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TurboGraph++: A Scalable and Fast Graph Analytics System

Title
TurboGraph++: A Scalable and Fast Graph Analytics System
Authors
SEONGYUN, KOHAN, WOOK SHIN
POSTECH Authors
HAN, WOOK SHIN
Date Issued
Jun-2018
Publisher
ACM
Abstract
Existing distributed graph analytics systems are categorized into two main groups: those that focus on efficiency with a risk of out-of-memory error and those that focus on scale-up with a fixed memory budget and a sacrifice in performance. While the former group keeps a partitioned graph resident in memory of each machine and uses an in-memory processing technique, the latter stores the partitioned graph in external memory of each machine and exploits a streaming processing technique. Gemini and Chaos are the state-of-the-art distributed graph systems in each group, respectively. We present TurboGraph++, a scalable and fast graph analytics system which efficiently processes large graphs by exploiting external memory for scale-up without compromising efficiency. First, TurboGraph++ provides a new graph processing abstraction for efficiently supporting neighborhood analytics that requires processing multi-hop neighborhoods of vertices, such as triangle counting and local clustering coefficient computation, with a fixed memory budget. Second, TurboGraph++ provides a balanced and buffer-aware partitioning scheme for ensuring balanced workloads across machines with reasonable cost. Lastly, TurboGraph++ leverages three-level parallel and overlapping processing for fully utilizing three hardware resources, CPU, disk, and network, in a cluster. Extensive experiments show that TurboGraph++ is designed to scale well to very large graphs, like Chaos, while its performance is comparable to Gemini.
URI
http://oasis.postech.ac.kr/handle/2014.oak/92216
DOI
10.1145/3183713.3196915
ISSN
0730-8078
Article Type
Article
Citation
Proceedings of the ACM SIGMOD International Conference on Management of Data, page. 395 - 410, 2018-06
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 HAN, WOOK SHIN
Dept. of Creative IT Engin.
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