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If you use KNL as an accelerator you got same problem as GPU. If you're using it in standalone mode then keep in mind that every part of code that is not using

by llukas 10y ago

If you use KNL as an accelerator you got same problem as GPU. If you're using it in standalone mode then keep in mind that every part of code that is not using AVX runs on slow Atom-like CPU. You have to vectorize code yourself or find a library that did that for you. Neither of above is close to "recompile good old X86 and run".

loeg 10y ago
Presumably even on good old X86 Xeon you cared about performance and figured out how to vectorize your code, so there's no change there.
CmdrSprinkles 10y ago
The argument is that you should be vectorizing your code anyway. You won't see as much of a boost on a skylake as you would on a KNL, but you are still going to see a pretty good boost. Obviously if your code is completely serial (and arguably bad), you have a lot of work ahead of you. Whereas, if you have been taking advantage of AVX already (or relying on libraries that do), you are basically just going to need to recompile. Are you going to get peak on a KNL? God no. But if it runs well on a "normal" processor, it is likely to run pretty well on the KNL. Which is a MUCH better foundation to start from than if you are trying to port a code to a GPU And those tweaks you make to better utilize that KNL? It will probably improve normal perf as well. It is obviously not as simple as the marketing led us to believe, but it never is. GPUs are a load of toss without a lot of work (and often some creative presentations). But the underlying principle of "Use the same code on both types of processor" does actually hold true as of KNL. As for performance numbers: Check the presentations from SC as they become available. Multiple outlets have begun to publish, or at least present, these numbers and their experiences using KNLs. I would personally argue that we still have a long way to go, but there is actually light at the end of the tunnel and it does look like there is an actual path now.

by llukas 10y ago

loeg 10y ago

Presumably even on good old X86 Xeon you cared about performance and figured out how to vectorize your code, so there's no change there.

CmdrSprinkles 10y ago

The argument is that you should be vectorizing your code anyway. You won't see as much of a boost on a skylake as you would on a KNL, but you are still going to see a pretty good boost. Obviously if your code is completely serial (and arguably bad), you have a lot of work ahead of you. Whereas, if you have been taking advantage of AVX already (or relying on libraries that do), you are basically just going to need to recompile. Are you going to get peak on a KNL? God no. But if it runs well on a "normal" processor, it is likely to run pretty well on the KNL. Which is a MUCH better foundation to start from than if you are trying to port a code to a GPU And those tweaks you make to better utilize that KNL? It will probably improve normal perf as well. It is obviously not as simple as the marketing led us to believe, but it never is. GPUs are a load of toss without a lot of work (and often some creative presentations). But the underlying principle of "Use the same code on both types of processor" does actually hold true as of KNL. As for performance numbers: Check the presentations from SC as they become available. Multiple outlets have begun to publish, or at least present, these numbers and their experiences using KNLs. I would personally argue that we still have a long way to go, but there is actually light at the end of the tunnel and it does look like there is an actual path now.