The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A lightweight infrastructure for graph analytics
Open accessAbstract
Several domain-specific languages (DSLs) for parallel graph analytics have been proposed recently. In this paper, we argue that existing DSLs can be implemented on top of a general-purpose infrastructure that (i) supports very fine-grain tasks, (ii) implements autonomous, speculative execution of these tasks, and (iii) allows application-specific control of task scheduling policies. To support this claim, we describe such an implementation called the Galois system.
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Cites
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A faster algorithm for betweenness centrality*
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A Bridging Model for Parallel Computation
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Pregel: a system for large-scale graph processing
Malewicz, G., Austern, M. H., Bik, A. J. C., Dehnert, J. C., Horn, I., Leiser, N., & Czajkowski, G. (2010) · Proceedings of the 2010 ACM SIGMOD International Conference on Management of data · doi:10.1145/1807167.1807184 · Cited by 3,515
Measuring user influence in twitter: The million follower fallacy
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“The twitter40 [16] and twitter50 [6] graphs are real-world social network graphs.”
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GraphChi: Large-Scale Graph Computation on Just a PC
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Cited by
McCune, R. R., Weninger, T., & Madey, G. (2015) · ACM Computing Surveys · doi:10.1145/2818185 · Cited by 294
GraphMat: high performance graph analytics made productive
Sundaram, N., Satish, N., Patwary, M. M. A., Dulloor, S. R., Anderson, M. J., Vadlamudi, S. G., Das, D., & Dubey, P. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2809974.2809983 · Cited by 282
PrismX: A Single-Machine System for Querying Big Graphs
Liu, S., Liu, Y., & Fan, W. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3685800.3685906 · Cited by 2
Metadata
- Authors
- Nguyen, D., Lenharth, A., & Pingali, K.
- Venue
- Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
- Publisher
- ACM
- Published
- 2013-11-03
- Pages
- 456-471
- OpenAlex
- W2034102265
- Cited by
- 579
- References
- 19
- FWCI
- 23.47
- Type
- conference paper
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
