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.
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.
Matrix multiplication (MM) of two matrices is one of the most fundamental operations in linear algebra. The algorithm for MM is very simple, it could be easily implemented in any programming language. This paper shows that performance significantly improves when different optimization techniques are applied.
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
Exercise in performance optimization on Intel Architecture, including Intel® Xeon Phi™ processors.
This case study examines the situation where the problem decomposition is the same for threading as it is for Message Passing Interface* (MPI); that is, the threading parallelism is elevated to the same level as MPI parallelism.
This document is designed to help users get started writing code and running MPI applications using the Intel® MPI Library on a development platform that includes the Intel® Xeon Phi™ processor.
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.