Performance assessment of OpenMP constructs and benchmarks using modern compilers and multi-core CPUs

• Autorzy: Gawrych Bartłomiej , Czarnul Pawel

Identyfikatory

DOI

10.15439/2023F7822

• Języki publikacji: EN

Abstrakty

EN

Considering ongoing developments of both modern CPUs, especially in the context of increasing numbers of cores, cache memory and architectures as well as compilers there is a constant need for benchmarking representative and frequently run workloads. The key metric is speed-up as the computational power of modern CPUs stems mainly from using multiple cores. In this paper, we show and discuss results from running codes such as: batch normalization, convolution, linear function, matrix multiplication, prime number test and wave equation; using compilers such as: GNU gcc, LLVM clang, icx, icc; run on four different 1 or 2-socket systems: 1 x Intel Core i7-5960X, 1 x Intel Core i9-9940X, 2 x Intel Xeon Platinum 8280L, 2 x Intel Xeon Gold 6130. Results can be regarded as suggestions concerning scaling on particular CPUs including recommended thread number configurations.

Słowa kluczowe

EN

measurement; context; gold; multicore processing; convolution; propagation; instruction set

PL

pomiar; kontekst; złoto; przetwarzanie wielordzeniowe; splot; propagacja

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 973--978
• Opis fizyczny: Bibliogr. 13 poz., tab., wykr.

Twórcy

autor

Gawrych Bartłomiej

• Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology Narutowicza 11/12, 80-233 Poland

autor

Czarnul Pawel

• Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology Narutowicza 11/12, 80-233 Poland

Bibliografia

• 1. P. Czarnul, Parallel Programming for Modern High Performance Computing Systems. CRC Press, Taylor & Francis, 2018, iSBN 9781138305953.
• 2. A. Prabhakar, V. Getov, and B. Chapman, “Performance comparisons of basic openmp constructs,” in High Performance Computing, H. P. Zima, K. Joe, M. Sato, Y. Seo, and M. Shimasaki, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. ISBN 978-3-540-47847-8 pp. 413–424.
• 3. Q. Luo, C. Kong, Y. Cai, and G. Liu, “Performance evaluation of openmp constructs and kernel benchmarks on a loongson-3a quadcore smp system,” in 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies, 2011. http://dx.doi.org/10.1109/PDCAT.2011.66 pp. 191–196.
• 4. N. R. Fredrickson, A. Afsahi, and Y. Qian, “Performance characteristics of openmp constructs, and application benchmarks on a large symmetric multiprocessor,” in Proceedings of the 17th Annual International Conference on Supercomputing, ser. ICS ’03. New York, NY, USA: Association for Computing Machinery, 2003. http://dx.doi.org/10.1145/782814.782835. ISBN 1581137338 p. 140–149. [Online]. Available: https://doi.org/10.1145/782814.782835
• 5. P. Czarnul, “Assessment of openmp master–slave implementations for selected irregular parallel applications,” Electronics, vol. 10, no. 10, 2021. http://dx.doi.org/10.3390/electronics10101188. [Online]. Available: https://www.mdpi.com/2079-9292/10/10/1188
• 6. K. Fürlinger and M. Gerndt, “Analyzing overheads and scalability characteristics of openmp applications,” in High Performance Computing for Computational Science - VECPAR 2006, M. Daydé, J. M. L. M. Palma, Á. L. G. A. Coutinho, E. Pacitti, and J. C. Lopes, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. ISBN 978-3-540-71351-7 pp. 39–51.
• 7. V. G. M. Vergara, W. D. Joubert, M. G. Lopez, and O. R. Hernandez, “Early experiences writing performance portable openmp 4 codes,” in Cray User Group Conference, London, United Kingdom, May 2016.
• 8. W. Zhu, J. del Cuvillo, and G. R. Gao, “Performance characteristics of openmp language constructs on a many-core-on-a-chip architecture,” in OpenMP Shared Memory Parallel Programming, M. S. Mueller, B. M. Chapman, B. R. de Supinski, A. D. Malony, and M. Voss, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. ISBN 978-3-540-68555-5 pp. 230–241.
• 9. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, ser. ICML’15. JMLR.org, 2015, p. 448–456.
• 10. B. Gawrych and P. Czarnul, “Performance investigation of openmp constructs and benchmarks using modern compilers and multi-core cpus,” September 2021, https://github.com/bgawrych/openmp_benchmark.
• 11. A. Krzywaniak, J. Proficz, and P. Czarnul, “Analyzing energy/performance trade-offs with power capping for parallel applications on modern multi and many core processors,” in 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), 2018, pp. 339–346.
• 12. P. Czarnul, P. Rościszewski, M. Matuszek, and J. Szymański, “Simulation of parallel similarity measure computations for large data sets,” in 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), 2015. http://dx.doi.org/10.1109/CYBConf.2015.7175980 pp. 472–477.
• 13. P. Czarnul, A. Ciereszko, and M. Frązak, “Towards efficient parallel image processing on cluster grids using gimp,” in Computational Science - ICCS 2004, M. Bubak, G. D. van Albada, P. M. A. Sloot, and J. Dongarra, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. ISBN 978-3-540-24687-9 pp. 451–458.

Uwagi

1. Thematic Tracks Short Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).