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.
|
Year | DOI | Venue |
---|---|---|
2018 | 10.1145/3183713.3196915 | SIGMOD/PODS '18: International Conference on Management of Data
Houston
TX
USA
June, 2018 |
Field | DocType | ISSN |
Data mining,Abstraction,Vertex (geometry),Computer science,Parallel computing,Exploit,Analytics,Clustering coefficient,Computation,Auxiliary memory,Scalability | Conference | 0730-8078 |
ISBN | Citations | PageRank |
978-1-4503-4703-7 | 4 | 0.41 |
References | Authors | |
32 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Seongyun Ko | 1 | 24 | 1.66 |
Wook-Shin Han | 2 | 805 | 57.85 |