Influence of program inputs on the selection of garbage collectors

Feng Mao, Eddy Z. Zhang, Xipeng Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

Many studies have shown that the best performer among a set of garbage collectors tends to be different for different applications. Researchers have proposed application-specific selection of garbage collectors. In this work, we concentrate on a second dimension of the problem: the influence of program inputs on the selection of garbage collectors. We collect tens to hundreds of inputs for a set of Java benchmarks, and measure their performance on Jikes RVM with different heap sizes and garbage collectors. A rigorous statistical analysis produces four-fold insights. First, inputs influence the relative performance of garbage collectors significantly, causing large variations to the top set of garbage collectors across inputs. Profiling one or few runs is thus inadequate for selecting the garbage collector that works well for most inputs. Second, when the heap size ratio is fixed, one or two types of garbage collectors are enough to stimulate the top performance of the program on all inputs. Third, for some programs, the heap size ratio significantly affects the relative performance of different types of garbage collectors. For the selection of garbage collectors on those programs, it is necessary to have a cross-input predictive model that predicts the minimum possible heap size of the execution on an arbitrary input. Finally, based on regression techniques, we demonstrate the predictability of the minimum possible heap size, indicating the potential feasibility of the input-specific selection of garbage collectors.

Original languageEnglish (US)
Title of host publicationProceedings of the 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE'09
Pages91-100
Number of pages10
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE'09 - Washington, DC, United States
Duration: Mar 11 2009Mar 13 2009

Publication series

NameProceedings of the 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE'09

Other

Other2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE'09
CountryUnited States
CityWashington, DC
Period3/11/093/13/09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software

Keywords

  • Cross-input program analysis
  • Input-specific selection
  • Minimum possible
  • Profiling
  • Selection of garbage collectors

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