Comparing mark-and sweep and stop-and-copy garbage collection
LFP '90 Proceedings of the 1990 ACM conference on LISP and functional programming
Predicting program behavior using real or estimated profiles
PLDI '91 Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation
Comparing mostly-copying and mark-sweep conservative collection
Proceedings of the 1st international symposium on Memory management
Adaptive optimization in the Jalapeño JVM
OOPSLA '00 Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
The case for profile-directed selection of garbage collectors
Proceedings of the 2nd international symposium on Memory management
Predicting whole-program locality through reuse distance analysis
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Oil and Water? High Performance Garbage Collection in Java with MMTk
Proceedings of the 26th International Conference on Software Engineering
Myths and realities: the performance impact of garbage collection
Proceedings of the joint international conference on Measurement and modeling of computer systems
Dynamic selection of application-specific garbage collectors
Proceedings of the 4th international symposium on Memory management
Improving virtual machine performance using a cross-run profile repository
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
The DaCapo benchmarks: java benchmarking development and analysis
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Predicting locality phases for dynamic memory optimization
Journal of Parallel and Distributed Computing
Hot-swapping between a mark&sweep and a mark&compact garbage collector in a generational environment
JVM'01 Proceedings of the 2001 Symposium on JavaTM Virtual Machine Research and Technology Symposium - Volume 1
Intelligent selection of application-specific garbage collectors
Proceedings of the 6th international symposium on Memory management
Statistically rigorous java performance evaluation
Proceedings of the 22nd annual ACM SIGPLAN conference on Object-oriented programming systems and applications
Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
Garbage collection auto-tuning for Java mapreduce on multi-cores
Proceedings of the international symposium on Memory management
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Many studies have shown that the best performer among a set of garbage collectors tends to be different for different applications. Researchers have proposed applicationspecific 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, by adoptingstatistical learning techniques, we investigate the cross-input predictability of the influence. Experimental results demonstrate that with regression and classification techniques, it is possible to predict the best garbage collector (along with the minimum possible heap size) with reasonable accuracy given an arbitrary input to an application. The exploration opens the opportunities for tailoring the selection of garbage collectors to not only applications but also their inputs.