This is the second article in a series of articles about High Performance Computing with the Intel Xeon Phi.
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.
Download this guide for developing multithreaded applications, which also includes general topics such as application threading and synchronization.
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.
Part one of a five-part series, this article teaches a methodology to interpret statistics gathered during test runs and use those interpretations to improve parallel code.
By Jim Dempsey