Annotation-based empirical performance tuning using Orio

In many scientific applications, significant time is spent tuning codes for a particular highperformance architecture. Tuning approaches range from the relatively nonintrusive (e.g., by using compiler options) to extensive code modifications that attempt to exploit specific architecture features. Intrusive techniques often result in code changes that are not easily reversible, which can negatively impact readability, maintainability, and performance on different architectures. We introduce an extensible annotation-based empirical tuning system called Orio, which is aimed at improving both performance and productivity by enabling software developers to insert annotations in the form of structured comments into their source code that trigger a number of low-level performance optimizations on a specified code fragment. To maximize the performance tuning opportunities, we have designed the annotation processing infrastructure to support both architecture-independent and architecture-...

• Type: Conference
• Year: 2009
• Where: IPPS
• Authors: Albert Hartono, Boyana Norris, Ponnuswamy Sadayappan
• Keywords: Code, Distributed And Parallel Computing, IPPS 2009, Low-level Performance Optimizations, Performance