Automatic Recognition of Performance Idioms in Scientific Applications

Abstract

Basic data flow patterns that we call \textbf{performance idioms}, such as stream, transpose, reduction, random access and stencil, are common in scientific numerical applications. We hypothesize that a small number of idioms can cover most programming constructs that dominate the execution time of scientific codes and can be used to approximate the application performance. To check these hypotheses, we proposed an automatic idioms recognition method and implemented the method, based on the open source compiler Open64. With the NAS Parallel Benchmark (NPB) as a case study, the prototype system is about $90%$ accurate compared with idiom classification by a human expert. Our results showed that the above five idioms suffice to cover $100%$ of the six NPB codes (MG, CG, FT, BT, SP and LU). We also compared the performance of our idiom benchmarks with their corresponding instances in the NPB codes on two different platforms with different methods. The approximation accuracy is up to $96.6%$. The contribution is to show that a small set of idioms can cover more complex codes, that idioms can be recognized automatically, and that suitably defined idioms may approximate application performance.