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.
In this article, I discuss some common performance pitfalls in Cilk™ Plus programs that prevent users from seeing speedups in their code, and describe some techniques for avoiding these pitfalls.
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.