Virtual Machine Profiling for Analyzing Resource Usage of Applications

Peng, Xuesong; Pernici, Barbara; Vitali, Monica

doi:10.1007/978-3-319-94376-3_7

Cited by 8 publications

(1 citation statement)

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“…Other profiling solutions exists in the literature, although they center the analysis in improving the speed of the running application. So, they provide little to none information about the interactions between the VM components [21,25,33,38].…”

Section: Related Workmentioning

confidence: 99%

Profiling code cache behaviour via events

Tesone

Polito

Ducasse

2021

Proceedings of the 18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes

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Virtual machine performance tuning for a given application is an arduous and challenging task. For example, parametrizing the behaviour of the JIT compiler machine code caches affects the overall performance of applications while being rather obscure for final users not knowledgeable about VM internals. Moreover, VM components are often heavily coupled and changes in some parameters may affect several seemingly unrelated components and may have unclear performance impacts. Therefore, choosing the best parametrization requires to have precise information.In this paper, we present Vicoca, a tool that allows VM users and developers to obtain detailed information about the behaviour of the code caches and their interactions with other virtual machine components. We present a complex optimization problem due to the heavy interaction of components in the Pharo VM, and we explain it using Vicoca. The information produced by the tool allows developers to produce an optimized configuration for the VM. Vicoca is based on event recording that are manipulated during offline analysis. Vicoca not only allows us to understand this given problem, but it opens the door to future work such as automatic detection of application characteristics, identification of performance issues, and automatic hinting.CCS Concepts: • Software and its engineering → Runtime environments.

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Section: Related Workmentioning

confidence: 99%