Abstract
Big data applications tend to be memory intensive, and many of them are written in memory managed languages like Java/Scala. The efficiency of the garbage collector (GC) plays an important role in the performance of these applications. In our paper, we perform a comparative analysis of Java garbage collectors for three commonly used big data workloads to check the choice of the garbage collector for each of the workloads. The garbage collectors under scrutiny are garbage first, parallel and ConcurrentMarkSweep. We demonstrate (a) the relative difference between existing Java workloads that are used to study garbage collectors and big data workloads and (b) the selection of the right garbage collector for a given workload. We find that the garbage first collector gives a performance uplift of up to 15% in certain workloads.
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References
W. Gao, J. Zhan, L. Wang, C. Luo, D. Zheng, X. Wen, H. Tang, Bigdatabench: a scalable and unified big data and AI benchmark suite. arXiv preprint arXiv:1802.08254 (2018)
Apache Spark. https://spark.apache.org/docs/1.3.0/. Accessed 14 July 2020
OpenJDK 12. https://openjdk.java.net/projects/jdk/12/. Accessed 14 July 2020
G1 GC. https://docs.oracle.com/en/java/javase/12/gctuning/garbage-first-garbage-collector.html. Accessed 14 July 2020
CMS GC. https://docs.oracle.com/en/java/javase/12/gctuning/concurrent-mark-sweep-cms-collector.html. Accessed 14 July 2020
Parallel GC. https://docs.oracle.com/en/java/javase/12/gctuning/parallel-collector1.html. Accessed 14 July 2020
L. Gidra, G. Thomas, J. Sopena, M. Shapiro, N. Nguyen, NumaGiC: a garbage collector for big data on big NUMA machines. ACM SIGARCH Comput. Arch. News 43(1), 661–673 (2015)
K. Nguyen, L. Fang, G. Xu, B. Demsky, S. Lu, S. Alamian, O. Mutlu, Yak: a high-performance big-data-friendly garbage collector, in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 349–365 (2016)
S.M. Blackburn, R. Garner, C. Hoffmann, A.M. Khang, K.S. McKinley, R. Bentzur, M. Hirzel, The DaCapo benchmarks: Java benchmarking development and analysis, in Proceedings of the 21st Annual ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications, pp. 169–190 (2006, October)
Y. He, C. Yang, X.F. Li, Improve google android user experience with regional garbage collection, in IFIP International Conference on Network and Parallel Computing (Springer, Berlin, Heidelberg, 2011, October), pp. 350–365
T. Gerlitz, I. Kalkov, J.F. Schommer, D. Franke, S. Kowalewski, Non-blocking garbage collection for real-time android, in Proceedings of the 11th International Workshop on Java Technologies for Real-time and Embedded Systems, pp. 108–117 (2013, October)
L. Gidra, G. Thomas, J. Sopena, M. Shapiro, A study of the scalability of stop-the-world garbage collectors on multicores. ACM SIGPLAN Notices 48(4), 229–240 (2013)
L. Gidra, G. Thomas, J. Sopena, M. Shapiro, Assessing the scalability of garbage collectors on many cores, in Proceedings of the 6th Workshop on Programming Languages and Operating Systems (ACM, New York, 2011, October), p. 7
M. Carpen-Amarie, P. Marlier, P. Felber, G. Thomas, A performance study of Java garbage collectors on multicore architectures, in Proceedings of the Sixth International Workshop on Programming Models and Applications for Multicores and Manycores, pp. 20–29 (2015, February)
P. Lengauer, V. Bitto, H. Möossenböck, M. Weninger, A comprehensive Java benchmark study on memory and garbage collection behavior of DaCapo, DaCapo Scala, and SPECjvm2008, in Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, pp. 3–14 (2017, April)
H. Grgic, B. Mihaljević, A. Radovan, Comparison of garbage collectors in Java programming language, in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (IEEE, New York, 2018, May), pp. 1539–1544
P. Pufek, H. Grgić, B. Mihaljević, Analysis of garbage collection algorithms and memory management in Java, in 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (IEEE, New York, 2019, May), pp. 1677–1682
S. Iqbal, M. Khan, I. Memon, A Comparative Study of Garbage Collection Techniques in Java Virtual Machines. Sindh University Research Journal-SURJ (Science Series) 44(4) (2012)
R. Bruno, P. Ferreira, A study on garbage collection algorithms for big data environments. ACM Comput. Surv. (CSUR) 51(1), 1–35 (2018)
L. Xu, T. Guo, W. Dou, W. Wang, J. Wei, An experimental evaluation of garbage collectors on big data applications, in The 45th International Conference on Very Large Data Bases (VLDB’19) (2019, January)
BigDataBench User Manual. http://prof.ict.ac.cn/BigDataBench/wp-content/uploads/2014/12/BigDataBench-User-Manual.pdf. Accessed 14 July 2020
DaCapo benchmarks. http://dacapobench.org. Accessed 14 July 2020
DaCapo benchmarks description. http://dacapobench.sourceforge.net/benchmarks.html. Accessed 14 July 2020
Gceasy Tool. https://gceasy.io. Accessed 14 July 2020
J. Singer, G. Kovoor, G. Brown, M. Luján, Garbage collection auto-tuning for java mapreduce on multi-cores. ACM SIGPLAN Notices 46(11), 109–118 (2011)
Acknowledgements
The authors wish to thank Dr. Prakash Raghavendra from AMD India Pvt. Ltd. for providing intellectual assistance throughout the study.
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Nair, A., Sriram, A., Simon, A., Kalambur, S., Sitaram, D. (2021). A Comparative Analysis of Garbage Collectors and Their Suitability for Big Data Workloads. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 735. Springer, Singapore. https://doi.org/10.1007/978-981-33-6977-1_24
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