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Automatic Generation of Peephole Superoptimizers Sorav Bansal and Alex Aiken
 

Summary: Automatic Generation of Peephole Superoptimizers
Sorav Bansal and Alex Aiken
Computer Systems Lab
Stanford University
{sbansal, aiken}@cs.stanford.edu
Abstract
Peephole optimizers are typically constructed using human-written
pattern matching rules, an approach that requires expertise and
time, as well as being less than systematic at exploiting all oppor-
tunities for optimization. We explore fully automatic construction
of peephole optimizers using brute force superoptimization. While
the optimizations discovered by our automatic system may be less
general than human-written counterparts, our approach has the po-
tential to automatically learn a database of thousands to millions
of optimizations, in contrast to the hundreds found in current peep-
hole optimizers. We show experimentally that our optimizer is able
to exploit performance opportunities not found by existing com-
pilers; in particular, we show speedups from 1.7 to a factor of 10
on some compute intensive kernels over a conventional optimizing
compiler.

  

Source: Aiken, Alex - Department of Computer Science, Stanford University

 

Collections: Computer Technologies and Information Sciences