DC Field | Value | Language |
---|---|---|
dc.contributor.author | SEONGYUN, KO | - |
dc.contributor.author | HAN, WOOK SHIN | - |
dc.date.accessioned | 2018-09-03T00:51:51Z | - |
dc.date.available | 2018-09-03T00:51:51Z | - |
dc.date.created | 2018-08-15 | - |
dc.date.issued | 2018-06 | - |
dc.identifier.issn | 0730-8078 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/92216 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | ACM | - |
dc.relation.isPartOf | Proceedings of the ACM SIGMOD International Conference on Management of Data | - |
dc.title | TurboGraph++: A Scalable and Fast Graph Analytics System | - |
dc.type | Article | - |
dc.identifier.doi | 10.1145/3183713.3196915 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Proceedings of the ACM SIGMOD International Conference on Management of Data, pp.395 - 410 | - |
dc.identifier.wosid | 000460373700027 | - |
dc.citation.endPage | 410 | - |
dc.citation.startPage | 395 | - |
dc.citation.title | Proceedings of the ACM SIGMOD International Conference on Management of Data | - |
dc.contributor.affiliatedAuthor | SEONGYUN, KO | - |
dc.contributor.affiliatedAuthor | HAN, WOOK SHIN | - |
dc.identifier.scopusid | 2-s2.0-85048803929 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.scptc | 0 | * |
dc.date.scptcdate | 2018-09-244 | * |
dc.description.isOpenAccess | N | - |
dc.type.docType | Proceedings Paper | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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