Empirical Guide to Use of Persistent Memory for Large-Scale In-Memory Graph Analysis
- Authors
- Bae, Hanyeoreum; Kwon, Miryeong; Gouk, Donghyun; Han, Sanghyun; Koh, Sungjoon; Lee, Changrim; Park, Dongchul; Jung, Myoungsoo
- Issue Date
- Dec-2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors, v.2021-October, pp 316 - 320
- Pages
- 5
- Journal Title
- Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors
- Volume
- 2021-October
- Start Page
- 316
- End Page
- 320
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/145988
- DOI
- 10.1109/ICCD53106.2021.00057
- ISSN
- 1063-6404
2576-6996
- Abstract
- We investigate runtime environment characteristics and explore the challenges of conventional in-memory graph processing. This system-level analysis includes empirical results and observations, which are opposite to the existing expectations of graph application users. Specifically, since raw graph data are not the same as the in-memory graph data, processing a billion-scale graph exhausts all system resources and makes the target system unavailable due to out-of-memory at runtime.To address a lack of memory space problem for big-scale graph analysis, we configure real persistent memory devices (PMEMs) with different operation modes and system software frameworks. In this work, we introduce PMEM to a representative in-memory graph system, Ligra, and perform an in-depth analysis uncovering the performance behaviors of different PMEM-applied in-memory graph systems. Based on our observations, we modify Ligra to improve the graph processing performance with a solid level of data persistence. Our evaluation results reveal that Ligra, with our simple modification, exhibits 4.41× and 3.01× better performance than the original Ligra running on a virtual memory expansion and conventional persistent memory, respectively. © 2021 IEEE.
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