Vectorizing improves performance, and achieving high performance can save power. Introduction to tools for vectorizing compute-intensive processing.
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 is part of the Intel® Modern Code Developer Community documentation which supports developers in leveraging application performance in code through a systematic step-by-step optimization framework methodology. This article addresses: Thread level parallelization.
This is the second article in a series of articles about High Performance Computing with the Intel Xeon Phi.