Dissertation/Thesis Abstract

Search-based optimization for compiler machine-code generation
by Clauson, Aran, Ph.D., University of Oregon, 2013, 121; 3600071
Abstract (Summary)

Compilation encompasses many steps. Parsing turns the input program into a more manageable syntax tree. Verification ensures that the program makes some semblance of sense. Finally, code generation transforms the internal abstract program representation into an executable program. Compilers strive to produce the best possible programs. Optimizations are applied at nearly every level of compilation. Instruction Scheduling is one of the last compilation tasks. It is part of code generation. Instruction Scheduling replaces the internal graph representation of the program with an instruction sequence. The scheduler should produce some sequence that the hardware can execute quickly. Considering that Instruction Scheduling is an NP-Complete optimization problem, it is interesting that schedules are usually generated by a greedy, heuristic algorithm called List Scheduling. Given search-based algorithms' successes in other NP-Complete optimization domains, we ask whether search-based algorithms can be applied to Instruction Scheduling to generate superior schedules without unacceptably increasing compilation time. To answer this question, we formulate a problem description that captures practical scheduling constraints. We show that this problem is NP-Complete given modest requirements on the actual hardware. We adapt three different search algorithms to Instruction Scheduling in order to show that search is an effective Instruction Scheduling technique. The schedules generated by our algorithms are generally shorter than those generated by List Scheduling. Search-based scheduling does take more time, but the increases are acceptable for some compilation domains.

Indexing (document details)
Advisor: Wilson, Christopher, Ginsberg, Matthew L.
Commitee: Levin, David A., Massey, Bart, Young, Michal
School: University of Oregon
Department: Department of Computer and Information Science
School Location: United States -- Oregon
Source: DAI-B 75/02(E), Dissertation Abstracts International
Subjects: Comparative literature, Artificial intelligence, Computer science
Keywords: Code-generation, Compiler, Machine-code, Search, Search-based optimization
Publication Number: 3600071
ISBN: 978-1-303-50001-5
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