Automatic configuration of GCC using irace

Leslie Pérez Cáceres, Federico Pagnozzi, Alberto Franzin, Thomas Stützle

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

6 Citas (Scopus)

Resumen

Automatic algorithm configuration techniques have proved to be successful in finding performance-optimizing parameter settings of many search-based decision and optimization algorithms. A recurrent, important step in software development is the compilation of source code written in some programming language into machine-executable code. The generation of performance-optimized machine code itself is a difficult task that can be parametrized in many different possible ways. While modern compilers usually offer different levels of optimization as possible defaults, they have a larger number of other flags and numerical parameters that impact properties of the generated machine-code. While the generation of performance-optimized machine code has received large attention and is dealt with in the research area of auto-tuning, the usage of standard automatic algorithm configuration software has not been explored, even though, as we show in this article, the performance of the compiled code has significant stochasticity, just as standard optimization algorithms. As a practical case study, we consider the configuration of the well-known GNU compiler collection (GCC) for minimizing the run-time of machine code for various heuristic search methods. Our experimental results show that, depending on the specific code to be optimized, improvements of up to 40% of execution time when compared to the -O2 and -O3 optimization flags is possible.

Idioma originalInglés
Título de la publicación alojadaArtificial Evolution - 13th International Conference, Evolution Artificielle, EA 2017, Revised Selected Papers
EditoresPierre Parrend, Evelyne Lutton, Pierrick Legrand, Nicolas Monmarche, Marc Schoenauer
EditorialSpringer Verlag
Páginas202-216
Número de páginas15
ISBN (versión impresa)9783319781327
DOI
EstadoPublicada - 2018
Publicado de forma externa
Evento13th International Conference on Artificial Evolution, EA 2017 - Paris, Francia
Duración: 25 oct 201727 oct 2017

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10764 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th International Conference on Artificial Evolution, EA 2017
País/TerritorioFrancia
CiudadParis
Período25/10/1727/10/17

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