@misc{10498/33025,
year = {2024},
month = {6},
url = {http://hdl.handle.net/10498/33025},
abstract = {Traditionally, compilers perform a sequence of optimizations that does not account for the distinct characteristics of different software/hardware combinations, often leading to suboptimal performance. We propose in this work  a  novel  approach  to  automatize  the  process  of  optimizing  the  performance of a particular SW program on a specific HW. For that, we define a novel combinatorial optimization problem, that we call SCOP (for Software Code Optimization Problem), consisting in finding a sequence of code transformations that minimizes the runtime of the resulting SW on the considered HW architecture. Utilizing LLVM for transformations, our approach significantly diverges from conventional methods by automating the optimization process to adapt dynamically to various SW/HW pairs, surpassing standard optimization flags in efficacy. We propose the use of a Cellular Genetic Algorithm  (cGA)  to  find  a  sequence  of  LLVM  passes  that  minimizes  the  SW runtime.  To  deal  with  the  uncertainty  on  the  SW  runtime,  we  study  five different  fitness  functions:  one  execution,  median,  worst  case,  interval,  and worst-case interval from 15 executions, the latter two being novel proposals.We evaluated our approach using the Polybench benchmark on various HW architectures,  including  ARM  and  Intel-based  systems.  The  results  demonstrate that the sequences identified by the cGA consistently outperform the runtime  of  SW  optimized  using  traditional  methods,  such  as  the  -O3  optimization flag provided by Clang. This paper describes a novel combinatorial optimization problem for automatically optimizing SW programs performance considering uncertainty in measurements. The work was originally published in [1]. Among the five different fitness functions considered, those based on the worst case are the most resilient to system uncertainty, with our proposed new approach being the most accurate. The execution time of the original program is reduced by up to 63.2%, outperforming the -O3 flag by up to 26.3%.},
keywords = {Software performance},
keywords = {LLVM},
keywords = {execution time},
keywords = {uncertainty},
keywords = {source code transformation},
keywords = {evolutionary computation},
title = {Software Performance Optimization with Genetic Algorithms},
doi = {10.5281/zenodo.11922271},
author = {Aragón Jurado, José Miguel and Torre Macías, Juan Carlos de la and Ruiz Villalobos, Patricia and Dorronsoro Díaz, Bernabé},
}