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Improve Vectorization Performance with Intel® AVX-512

See how the new Intel® Advanced Vector Extensions 512CD and the Intel AVX512F subsets (available in the Intel® Xeon Phi processor and in future Intel Xeon processors) lets the compiler automatically generate vector code with no changes to the code.
Автор: Alberto V. (Intel) Последнее обновление: 08.07.2019 - 19:26
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Big Datasets from Small Experiments

Автор: Andrey Vladimirov Последнее обновление: 04.07.2019 - 18:46
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Brain Development Simulation, 300x Faster

Автор: Andrey Vladimirov Последнее обновление: 04.07.2019 - 17:45
Article

Case Study: Optimized Code for Neural Cell Simulations

One of the Intel® Modern Code Developer Challenge winners, Daniel Falguera, describes many of the optimizations he implemented and why some didn't work.
Автор: Последнее обновление: 03.10.2019 - 07:55
Article

案例研究: 面向神经细胞模拟优化代码

Intel held the Intel® Modern Code Developer Challenge that had about 2,000 students from 130 universities in 19 countries registered to participate in the Challenge. They were provided access to Intel® Xeon Phi™ coprocessors to optimize code used in a CERN openlab brain simulation research project. In this article Daniel Vea Falguera (Modern Code Developer Challenge winner) shares how he...
Автор: Последнее обновление: 03.10.2019 - 07:56
Article

基于英特尔® 架构加速金融应用

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Автор: George Raskulinec (Intel) Последнее обновление: 03.10.2019 - 08:00
Article

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Автор: Последнее обновление: 15.10.2019 - 15:30
Article

Thread Parallelism in Cython*

Cython* is a superset of Python* that additionally supports C functions and C types on variable and class attributes. Cython generates C extension modules, which can be used by the main Python program using the import statement.
Автор: Nguyen, Loc Q (Intel) Последнее обновление: 15.10.2019 - 16:40
Article

面向英特尔® 架构优化的 Caffe*:使用现代代码技巧

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Автор: Последнее обновление: 15.10.2019 - 16:50
Article

Accelerating Financial Applications on Intel® architecture

Learn more about an in-depth analysis of code modernization performance conducted by optimizing original CPU code and re-running tests on the latest GPU/CPU hardware.
Автор: George Raskulinec (Intel) Последнее обновление: 17.10.2019 - 14:37