Popular big data frameworks, ranging from Hadoop MapReduce to Spark, rely on garbage-collected languages, such as Java and Scala. Big data applications are especially sensitive to the effectiveness of garbage collection (i.e., GC), because they usually process a large volume of data objects that lead to heavy GC overhead. Lacking in-depth understanding of GC performance has impeded performance improvement in big data applications. In this paper, we conduct the first comprehensive evaluation on three popular garbage collectors, i.e., Parallel, CMS, and G1, using four representative Spark applications. By thoroughly investigating the correlation between these big data applications’ memory usage patterns and the collectors’ GC patterns, we obtain many findings about GC inefficiencies. We further propose empirical guidelines for application developers, and insightful optimization strategies for designing big-data-friendly garbage collectors.
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Towards a Model Checking Framework for a New Collector Framework
Garbage collectors provide memory safety, an important step toward program correctness. However, correctness of the collector itself can be challenging to establish, given both the style in which such systems are written and the weakly-ordered memory accesses of modern hardware. One way to maximize benefits is to use a framework in which effort can be focused on the correctness of small, modular critical components from which various collectors may be composed. Full proof of correctness is likely impractical, so we propose to gain a degree of confidence in collector correctness by applying model checking to critical kernels within a garbage collection framework. We further envisage a model framework, paralleling the framework nature of the collector, in hope that it will be easy to create new models for new collectors. We describe here a prototype model structure, and present results of model checking both stop-the-world and snapshot-at-the-beginning concurrent marking. We found useful regularities of model structure, and that models could be checked within possible time and space budgets on capable servers. This suggests that collectors built in a modular style might be model checked, and further that it may be worthwhile to develop a model checking framework with a domain-specific language from which to generate those models.
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- Award ID(s):
- 1909731
- PAR ID:
- 10431636
- Date Published:
- Journal Name:
- MPLR '22: Proceedings of the 19th International Conference on Managed Programming Languages and Runtimes
- Page Range / eLocation ID:
- 128 to 139
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3546918.3546923
