Sage: parallel semi-asymmtric graph algorithms for NVRAMs

Dhulipala, Laxman; McGuffey, Charles; Kang, Hongbo; Gu, Yan; Blelloch, Guy E.; Gibbons, Phillip B.; Shun, Julian

doi:10.14778/3397230.3397251

Sage: parallel semi-asymmtric graph algorithms for NVRAMs

Journal Article · Fri Jun 26 04:00:00 EDT 2020 · Proceedings of the VLDB Endowment

DOI:https://doi.org/10.14778/3397230.3397251· OSTI ID:1803480

Dhulipala, Laxman ^[1]; McGuffey, Charles ^[2]; Kang, Hongbo ^[3]; Gu, Yan ^[4]; Blelloch, Guy E. ^[2]; Gibbons, Phillip B. ^[2]; Shun, Julian ^[5]

Carnegie Mellon Univ., Pittsburgh, PA (United States); OSTI
Carnegie Mellon Univ., Pittsburgh, PA (United States)
Tsinghua Univ., Beijing (China)
Univ. of California, Riverside, CA (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Computer Science and Artificial Intelligence Lab. (CSAIL)

Non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, including byte-addressability, low idle power, and improved memory-density. NVRAM systems today have an order of magnitude more NVRAM than traditional memory (DRAM). NVRAM systems could therefore potentially allow very large graph problems to be solved on a single machine, at a modest cost. However, a significant challenge in achieving high performance is in accounting for the fact that NVRAM writes can be much more expensive than NVRAM reads. In this paper, we propose an approach to parallel graph analytics using the Parallel Semi-Asymmetric Model (PSAM), in which the graph is stored as a read-only data structure (in NVRAM), and the amount of mutable memory is kept proportional to the number of vertices. Similar to the popular semi-external and semi-streaming models for graph analytics, the PSAM approach assumes that the vertices of the graph fit in a fast read-write memory (DRAM), but the edges do not. In NVRAM systems, our approach eliminates writes to the NVRAM, among other benefits. To experimentally study this new setting, we develop Sage, a parallel semi-asymmetric graph engine with which we implement provably-efficient (and often work-optimal) PSAM algorithms for over a dozen fundamental graph problems. We experimentally study Sage using a 48--core machine on the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) equipped with Optane DC Persistent Memory, and show that Sage outperforms the fastest prior systems designed for NVRAM. Importantly, we also show that Sage nearly matches the fastest prior systems running solely in DRAM, by effectively hiding the costs of repeatedly accessing NVRAM versus DRAM.

View Accepted Manuscript (DOE)

Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Science Foundation (NSF); Defense Advanced Research Projects Agency (DARPA)

Grant/Contract Number:: SC0018947

OSTI ID:: 1803480

Journal Information:: Proceedings of the VLDB Endowment, Journal Name: Proceedings of the VLDB Endowment Journal Issue: 9 Vol. 13; ISSN 2150-8097

Publisher:: Association for Computing Machinery (ACM)Copyright Statement

Country of Publication:: United States

Language:: English

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