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.
Fine-Tuning Optimization for a Numerical Method for Hyperbolic Equations Applied to a Porous Media Flow Problem with Intel® ToolsThis paper presents an analysis for potential optimization for a Godunov-type semi-discrete central scheme, for a particular hyperbolic problem implicated in porous media flow, using OpenMP* and Intel® Advanced Vector Extensions 2.
The newest versions of the Intel® C++ and Fortran compilers now support OpenMP* environment variable OMP_PROC_BIND on compatible non-Intel processors for Linux* and Windows* platfo
Intel is bringing to market, in anticipation of general availability of the Intel® Xeon Phi™ Processor (codenamed Knights Landing), the Developer Access Program (DAP). DAP is an early access program for developers worldwide to purchase an Intel Xeon Phi Processor based system.
Purpose of this demo is to show an advantage of Westmere Crypto Acceleration Engine.
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.
A step-by-step introduction to application performance tuning using the Intel® Compilers version 13 for IA-32 and Intel® 64 processors that are included with Intel® Parallel Studio XE 2013
The article provides hints for linking your program with Intel® MKL from the Microsoft* Visual Studio Environment: Microsoft* Visual Studio 2017/2015/2013/2012/2010 -- Automatically Microsoft* Visual Studio 2017/2015/2013/2012/2010 -- Manually
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.