Near-optimal instruction selection on dags

  • Authors:
  • David Ryan Koes;Seth Copen Goldstein

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
  • Year:
  • 2008

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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.