TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance-Reference-Cited by-同舟云学术

TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance

Published:2021-07 Issue:4 Volume:36 Page:778-791
ISSN:1000-9000
Container-title:Journal of Computer Science and Technology
language:en
Short-container-title:J. Comput. Sci. Technol.

Author:

Niu Songjie,Chen Shimin

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Computer Science Applications,Hardware and Architecture,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11390-021-1356-2.pdf

Reference33 articles.

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2. Gonzalez J E, Low Y, Gu H, Bickson D, Guestrin C. PowerGraph: Distributed graph-parallel computation on natural graphs. In Proc. the 10th USENIX Symposium on Operating Systems Design and Implementation, October 2012, pp.17-30.

3. Xin R S, Gonzalez J E, Franklin M J, Stoica I. GraphX: A resilient distributed graph system on Spark. In Proc. the 1st International Workshop on Graph Data Management Experiences and Systems, June 2013, Article No. 2. https://doi.org/10.1145/2484425.2484427.

4. Niu S, Chen S. Optimizing CPU cache performance for Pregel-like graph computation. In Proc. the 31st IEEE International Conference on Data Engineering Workshops, April 2015, pp.149-154. https://doi.org/10.1109/ICDEW.2015.7129568.

5. Han M, Daudjee K, Ammar K, Özsu M T, Wang X, Jin T. An experimental comparison of Pregel-like graph processing systems. Proc. VLDB Endow., 2014, 7(12): 1047-1058. https://doi.org/10.14778/2732977.2732980.