Large-Scale Graph Processing on Emerging Storage Devices


Nima Elyasi, The Pennsylvania State University; Changho Choi, Samsung Semiconductor Inc.; Anand Sivasubramaniam, The Pennsylvania State University


Graph processing is becoming commonplace in many applications to analyze huge datasets. Much of the prior work in this area has assumed I/O devices with considerable latencies, especially for random accesses, using large amount of DRAM to trade-off additional computation for I/O accesses. However, emerging storage devices, including currently popular SSDs, provide fairly comparable sequential and random accesses, making these prior solutions inefficient. In this paper, we point out this inefficiency, and propose a new graph partitioning and processing framework to leverage these new device capabilities. We show experimentally on an actual platform that our proposal can give 2X better performance than a state-of-the-art solution.

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@inproceedings {227788,
author = {Nima Elyasi and Changho Choi and Anand Sivasubramaniam},
title = {{Large-Scale} Graph Processing on Emerging Storage Devices},
booktitle = {17th USENIX Conference on File and Storage Technologies (FAST 19)},
year = {2019},
isbn = {978-1-939133-09-0},
address = {Boston, MA},
pages = {309--316},
url = {},
publisher = {USENIX Association},
month = feb

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