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A comprehensive toolchain for workload characterization across JVM languages

Publication at Faculty of Mathematics and Physics |
2013

Abstract

The Java Virtual Machine (JVM) today hosts implementations of numerous languages. To achieve high performance, JVM implementations rely on heuristics in choosing compiler optimizations and adapting garbage collection behavior.

Historically, these heuristics have been tuned to suit the dynamics of Java programs only. This leads to unnecessarily poor performance in case of non-Java languages, which often exhibit systematic differences in workload behavior.

Dynamic metrics characterizing the workload help to identify and quantify useful optimizations, but so far, no cohesive suite of metrics has adequately covered properties that vary systematically between Java and non-Java workloads. We present a suite of such metrics, justifying our choice with reference to a range of guest languages.

These metrics are implemented on a common portable infrastructure which ensures ease of deployment and customization.