Near-optimal instruction selection on dags

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Near-optimal instruction selection on dags

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Code Generation and Optimization archive
Proceedings of the sixth annual IEEE/ACM international symposium on Code generation and optimization table of contents

Boston, MA, USA

SESSION: Static program analysis table of contents

Pages 45-54

Year of Publication: 2008

ISBN:978-1-59593-978-4

Authors

David Ryan Koes	Carnegie Mellon University, Pittsburgh, PA, USA
Seth Copen Goldstein	Carnegie Mellon University, Pittsburgh, PA, USA

Sponsors

ACM: Association for Computing Machinery
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
SIGPLAN: ACM Special Interest Group on Programming Languages

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 6, Downloads (12 Months): 74, Citation Count: 0

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ABSTRACT

Instruction selection is a key component of code generation. High quality instruction selection is of particular importance in the embedded space where complex instruction sets are common and code size is a prime concern. Although instruction selection on tree expressions is a well understood and easily solved problem, instruction selection on directed acyclic graphs is NP-complete. In this paper we present NOLTIS, a near-optimal, linear time instruction selection algorithm for DAG expressions. NOLTIS is easy to implement, fast, and effective with a demonstrated average code size improvement of 5.1% compared to the traditional tree decomposition and tiling approach.

REFERENCES

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