LIPIcs.ECOOP.2024.35.pdf
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Partial Redundancy Elimination in Two Iterative Data Flow Analyses
Authors
Reshma Roy 
,
Sreekala S ,
Vineeth Paleri
-
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38th European Conference on Object-Oriented Programming (ECOOP 2024)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: European Conference on Object-Oriented Programming (ECOOP) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-09-12
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Reshma Roy, Sreekala S, and Vineeth Paleri. Partial Redundancy Elimination in Two Iterative Data Flow Analyses. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 35:1-35:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.ECOOP.2024.35
Abstract
Partial Redundancy Elimination (PRE) is a powerful and well-known code optimization. The idea to combine Common Subexpression Elimination and Loop Invariant Code Motion optimizations into a single optimization was originally conceived by Morel and Renvoise. Their algorithm is bidirectional in nature and was not complete and optimal. Later, Knoop et al. proposed the first complete and optimal algorithm, Lazy Code Motion (LCM), which takes four unidirectional data flow analyses. In a recent paper, Roy et al. proposed an algorithm for PRE that uses three iterative data flow analyses. Here, we propose an efficient algorithm for PRE, which takes only two iterative data flow analyses followed by two computation passes over the program. The algorithm is both computationally and lifetime optimal. The proposed algorithm computes the information required for performing the transformation in two passes over the program without considering safety. The two iterative data flow analyses are required for making the transformation safe. The use of well-known data flow analyses, i.e., available expressions analysis and anticipated expressions analysis, makes the algorithm simple to understand and easy to prove its correctness. The proposed algorithm is more efficient than the existing algorithms since it takes only two iterative data flow analyses. The efficiency of the proposed algorithm is demonstrated by implementing it in LLVM Compiler Infrastructure and comparing the time taken with other selected best-known algorithms.
Subject Classification
ACM Subject Classification
- Software and its engineering → Compilers
Keywords
- Static Analysis
- Data Flow Analysis
- Code Optimization
- Partial Redundancy Elimination
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References
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