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 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.
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.
This paper introduces the Artificial Intelligence (AI) community to Intel® optimization for TensorFlow* on Intel® Xeon® and Intel® Xeon Phi™ processor-based CPU platforms.
Boosting Deep Learning Training & Inference Performance on Intel® Xeon® and Intel® Xeon Phi™ ProcessorsIn this work we present how, without a single line of code change in the framework, we can further boost the performance for deep learning training by up to 2X and inference by up to 2.7X on top of the current software optimizations available from open source TensorFlow* and Caffe* on Intel® Xeon® processors.
This case study evaluates the ability of TensorFlow* Object Detection API to solve a real-time problem such as traffic light detection on Intel® Xeon® processor-based CPU machines.