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OpenMP: an industry standard API for shared-memory programming
Abstract
At its most elemental level, OpenMP is a set of compiler directives and callable runtime library routines that extend Fortran (and separately, C and C++ to express shared memory parallelism. It leaves the base language unspecified, and vendors can implement OpenMP in any Fortran compiler. Naturally, to support pointers and allocatables, Fortran 90 and Fortran 95 require the OpenMP implementation to include additional semantics over Fortran 77. OpenMP leverages many of the X3H5 concepts while extending them to support coarse grain parallelism. The standard also includes a callable runtime library with accompanying environment variables.
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Cited by
Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-26 · Cited by 5,098
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial
Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014) · IEEE Access · doi:10.1109/access.2014.2332453 · Cited by 1,099
Pareto-Efficient Quantum Circuit Simulation Using Tensor Contraction Deferral
Pednault, E., Gunnels, J. A., Nannicini, G., Horesh, L., Magerlein, T., Solomonik, E., Draeger, E. W., Holland, E. T., & Wisnieff, R. (2017) · arXiv · doi:10.48550/arxiv.1710.05867 · Cited by 48
Data-driven reverse engineering of signaling pathways using ensembles of dynamic models
Henriques, D., Villaverde, A. F., Rocha, M., Saez-Rodriguez, J., & Banga, J. R. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005379 · Cited by 44
DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors
Tabanelli, E., Tagliavini, G., & Benini, L. (2021) · arXiv · doi:10.48550/arxiv.2107.09448 · Cited by 36
Juhasz, Z., Ďurian, J., Derzsi, A., Matejčík, Š., Donkó, Z., & Hartmann, P. (2021) · Computer Physics Communications · doi:10.1016/j.cpc.2021.107913 · Cited by 35
A federated learning architecture for secure and private neuroimaging analysis
Stripelis, D., Gupta, U., Saleem, H., Dhinagar, N., Ghai, T., Anastasiou, C., Sánchez, R., Steeg, G. V., Ravi, S., Naveed, M., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101031 · Cited by 31
MergeShuffle: A Very Fast, Parallel Random Permutation Algorithm
Bacher, A., Bodini, O., Hollender, A., & Lumbroso, J. (2015) · arXiv · doi:10.48550/arxiv.1508.03167 · Cited by 18
High-performance dynamic quantum clustering on graphics processors
Wittek, P. (2013) · Journal of Computational Physics · doi:10.1016/j.jcp.2012.08.048 · Cited by 16
Language Extensions in Support of Compiler Parallelization
Shirako, J., Kasahara, H., & Sarkar, V. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-85261-2_6 · Cited by 16
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
Olechnovič, K., & Grudinin, S. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70178 · Cited by 3
Metadata
- Authors
- Dagum, L., & Menon, R.
- Venue
- IEEE Computational Science and Engineering
- Published
- 1998
- Volume
- 5
- Issue
- 1
- Pages
- 46-55
- ISSN
- 1070-9924 (ISSN-L)
- OpenAlex
- W1988888548
- Cited by
- 3,343
- References
- 0
- FWCI
- 26.30
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
