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Unleash the Parallel Performance of Python* Programs

[updated 10/5/2018]

Criado por Anton Malakhov (Intel) Última atualização em 05/10/2018 - 18:24
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Intel® CPU Excels in MLPerf* Reinforcement Learning Training

Today, MLPerf* consortium, a group of 40 companies and university research institutes, published the 2nd round of the benchmark results based upon ML

Criado por Koichi Yamada (Intel) Última atualização em 30/09/2019 - 16:50
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Modernizing Software with Future-Proof Code Optimizations

by Henry A. Gabb, Sr. Principal Engineer, Intel Software and Services Group

Criado por Henry Gabb (Intel) Última atualização em 15/10/2019 - 19:18
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Doubling the Performance of OpenStack Swift* with No Code Changes

My current gig is mostly about performance. I manage a group of software engineers dedicated to the languages becoming really important to the cloud and the datacenter.

Criado por David S. (Blackbelt) Última atualização em 15/10/2019 - 19:31
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The JITter Conundrum - Just in Time for Your Traffic Jam

In interpreted languages, it just takes longer to get stuff done - I earlier gave the example where the Python source code a = b + c would result in a BINARY_ADD byte code which takes 78 machine instructions to do the add, but it's a single native ADD instruction if run in compiled language like C or C++. How can we speed this up? Or as the performance expert would say, how do I decrease...
Criado por David S. (Blackbelt) Última atualização em 15/10/2019 - 19:42