High-Performance Computing Challenges
Abstract
Driven by the need for high processing capabilities required by Big Data, Machine Learning and Artificial Intelligence algorithms, High-Performance Computing has rapidly become one of the most active computer science fields. The state-of-art algorithms from these fields are notoriously demanding in terms of computer resources. Choosing the right computer system to optimize their performance is paramount. This paper presents the main challenges on future computer architectures, heterogeneous systems and quantum computing.
Keywords:
High-performance Computing, Heterogeneous Architectures, Energy Consumption, Machine Learning, Big Data
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Barney, B. (2009). POSIX threads programming. National Laboratory. https://computing.llnl.gov/tutorials/pthreads [Acesso em: 4 Mai. 2021].
Chandra, R., Dagum, L., Kohr, D., Menon, R., Maydan, D., & McDonald, J. (2001). Parallel programming in OpenMP. Morgan kaufmann.
Cook, S. (2012). CUDA programming: a developer's guide to parallel computing with GPUs. Newnes.
Cox M., Ellsworth D. (1997) “Managing Big Data for Scientific Visualization” Conference on Visualization ’97. VIS ’97.
Cruz E., Diener M., Pilla L., Navaux P. (2021) “Online Thread and Data Mapping Using a Sharing-Aware Memory Management Unit” ACM Transactions on Modeling and Performance Evaluation of Computing Systems January 2021, Vol. 5 No. 4 Article.
Davila G. P. , Oliveira D. A. G. , Navaux P. O, A, , Rech P. : Identifying the Most Reliable Collaborative Workload Distribution in Heterogeneous Devices. DATE 2019: 1325-1330
Desjardins J. (2019). How much data is generated each day? https://www.visualcapitalist.com/how-much-data-is-generated-each-day/. [Acesso em: 12 Mar. 2021].
Diaz, J., Munoz-Caro, C., & Nino, A. (2012). A survey of parallel programming models and tools in the multi and many-core era. IEEE Transactions on parallel and distributed systems, 23(8), 1369-1386.
Dongarra J., H. M. and Strohmaier, E. (2020). Top500 supercomputer: November 2020. https://www.top500.org/lists/top500/2020/11/. [Acesso em: 10 Mar. 2021].
Farber, R. (2016). Parallel programming with OpenACC. Newnes.
Frontier (2021). ORNL Exascale Supercomputer. https://www.olcf.ornl.gov/frontier/ [Acesso em: 10 Abr. 2021].
Fujitsu (2021). Supercomputer Fugaku. https://www.fujitsu.com/. [Acesso em: 10 Abr. 2021].
Gabriel, E., Fagg, G. E., Bosilca, G., Angskun, T., Dongarra, J. J., Squyres, J. M., ... & Woodall, T. S. (2004, September). Open MPI: Goals, concept, and design of a next generation MPI implementation. In European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting (pp. 97-104). Springer, Berlin, Heidelberg.
Hendrickx N., Lawrie W., Russ M., Riggelen F., Snoo S., Schouten R., Sammak A., Scappucci G., Veldhorst M. (2021) “A four-qubit germanium quantum processor”. Nature, 2021; 591 (7851): 580 DOI: 10.1038/s41586-021-03332-6.
Kooge P. & All (2008) “ExaScale Computing Study: Technology Challenges in Achieving Exascale Systems” Defense Advanced Research Projects Agency Information Processing Techniques Office, Tech. Rep.
Munshi, A., Gaster, B., Mattson, T. G., & Ginsburg, D. (2011). OpenCL programming guide. Pearson Education.
Padoin E. L., Diener M., Navaux P. O. A. , Méhaut J. F. : Managing Power Demand and Load Imbalance to Save Energy on Systems with Heterogeneous CPU Speeds. SBAC-PAD 2019: 72-79
Schardl, T. B., Lee, I. T. A., & Leiserson, C. E. (2018, July). Brief announcement: Open cilk. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures (pp. 351-353).
Serpa, M. S., Moreira, F. B., Navaux, P. O., Cruz, E. H., Diener, M., Griebler, D., & Fernandes, L. G. (2019). Memory Performance and Bottlenecks in Multicore and GPU Architectures. In 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (pp. 233-236). IEEE.
Stevens R., Nichols J., Yelick K., Helland B., Leads C. (2019) “AI for Science” Report on the Department of Energy (DOE).
Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2020). A survey on distributed machine learning. ACM Computing Surveys (CSUR), 53(2), 1-33.
Vetter J. and all (2018) “Extreme Heterogeneity 2018: Productive Computational Science in the Era of Extreme Heterogeneity” Report for DOE ASCR Basic Research Needs Workshop on Extreme Heterogeneity.
Published
2021-07-18
How to Cite
NAVAUX, Philippe Olivier Alexandre; SERPA, Matheus da Silva.
High-Performance Computing Challenges. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 48. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 39-49.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2021.15805.
