research-article
Authors: Gurbinder Gill, Roshan Dathathri, Loc Hoang, Ramesh Peri, and Keshav Pingali
Proceedings of the VLDB Endowment, Volume 13, Issue 8
Pages 1304 - 1318
Published: 01 April 2020 Publication History
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Abstract
Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable memory with higher density and lower cost than DRAM. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this paper, we present key runtime and algorithmic principles to consider when performing graph analytics on extreme-scale graphs on Optane PMM and highlight principles that can apply to graph analytics on all large-memory platforms.
To demonstrate the importance of these principles, we evaluate four existing shared-memory graph frameworks and one out-of-core graph framework on large real-world graphs using a machine with 6TB of Optane PMM. Our results show that frameworks using the runtime and algorithmic principles advocated in this paper (i) perform significantly better than the others and (ii) are competitive with graph analytics frameworks running on production clusters.
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Published In
Proceedings of the VLDB Endowment Volume 13, Issue 8
April 2020
172 pages
ISSN:2150-8097
- Editors:
- Magdalena Balazinska
University of Washington
, - Xiaofang Zhou
University of Queensland, Australia
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VLDB Endowment
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Published: 01 April 2020
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