TY - GEN
T1 - Automatic configuration of GCC using irace
AU - Pérez Cáceres, Leslie
AU - Pagnozzi, Federico
AU - Franzin, Alberto
AU - Stützle, Thomas
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Automatic configuration
KW - GCC
KW - Irace
KW - Parameter tuning
UR - http://www.scopus.com/inward/record.url?scp=85044424996&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-78133-4_15
DO - 10.1007/978-3-319-78133-4_15
M3 - Conference contribution
AN - SCOPUS:85044424996
SN - 9783319781327
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 202
EP - 216
BT - Artificial Evolution - 13th International Conference, Evolution Artificielle, EA 2017, Revised Selected Papers
A2 - Parrend, Pierre
A2 - Lutton, Evelyne
A2 - Legrand, Pierrick
A2 - Monmarche, Nicolas
A2 - Schoenauer, Marc
PB - Springer Verlag
T2 - 13th International Conference on Artificial Evolution, EA 2017
Y2 - 25 October 2017 through 27 October 2017
ER -