Hacker News Reader

Comment by seanmcdirmid | Hacker News Reader

For deep learning applications, not general HPC (at least, from what I understand).

Is anyone actually using these and finding them better than GPUs?

NERSC's Cori machine is the biggest machine of Knight's Landing (10,000 nodes), 10th on TOP500. Don't know of any planned Knight's Mill machines.

Generally, Xeon Phis get fewer FLOPs than GPUs but are better at certain tasks, e.g. graph algorithms with lots of divergent control flow. Also it's possible to run pure MPI programs on them and get decent performance, and there's a lot of pure-MPI HPC software out there.

In terms of KNL deployments, LANL's Trinity system (#9 on June 2018) is slightly larger. Trinity has 9,984 KNL nodes, vs 9,688 in Cori.

Yep, I missed that one.

Both have AVX512 with, AFAIK, similar performance. The Knights Mill ones may have some extra extensions for lower precision floating point vector ops. The newer ones also have much better IO capabilities and virtualization.

Knights Mill has only half the double precision performance of KNL. So not really a successor, more heading off in a different direction.

I hate how the "Deep Learning optimized" actually means "you don't really need all that precision in the activation function of your neurons anyway".

Isn't that what all optimisation is about? Making trade-offs of one kind or another - usually ditching stuff you can do without for stuff you'd like more of.

I'd be perfectly fine with analog operations all the way to the final readout. Neurons are analog after all.

Intel has GHC (Haskell) running on these in-house if I recall correctly.

I think Phi is targeted at workloads like scientific fortran applications that no one could be bothered to redesign to take advantage of OCL/etc style programming models. They're 'better' than GPUs in that you can still use x86 and you're not stuck between the current choices: abusive-market-leader and low-quality-deliverables.

Reply on news.ycombinator.com