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Live-range unsplitting for faster optimal coalescing
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Language, Compiler and Tool Support for Embedded Systems archive
Proceedings of the 2009 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems table of contents
Dublin, Ireland
SESSION: Programming languages and compiler table of contents
Pages 70-79  
Year of Publication: 2009
ISBN:978-1-60558-356-3
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Authors
Sandrine Blazy  ENSIIE, Evry, France
Benoit Robillard  ENSIIE, Evry, France
Sponsors
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

Register allocation is often a two-phase approach: spilling of registers to memory, followed by coalescing of registers. Extreme live-range splitting (i.e. live-range splitting after each statement) enables optimal solutions based on ILP, for both spilling and coalescing. However, while the solutions are easily found for spilling, for coalescing they are more elusive. This difficulty stems from the huge size of interference graphs resulting from live-range splitting.

This paper focuses on coalescing in the context of extreme live-range splitting. It presents some theoretical properties that give rise to an algorithm for reducing interference graphs. This reduction consists mainly in finding and removing useless splitting points. It is followed by a graph decomposition based on clique separators. The reduction and decomposition are general enough, so that any coalescing algorithm can be applied afterwards.

Our strategy for reducing and decomposing interference graphs preserves the optimality of coalescing. When used together with an optimal coalescing algorithm (e.g. ILP), optimal solutions are much more easily found. The strategy has been tested on a standard benchmark, the optimal coalescing challenge. For this benchmark, the cutting-plane algorithm for optimal coalescing (the only optimal algorithm for coalescing) runs 300 times faster when combined with our strategy. Moreover, we provide all the optimal solutions of the optimal coalescing challenge, including the three instances that were previously unsolved.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Andrew W. Appel and Lal George. Optimal coalescing challenge, 2000. http://www.cs.princeton.edu/ appel/coalesce
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Collaborative Colleagues:
Sandrine Blazy: colleagues
Benoit Robillard: colleagues