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.
Find out how to use the command-line interface in Intel® Advisor 2017 for a quick, initial analysis of loop performance that gives an overview of the hotspots in your code.
This paper examines software performance optimization for an implementation of a non-library version of DGEMM executing on the Intel® Xeon Phi™ processor (code-named Knights Landing, with acronym K
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.
This article focuses on the steps to improve software performance with vectorization. Included are examples of full applications along with some simpler cases to illustrate the steps to vectorization.
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.