The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Optimistic parallelism requires abstractions
Abstract
Irregular applications, which manipulate large, pointer-based data structures like graphs, are difficult to parallelize manually. Automatic tools and techniques such as restructuring compilers and run-time speculative execution have failed to uncover much parallelism in these applications, in spite of a lot of effort by the research community. These difficulties have even led some researchers to wonder if there is any coarse-grain parallelism worth exploiting in irregular applications.
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Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator
Shewchuk, J. R. (1996) · Lecture Notes in Computer Science · doi:10.1007/bfb0014497 · Cited by 2,115
Cited by
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Bocchino, R. L., Adve, V. S., Dig, D., Adve, S. V., Heumann, S., Komuravelli, R., Overbey, J., Simmons, P., Sung, H., & Vakilian, M. (2009) · Proceedings of the 24th ACM SIGPLAN conference on Object oriented programming systems languages and applications · doi:10.1145/1640089.1640097 · Cited by 265
PaSh: light-touch data-parallel shell processing
Vasilakis, N., Kallas, K., Mamouras, K., Benetopoulos, A., & Cvetković, L. (2021) · Proceedings of the Sixteenth European Conference on Computer Systems · doi:10.1145/3447786.3456228 · Cited by 17
Metadata
- Authors
- Kulkarni, M., Pingali, K., Walter, B., Ramanarayanan, G., Bala, K., & Chew, L. P.
- Venue
- Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation
- Publisher
- ACM
- Published
- 2007-06-10
- Pages
- 211-222
- ISSN
- 1531-7102 (ISSN-L)
- OpenAlex
- W2097717378
- Cited by
- 441
- References
- 1
- FWCI
- 28.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kulkarni, M., Pingali, K., Walter, B., Ramanarayanan, G., Bala, K., & Chew, L. P. (2007). Optimistic parallelism requires abstractions. Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation. https://doi.org/10.1145/1250734.1250759
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.