Type-sensitive control-flow analysis

Higher-order typed languages, such as ML, provide strong support for data and type abn. While such abstraction is often viewed as costing performance, there are situations where it may provide opportunities for more aggressive program optimization. Specifically, we oit the fact that type abstraction guarantees representation independence, which allows the compiler to specialize data representations. This paper describes a first step in supporting such optimizations; namely a modular control-flow analysis that uses the program's type information to compute more precise results. We present our algorithm as an extension of Serrano's version of 0-CFA and we show that it respects types. We also discuss applications of the analysis with a specific focus on optimizing Concurrent ML programs.

• Type: Journal
• Year: 2006
• Where: ML
• Authors: John H. Reppy
• Keywords: Aggressive Program Optimization, Machine Learning, ML 2006, Modular Control-flow Analysis, Type Abn