Optimizing for reduced code space using genetic algorithms

  • Authors:

  • Keith D. Cooper;Philip J. Schielke;Devika Subramanian

  • Affiliations:

  • Department of Computer Science, Rice University, Houston, Texas;Department of Computer Science, Rice University, Houston, Texas;Department of Computer Science, Rice University, Houston, Texas

  • Venue:
  • Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
  • Year:
  • 1999

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Abstract

Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimizations are designed to reduce the execution time of compiled code, but not necessarily the size of the compiled code. Further, different results can be achieved by running some optimizations more than once and changing the order in which optimizations are applied. Register allocation only complicates matters, as the interactions between different optimizations can cause more spill code to be generated. The compiler for embedded systems, then, must take care to use the best sequence of optimizations to minimize code space.Since much of the code for embedded systems is compiled once and then burned into ROM, the software designer will often tolerate much longer compile times in the hope of reducing the size of the compiled code. We take advantage of this by using a genetic algorithm to find optimization sequences that generate small object codes. The solutions generated by this algorithm are compared to solutions found using a fixed optimization sequence and solutions found by testing random optimization sequences. Based on the results found by the genetic algorithm, a new fixed sequence is developed to reduce code size. Finally, we explore the idea of using different optimization sequences for different modules and functions of the same program.