Abstract
Stream processing plays a vital role in applications that require continuous, low-latency data processing. Thanks to their extensive parallel processing capabilities and relatively low cost, GPUs are well-suited to scenarios where such applications require substantial computational resources. However, micro-batching becomes essential for efficient GPU computation within stream processing systems. However, finding appropriate batch sizes to maintain an adequate level of service is often challenging, particularly in cases where applications experience fluctuations in input rate and workload. Addressing this challenge requires adjusting the optimal batch size at runtime. This study proposes a methodology for evaluating different self-adaptive micro-batching strategies in a real-world complex streaming application used as a benchmark.
This research has been supported by the Italian Resilience and Recovery Plan (PNRR) through the National Center for HPC, Big Data and Quantum Computing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aldinucci, M., Danelutto, M., Kilpatrick, P., Torquati, M.: Fastflow: high-level and efficient streaming on multicore, chap. 13, pp. 261–280. John Wiley & Sons, Ltd. (2017)
Araujo, G.A.d, et al.: Data and stream parallelism optimizations on GPUs. Master’s thesis, Pontifícia Universidade Católica do Rio Grande do Sul (2022)
Cheng, D., Zhou, X., Wang, Y., Jiang, C.: Adaptive scheduling parallel jobs with dynamic batching in spark streaming. IEEE Trans. Parallel Distrib. Syst. 29(12), 2672–2685 (2018)
Das, T., Zhong, Y., Stoica, I., Shenker, S.: Adaptive stream processing using dynamic batch sizing. In: Proceedings of the ACM Symposium on Cloud Computing, SOCC 2014, pp. 1–13. Association for Computing Machinery, New York (2014)
De Matteis, T., Mencagli, G., De Sensi, D., Torquati, M., Danelutto, M.: Gasser: An auto-tunable system for general sliding-window streaming operators on gpus. IEEE Access 7, 48753–48769 (2019)
Garcia, A.M., Griebler, D., Schepke, C., Fernandes, L.G.L.: Evaluating micro-batch and data frequency for stream processing applications on multi-cores. In: 2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 10–17. IEEE (2022)
Rockenbach, D.A., et al.: Stream processing on multi-cores with gpus: parallel programming models’ challenges. In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 834–841 (2019)
Rockenbach, D.A.: High-level programming abstractions for stream parallelism on gpus. Master’s thesis, Pontifícia Universidade Católica do Rio Grande do Sul (2020)
Stein, C.M., et al.: Latency-aware adaptive micro-batching techniques for streamed data compression on graphics processing units. 33, 5786 (2021)
Venkataraman, S., et al: Drizzle: fast and adaptable stream processing at scale. In: Proceedings of the 26th Symposium on Operating Systems Principles, SOSP 2017, pp. 374–389. Association for Computing Machinery, New York (2017)
Vogel, A., Griebler, D., Danelutto, M., Fernandes, L.G.: Self-adaptation on parallel stream processing: a systematic review. Concur. Comput. Pract. Exper. 34(6), e6759 (2022)
Zhang, Q., Song, Y., Routray, R.R., Shi, W.: Adaptive block and batch sizing for batched stream processing system. In: 2016 IEEE International Conference on Autonomic Computing (ICAC), pp. 35–44 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Leonarczyk, R., Griebler, D., Mencagli, G., Danelutto, M. (2024). Evaluation of Adaptive Micro-batching Techniques for GPU-Accelerated Stream Processing. In: Zeinalipour, D., et al. Euro-Par 2023: Parallel Processing Workshops. Euro-Par 2023. Lecture Notes in Computer Science, vol 14351. Springer, Cham. https://doi.org/10.1007/978-3-031-50684-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-50684-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50683-3
Online ISBN: 978-3-031-50684-0
eBook Packages: Computer ScienceComputer Science (R0)