2nd and 4th rows of the table. With a single 4x GPU server costing around $7k in total (row 4), you get nearly double the performance you get from spending $28k on four Xeon Phi servers (row 2). And that's assuming you've spent the time and disk replicating your data on all four of those Xeon Phi servers, or went to a likely relatively large amount of engineering effort to ensure that network IO doesn't bottleneck training.
- fnord123 10y ago
  Omnipath also sucks compared to Infiniband. How are they making so many inroads into HPC with these offerings? I mean, aside from their dominant-for-good-reason CPUs.
  - gnufx 10y ago
    Please share the data, particularly for the built-in interfaces on ~70 cores which are supposed to be available this year (given the blanket statement). Omni-path seems to be worrying Mellanox, judging by a recent visit.
- gnufx 10y ago
  So, a specific MD code may or may not work well with KNL -- we don't have data. KNL looks quite attractive for other chemistry, given all the vector units, large amount of fast memory, and ability to run realistically-sized examples without the network, or potentially the network-on-chip. We'll see how it pans out.
  - scottlegrand 10y ago
    I prefer to look at it the other way, why don't you point out an existing and important chemistry application where KNL bested its contemporary GPUs, say the best of Knight's Corner versus the best of Kepler (K40 or K80). I'm also open to Knight's Landing versus GP100 (vaporware versus no longer vaporware but hard to get) I'm genuinely interested here because I can't find this anywhere. I don't think it exists personally.

by pavanky 10y ago

vegabook 10y ago

yet we should keep in mind that Intel makes its own chips, while Nvidia clear has an availability problem[1] which it cannot fix on its own. Therefore to the extent that Intel controls its own destiny better, we may find that to be a major factor in adoption rates for large installations. [1] http://semiaccurate.com/2016/08/01/nvidia-finally-shows-off-working-gp100s/ http://semiaccurate.com/2016/08/01/nvidia-finally-shows-off-...

scottlegrand 10y ago

Not even wrong. I have two PCs with 4 Titan X (Maxwell) GPUs and a third PC with 4 Titan X (Pascal) GPUs. Both of these systems are available today (I built them myself, total BOM about $7K), and both will destroy 4 Xeon Phi servers at Deep Learning. The benchmark Intel presented here is as disingenuous as their infamous white paper from 2010: http://pcl.intel-research.net/publications/isca319-lee.pdf http://pcl.intel-research.net/publications/isca319-lee.pdf In comparison, a single Knights Landing Xeon Phi will be ~$7K. I know where I put my money. Caveat Emptor. But Xeon Phi and I go way back here. They've been trying to beat my AMBER GPU code since 2013 or so. Many man years later I believe that a Knight's Corner is now ~35% faster than 2 Xeon CPUs with 1M atoms or more (source: http://adsabs.harvard.edu/abs/2016CoPhC.201...95N http://adsabs.harvard.edu/abs/2016CoPhC.201...95N) Meanwhile, the CUDA code has continued to scale with the GPU roadmap and a Titan XP is arguably 9-10x faster than 2 Xeon CPUs. No data is supplied at the low-end for Xeon Phi and I think we can safely assume it's because performance there sucks. (source: http://ambermd.org/gpus/benchmarks.htm http://ambermd.org/gpus/benchmarks.htm) Xeon Phi? IMO avoid avoid avoid until they start winning head to head 3rd party benchmarking fights like Soumith Chintala's fantastic convnet benchmark data: https://github.com/soumith/convnet-benchmarks https://github.com/soumith/convnet-benchmarks

throwaway287391 10y ago
Yes to all of this. I'm really surprised they didn't compare costs in this blog post. Ignore the DGX-1 row of their table; the really damning comparison is between the 2nd and 4th rows of the table. With a single 4x GPU server costing around $7k in total (row 4), you get nearly double the performance you get from spending $28k on four Xeon Phi servers (row 2). And that's assuming you've spent the time and disk replicating your data on all four of those Xeon Phi servers, or went to a likely relatively large amount of engineering effort to ensure that network IO doesn't bottleneck training.
fnord123 10y ago
Omnipath also sucks compared to Infiniband. How are they making so many inroads into HPC with these offerings? I mean, aside from their dominant-for-good-reason CPUs.
- gnufx 10y ago
  Please share the data, particularly for the built-in interfaces on ~70 cores which are supposed to be available this year (given the blanket statement). Omni-path seems to be worrying Mellanox, judging by a recent visit.
gnufx 10y ago
So, a specific MD code may or may not work well with KNL -- we don't have data. KNL looks quite attractive for other chemistry, given all the vector units, large amount of fast memory, and ability to run realistically-sized examples without the network, or potentially the network-on-chip. We'll see how it pans out.
- scottlegrand 10y ago
  I prefer to look at it the other way, why don't you point out an existing and important chemistry application where KNL bested its contemporary GPUs, say the best of Knight's Corner versus the best of Kepler (K40 or K80). I'm also open to Knight's Landing versus GP100 (vaporware versus no longer vaporware but hard to get) I'm genuinely interested here because I can't find this anywhere. I don't think it exists personally.