Near-Optimal Instruction Selection on DAGs
David Ryan Koes and Seth Copen Goldstein
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 im-
plement, fast, and effective with a demonstrated average code size
improvement of 5.1% compared to the traditional tree decomposi-
tion and tiling approach.
Published:
"Near-Optimal Instruction Selection on DAGs"
David Ryan Koes and Seth Copen Goldstein
Proc. of the 2008 International Symposium on Code Generation and Optimization (CGO'08), Boston, MA, 2008.
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