DStream: A Streaming-Based Highly Parallel IFDS Framework.
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| Title: | DStream: A Streaming-Based Highly Parallel IFDS Framework. |
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| Authors: | Wang, Xizao1 wangxiz@smail.nju.edu.cn, Zuo, Zhiqiang1 zqzuo@nju.edu.cn, Bu, Lei1 bulei@nju.edu.cn, Zhao, Jianhua1 zhaojh@nju.edu.cn |
| Source: | ICSE: International Conference on Software Engineering. 2023, p2488-2500. 13p. |
| Subjects: | Streaming technology, Parallel computers, Data flow computing, Computer programming, Scalability |
| Abstract: | The IFDS framework supports interprocedural dataflow analysis with distributive flow functions over finite domains. A large class of interprocedural dataflow analysis problems can be formulated as IFDS problems and thus can be solved with the IFDS framework precisely. Unfortunately, scaling IFDS analysis to large-scale programs is challenging in terms of both massive memory consumption and low analysis efficiency. This paper presents DStream, a scalable system dedicated to precise and highly parallel IFDS analysis for large-scale programs. DStream leverages a streaming-based out-of-core computation model to reduce memory footprint significantly and adopts fine-grained data parallelism to achieve efficiency. We implemented a taint analysis as a DStream instance analysis and compared DStream with three state-of-the-art tools. Our experiments validate that DStream outperforms all other tools with average speedups from 4.37x to 14.46x on a commodity PC with limited available memory. Meanwhile, the experiments confirm that DStream successfully scales to large-scale programs which the state-of-the-art tools (e.g., FlowDroid and/or DiskDroid) fail to analyze. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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