Static Insertion of Safe and Effective Memory Reuse Commands into ML-like Programs

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We present a static analysis that estimates reusable memory cells and a source-level transformation that adds explicit memory reuse commands into the program text. For benchmark ML programs, our analysis and transformation system achieves a memory reuse ratio from 5.2% to 91.3% and reduces the memory peak from 0.0% to 71.9%. The small-ratio cases are for programs that have a number of data structures that are shared. For other cases, our experimental results are encouraging in terms of accuracy and cost. Major features of our analysis and transformation are: (1) polyvariant analysis of functions by parameterization for the argument heap cells; (2) use of multiset formulas in expressing the sharings and partitionings of heap cells; (3) deallocations conditioned by dynamic flags that are passed as extra arguments to functions; (4) individual heap cells as the granularity of explicit memory reuse. Our analysis and transformation system is fully automatic. (c) 2005 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2005-10
Language
English
Article Type
Article; Proceedings Paper
Citation

SCIENCE OF COMPUTER PROGRAMMING, v.58, no.1-2, pp.141 - 178

ISSN
0167-6423
DOI
10.1016/j.scico.2005.02.007
URI
http://hdl.handle.net/10203/225296
Appears in Collection
CS-Journal Papers(저널논문)
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