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Data parallel algorithms
Open accessAbstract
Parallel computers with tens of thousands of processors are typically programmed in a data parallel style, as opposed to the control parallel style used in multiprocessing. The success of data parallel algorithms—even on problems that at first glance seem inherently serial—suggests that this style of programming has much wider applicability than was previously thought.
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Citations
Cited by
Accelerating the XGBoost algorithm using GPU computing
Mitchell, R., & Frank, E. (2017) · PeerJ Computer Science · doi:10.7717/peerj-cs.127 · Cited by 359
Synthesis of divide and conquer parallelism for loops
Farzan, A., & Nicolet, V. (2017) · Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3062341.3062355 · Cited by 25
Modular divide-and-conquer parallelization of nested loops
Farzan, A., & Nicolet, V. (2019) · Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3314221.3314612 · Cited by 16
Metadata
- Authors
- Hillis, W. D., & Steele, G. L.
- Venue
- Communications of the ACM
- Published
- 1986-12
- Volume
- 29
- Issue
- 12
- Pages
- 1170-1183
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W1967810725
- Cited by
- 899
- References
- 0
- FWCI
- 31.62
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Data parallel algorithms. https://app.cochat.ai/papers/10.1145/7902.7903
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- Hillis, W. D., & Steele, G. L. (1986). Data parallel algorithms. Communications of the ACM. https://doi.org/10.1145/7902.7903
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
