Dynamic expressivity with static optimization for streaming languages
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Dynamic expressivity.pdf
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Author(s)
Gordon, Michael I.
Grimm, Robert
Hirzel, Martin
Soule, Robert
Amarasinghe, Saman P.
Date Issued
June 2013
Journal
Proceedings of the 7th ACM international conference on Distributed event-based systems (DEBS '13)
Publisher
Association for Computing Machinery (ACM)
Citation
Robert Soule, Michael I. Gordon, Saman Amarasinghe, Robert Grimm, and Martin Hirzel. 2013. Dynamic expressivity with static optimization for streaming languages. In Proceedings of the 7th ACM international conference on Distributed event-based systems (DEBS '13). ACM, New York, NY, USA, 159-170.
Version
Author's final manuscript
Abstract
Developers increasingly use streaming languages to write applications that process large volumes of data with high throughput. Unfortunately, when picking which streaming language to use, they face a difficult choice. On the one hand, dynamically scheduled languages allow developers to write a wider range of applications, but cannot take advantage of many crucial optimizations. On the other hand, statically scheduled languages are extremely performant, but have difficulty expressing many important streaming applications.
This paper presents the design of a hybrid scheduler for stream processing languages. The compiler partitions the streaming application into coarse-grained subgraphs separated by dynamic rate boundaries. It then applies static optimizations to those subgraphs. We have implemented this scheduler as an extension to the StreamIt compiler. To evaluate its performance, we compare it to three scheduling techniques used by dynamic systems (OS thread, demand, and no-op) on a combination of micro-benchmarks and real-world inspired synthetic benchmarks. Our scheduler not only allows the previously static version of StreamIt to run dynamic rate applications, but it outperforms the three dynamic alternatives. This demonstrates that our scheduler strikes the right balance between expressivity and performance for stream processing languages.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2488222.2488255
