GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Processing
Recently, research communities highlight the neces-sity of formulating a scalability continuum for large-scale graph processing, which gains the scale-out benefits from distributed graph systems, and the scale-up benefits from high-performance accelerators. To this end, we propose a middleware, call...
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| Vydané v: | Data engineering s. 2682 - 2694 |
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01.05.2022
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| ISSN: | 2375-026X |
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| Abstract | Recently, research communities highlight the neces-sity of formulating a scalability continuum for large-scale graph processing, which gains the scale-out benefits from distributed graph systems, and the scale-up benefits from high-performance accelerators. To this end, we propose a middleware, called the GX-plug, for the ease of integrating the merits of both. As a middleware, the GX-plug is versatile in supporting different runtime environments, computation models, and programming models. More, for improving the middleware performance, we study a series of techniques, including pipeline shuffle, synchro-nization caching and skipping, and workload balancing, for intra-, inter-, and beyond-iteration optimizations, respectively. Exper-iments show that our middleware efficiently plugs accelerators to representative distributed graph systems, e.g., GraphX and Powergraph, with up-to 20x acceleration ratio. |
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| AbstractList | Recently, research communities highlight the neces-sity of formulating a scalability continuum for large-scale graph processing, which gains the scale-out benefits from distributed graph systems, and the scale-up benefits from high-performance accelerators. To this end, we propose a middleware, called the GX-plug, for the ease of integrating the merits of both. As a middleware, the GX-plug is versatile in supporting different runtime environments, computation models, and programming models. More, for improving the middleware performance, we study a series of techniques, including pipeline shuffle, synchro-nization caching and skipping, and workload balancing, for intra-, inter-, and beyond-iteration optimizations, respectively. Exper-iments show that our middleware efficiently plugs accelerators to representative distributed graph systems, e.g., GraphX and Powergraph, with up-to 20x acceleration ratio. |
| Author | Kong, Deyu Xie, Xike Li, Qi Zou, Kai |
| Author_xml | – sequence: 1 givenname: Kai surname: Zou fullname: Zou, Kai email: slnt@mail.ustc.edu.cn organization: University of Science and Technology of China,Data Darkness Lab – sequence: 2 givenname: Xike surname: Xie fullname: Xie, Xike email: xkxie@ustc.edu.cn organization: University of Science and Technology of China,Data Darkness Lab – sequence: 3 givenname: Qi surname: Li fullname: Li, Qi email: likamo@mail.ustc.edu.cn organization: University of Science and Technology of China,Data Darkness Lab – sequence: 4 givenname: Deyu surname: Kong fullname: Kong, Deyu email: cavegf@mail.ustc.edu.cn organization: University of Science and Technology of China,Data Darkness Lab |
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| SubjectTerms | accelerators Computational modeling Data engineering Distributed graph systems Middleware Pipelines Programming Runtime environment Scalability Synchronization |
| Title | GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Processing |
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