Affiliation:
1. University of Texas at Austin
2. Intel Corporation
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.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
35 articles.
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1. DGAP: Efficient Dynamic Graph Analysis on Persistent Memory;Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis;2023-11-11
2. Persistent Memory Research in the Post-Optane Era;Proceedings of the 1st Workshop on Disruptive Memory Systems;2023-10-23
3. Rethinking Design Paradigm of Graph Processing System with a CXL-like Memory Semantic Fabric;2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid);2023-05
4. MiniGraph: Querying Big Graphs with a Single Machine;Proceedings of the VLDB Endowment;2023-05
5. Power-aware Computing with Optane Persistent Memory Modules;2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2023-05