Awesome collaborative effort across many contributors and companies! The boost is impressive and for MXNet users to get this boost "out of the box" is a great benefit and makes MXNet an even better choice.
Alex - can you clarify whether there are any down sides with regards to noon AVX-512 architectures, AMD CPUs, etc? Will it gracefully fallback? Hagay On Fri, Oct 19, 2018, 15:46 Sergio Fernández <wik...@apache.org> wrote: > If there is no downside on platforms not supporting AVX512 instructions, > then +1 > > > On Wed, Oct 17, 2018, 14:10 Alex Zai <aza...@gmail.com> wrote: > > > Hey all, > > We have been working hard these past few months to integrate and > stabilize > > Intel’s MKLDNN deep learning CPU accelerator into Mxnet and have made > > incredible progress. On CPUs with AVX512 instructions (such as c5.18x) we > > have seen performance increase up to 12x and on other platforms (Macs, > > AVX2) we seen a speedup of 1.5+. Full list of benchmarks can be found > here > > ( > > > https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=95650764 > > and https://github.com/apache/incubator-mxnet/pull/12591). > > > > Currently, using this accelerator requires the developer to either pip > > install the mxnet-mkl version of mxnet or to build it themselves from > > source. Given that we should try to provide the best performance "out of > > the box” with mxnet we should include this in the default build. The > mkldnn > > library is included with in the pip package build so it does not require > an > > external dependency. > > > > There were concerns that MKLDNN could cause regressions on certain > > platforms (as it did with the tensorflow version a while back); but we > > added a env flag (MXNET_MKLDNN_ENABLED) that allows users to turn of this > > feature during runtime. Please bring up any other concerns you may have > and > > your thoughts on including this accelerator in the default build. > > > > Best, > > Alex > > >
