I'm a bit surprised that Nvidia mentioned nothing about performance per watt in their reply.
Correcting Intel's Deep Learning Benchmark Mistakes
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Re: Correcting Intel's Deep Learning Benchmark Mistakes
#12I'm a bit surprised that Nvidia mentioned nothing about performance per watt in their reply.
Or that they didn't mention cost for performance. They equated 4 Xeon Phi servers to ONE DGX-1. The DGX has a 140k price tag.
Re: Correcting Intel's Deep Learning Benchmark Mistakes
#13Ok, its officially the new new thing when corporate communications types are sniping at each other :-) Once we start seeing press releases of products that are not available yet being shown to kill the existing competition we'll know that the hype train has officially gone super-product-sonic (that is a hype wave travelling faster than the product releases can support it).
I think both Knights Whatever and DGX-1 are already there. They seem to be in "order now, receive sometime" mode.
Re: Correcting Intel's Deep Learning Benchmark Mistakes
#14@imaleppert Agree getting the details on the testing would be helpful. I think NV was more pointing out that Intel were putting their latest against NV's oldest. It'd be like testing a RX480 against a GTX660. What's the use of that? @modeless the new Azure instances have M60's or you can purchase a 1080 or new TitanX which are both available (although stock has been tight). https://azure.microsoft.com/en-us/blog/azur…
Yeah but Azure is also introducing new K80 instances (based on four-year-old Kepler) becuase the M60 is worse in memory capacity and memory bandwidth, and not much better in FLOPS. NVIDIA is just not putting their best hardware out there for cloud providers to use.
Sure I can buy a Titan X for myself (already have), but I can't rent a hundred, or even one, on EC2 or Azure or GCE. And I can't get a P100 yet at all. I don't want to hear NVIDIA claiming unfair benchmarking and citing P100 numbers until P100s are actually available either to buy (and ship immediately) or in the cloud.
Re: Correcting Intel's Deep Learning Benchmark Mistakes
#15I'm a bit surprised that Nvidia mentioned nothing about performance per watt in their reply.
That usually implies something pretty specific, like maybe that's the only terms on which they're competitive?
Re: Correcting Intel's Deep Learning Benchmark Mistakes
#16Why don't they provide a link to their testing methodology? They need to back up their claims (on both sides) with the actual configuration, all versions, and sample datasets for people to independently verify. A docker container that runs their performance suite would be ideal.
Except that Docker containers play terribly with virtualization solutions. Still, some sort of configuration/infrastructure-as-code would go a long way.
Re: Correcting Intel's Deep Learning Benchmark Mistakes
#17Why don't they provide a link to their testing methodology? They need to back up their claims (on both sides) with the actual configuration, all versions, and sample datasets for people to independently verify. A docker container that runs their performance suite would be ideal.
Except that Docker containers play terribly with virtualization solutions. Still, some sort of configuration/infrastructure-as-code would go a long way.
What about NVIDIA-docker?
Re: Correcting Intel's Deep Learning Benchmark Mistakes
#18Ok, its officially the new new thing when corporate communications types are sniping at each other :-) Once we start seeing press releases of products that are not available yet being shown to kill the existing competition we'll know that the hype train has officially gone super-product-sonic (that is a hype wave travelling faster than the product releases can support it).
I think both Knights Whatever and DGX-1 are already there. They seem to be in "order now, receive sometime" mode.
Xeon Phi is in 23 of the Top500 supercomputer list, so it's not like they're not shipping. The next version, Knight's Landing, should be launching soon, and hopefully will have better availability.
Re: Correcting Intel's Deep Learning Benchmark Mistakes
#19I'm a bit surprised that Nvidia mentioned nothing about performance per watt in their reply.
Or that they didn't mention cost for performance. They equated 4 Xeon Phi servers to ONE DGX-1. The DGX has a 140k price tag.
That was for performance scaling, not raw performance. Although 140k probably includes a nice interconnect.
Re: Correcting Intel's Deep Learning Benchmark Mistakes
#20> Titan uses four-year-old GPUs ... as does nearly every public cloud provider. I agree with most of the article, but you can't fault Intel for benchmarking the hardware that cloud providers are actually offering. I'm not sure what exactly NVIDIA is doing with their Tesla product line but whatever it is, it's really restricting the availability of recent GPU hardware. Even Azure's GPU instances released this month ar…
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.
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-...