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Type-based alias analysis

Amer Diwan, Kathryn S. McKinley, J. Eliot B. Moss

Open publisher page 169 citations

Abstract

This paper evaluates three alias analyses based on programming language types. The first analysis uses type compatibility to determine aliases. The second extends the first by using additional high-level information such as field names. The third extends the second with a flow-insensitive analysis. Although other researchers suggests using types to disambiguate memory references, none evaluates its effectiveness. We perform both static and dynamic evaluations of type-based alias analyses for Modula-3, a statically-typed type-safe language. The static analysis reveals that type compatibility alone yields a very imprecise alias analysis, but the other two analyses significantly improve alias precision. We use redundant load elimination (RLE) to demonstrate the effectiveness of the three alias algorithms in terms of the opportunities for optimization, the impact on simulated execution times, and to compute an upper bound on what a perfect alias analysis would yield. We show modest dynamic improvements for (RLE), and more surprisingly, that on average our alias analysis is within 2.5% of a perfect alias analysis with respect to RLE on 8 Modula-3 programs. These results illustrate that to explore thoroughly the effectiveness of alias analyses, researchers need static, dynamic, and upper-bound analysis. In addition, we show that for type-safe languages like Modula-3 and Java, a fast and simple alias analysis may be sufficient for many applications.

About this research paper

What this paper is about

This paper evaluates three alias analyses based on programming language types. The first analysis uses type compatibility to determine aliases. The second extends the first by using additional high-level information such as field names. The third extends the second with a flow-insensitive analysis. Although other researchers suggests using types to disambiguate memory references, none evaluates its effectiveness. We perform both static and dynamic evaluations of type-based alias analyses for Modula-3, a statically-typed type-safe language. The static analysis reveals that type compatibility alone yields a very imprecise alias analysis, but the other two analyses significantly improve alias precision. We use redundant load elimination (RLE) to demonstrate the effectiveness of the three alias algorithms in terms of the opportunities for optimization, the impact on simulated execution times, and to compute an upper bound on what a perfect alias analysis would yield. We show modest dynamic improvements for (RLE), and more surprisingly, that on average our alias analysis is within 2.5% of a perfect alias analysis with respect to RLE on 8 Modula-3 programs. These results illustrate that to explore thoroughly the effectiveness of alias analyses, researchers need static, dynamic, and upper-bound analysis. In addition, we show that for type-safe languages like Modula-3 and Java, a fast and simple alias analysis may be sufficient for many applications.

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Available abstract

This paper evaluates three alias analyses based on programming language types. The first analysis uses type compatibility to determine aliases. The second extends the first by using additional high-level information such as field names. The third extends the second with a flow-insensitive analysis. Although other researchers suggests using types to disambiguate memory references, none evaluates its effectiveness. We perform both static and dynamic evaluations of type-based alias analyses for Modula-3, a statically-typed type-safe language. The static analysis reveals that type compatibility alone yields a very imprecise alias analysis, but the other two analyses significantly improve alias precision. We use redundant load elimination (RLE) to demonstrate the effectiveness of the three alias algorithms in terms of the opportunities for optimization, the impact on simulated execution times, and to compute an upper bound on what a perfect alias analysis would yield. We show modest dynamic improvements for (RLE), and more surprisingly, that on average our alias analysis is within 2.5% of a perfect alias analysis with respect to RLE on 8 Modula-3 programs. These results illustrate that to explore thoroughly the effectiveness of alias analyses, researchers need static, dynamic, and upper-bound analysis. In addition, we show that for type-safe languages like Modula-3 and Java, a fast and simple alias analysis may be sufficient for many applications.

Key concepts: Alias, Computer science, Java, Programming language, Static analysis, Algorithm, Parallel computing, Data mining

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