Skip to Main content Skip to Navigation
Conference papers

MILEPOST GCC: machine learning based research compiler

Abstract : Tuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe MILEPOST GCC, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures. Our preliminary experimental results show that it is possible to considerably reduce execution time of the MiBench benchmark suite on a range of platforms entirely automatically.
Complete list of metadata

Cited literature [28 references]  Display  Hide  Download
Contributor : Grigori Fursin <>
Submitted on : Thursday, July 10, 2008 - 11:56:22 AM
Last modification on : Thursday, July 8, 2021 - 3:52:50 AM
Long-term archiving on: : Monday, October 1, 2012 - 11:00:23 AM


Files produced by the author(s)


  • HAL Id : inria-00294704, version 1



Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, et al.. MILEPOST GCC: machine learning based research compiler. GCC Summit, Jun 2008, Ottawa, Canada. ⟨inria-00294704⟩



Record views


Files downloads