In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
This article will describe performance considerations for CPU inference using Intel® Optimization for TensorFlow*
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 explores what happens when Intel solutions support functional and logic programming languages that are regularly used for Artificial Intelligence (AI) and proposes a Prolog interpreter recompilation using Intel® C++ Compiler and libraries in order to evaluate their contribution to logic based AI.
This article provides a recipe for how to obtain, compile, and run ROME1.0 SML on Intel® Xeon® processors and Intel® Xeon Phi™ processors.
In continued efforts to optimize Deep Learning workloads on Intel® architecture, our engineers explore various paths leading to the maximum performance.
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.
Get recipes for installing development tools and libraries on various platforms for the Python library.