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.
Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
MILC software represents a set of codes written by the MIMD Lattice Computation collaboration used to study quantum chromodynamics. This article provides instructions for code access, build and run directions for the “ks_imp_rhmc” application on Intel® Xeon® Gold and Intel® Xeon Phi™ processors for better performance on a single node.
Modern high performance computers are built with a combination of resources including:
Learn practical tips for using the vectorization advisor, which is part of Intel® Advisor.
Get a background on vectorization and learn different techniques to evaluate its effectiveness.