Proceedings Article10.1145/3168817
Scalable concurrency debugging with distributed graph processing
Long Zheng,Xiaofei Liao,Hai Jin,Jieshan Zhao,Qinggang Wang +4 more
- 24 Feb 2018
- pp 188-199
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TL;DR: GraphDebugger is presented, a novel debugging framework to enable the scalable concurrency analysis on program graphs via a tailored graph-parallel analysis in a distributed environment and is verified that it is more capable than CLAP in reproducing the real-world bugs that involve a complex concurrencyAnalysis.
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Abstract: Existing constraint-solving-based technique enables an efficient and high-coverage concurrency debugging. Yet, there remains a significant gap between the state of the art and the state of the programming practices for scaling to handle long-running execution of programs. In this paper, we revisit the scalability problem of state-of-the-art constraint-solving-based technique. Our key insight is that concurrency debugging for many real-world bugs can be turned into a graph traversal problem. We therefore present GraphDebugger, a novel debugging framework to enable the scalable concurrency analysis on program graphs via a tailored graph-parallel analysis in a distributed environment. It is verified that GraphDebugger is more capable than CLAP in reproducing the real-world bugs that involve a complex concurrency analysis. Our extensive evaluation on 7 real-world programs shows that, GraphDebugger (deployed on an 8-node EC2 like cluster) is significantly efficient to reproduce concurrency bugs within 1∼8 minutes while CLAP does so with 1∼30 hours, or even without returning solutions.
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A Survey on Graph Processing Accelerators: Challenges and Opportunities
TL;DR: In this article, the authors conduct a systematical survey regarding the design and implementation of graph processing accelerators and present and discuss several challenges in details, and further explore the opportunities for the future research.
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A Survey on Graph Processing Accelerators: Challenges and Opportunities
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A Conflict-free Scheduler for High-performance Graph Processing on Multi-pipeline FPGAs
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Fast Triangle Counting on GPU
Chuangyi Gui,Long Zheng,Pengcheng Yao,Xiaofei Liao,Hai Jin +4 more
- 01 Sep 2019
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