Mechanisms for mapping high-level parallel performance data

Abstract

A primary problem in the performance measurement of high-level parallel programming languages is to map low-level events to high-level programming constructs. We discuss several aspects of this problem and present three methods with which performance tools can map performance data and provide accurate performance information to programmers. In particular, we discuss static mapping, dynamic mapping, and a new technique that uses a data structure called the set of active sentences. Because each of these methods requires cooperation between compilers and performance tools, we describe the nature and amount of cooperation required. The three mapping methods are orthogonal; we describe how they should be combined in a complete tool. Although we concentrate on mapping upward through layers of abstraction, our techniques are independent of mapping direction.