This is the second article in a series of articles about High Performance Computing with the Intel Xeon Phi.
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.
A 3-part educational series on Optimization Techniques for the Intel® MIC Architecture is provided by Colfax Research. The series focuses on select topics on optimization of applications for Intel’s multi-core and manycore architectures (Intel® Xeon® processors and Intel® Xeon Phi™ processors).
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.
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 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.
Get a background on vectorization and learn different techniques to evaluate its effectiveness.