Bulletin of Chinese Academy of Sciences (Chinese Version)
Keywords
emerging application areas; data-driven; data-intensive computing; high performance computing (HPC)
Document Type
Article
Abstract
The rapid development of data-intensive emerging application areas in recent years is one of major characteristics of the extensive and in-depth applications of high performance computing (HPC). The HPC applications in the emerging areas bring new challenges and opportunities at all levels of HPC, including system technological innovation, computing environment innovation, and application innovation. Based on reviewing the application progress of HPC in the emerging areas, this paper summarizes the current technical characteristics and challenges, and provides strategic recommendations for the development of emerging HPC application areas, including increasing the core technology innovation of HPC systems, building HPC environment for emerging application areas, promoting the development of HPC application software and new methods for traditional applications, as well as promoting the development of benchmarking tools in new areas such as big data and artificial intelligence.
First page
640
Last Page
647
Language
Chinese
Publisher
Bulletin of Chinese Academy of Sciences
References
Reinsel D, 武连峰, Gantz J F, et al. IDC:2025年中国将拥有全球最大的数据圈. Framingham:IDC, 2019.
Libbrecht M W, Noble W S. Machine learning applications in genetics and genomics. Nature Reviews Genetics, 2015, 16(6):321-332.
Wang J X, Cao H L, Zhang J Z, et al. Computational protein design with deep learning neural networks. Scientific Reports, 2018, 8(1):6349.
Jordan J, Ippen T, Helias M, et al. Extremely scalable spiking neuronal network simulation code:from laptops to exascale computers. Frontiers in Neuroinformatics, 2018, 12(2):1-21.
Damodaran S K, Couretas J M. Cyber modeling & simulation for cyber-range events//Proceedings of the Conference on Summer Computer Simulation. San Diego: Society for Computer Simulation International, 2015: 1-8.
Erfani S M, Rajasegarar S, Karunasekera S, et al. Highdimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognition, 2016, 58:121-134.
McGovern A, Elmore K L, Gagne D J, et al. Using artificial intelligence to improve real-time decision-making for highimpact weather. Bulletin of the American Meteorological Society, 2017, 98(10):2073-2090.
Kurth T, Treichler S, Romero J, et al. Exascale deep learning for climate analytics//Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC). Dallas: IEEE Press, 2018: 649-660.
Jouppi N P, Young C, Patil N, et al. In-datacenter performance analysis of a tensor processing unit//ISCA'17 Proceedings of the 44th Annual International Symposium on Computer Architecture. Toronto: IEEE Press, 2017: 1-12.
Vanderbauwhede W, Benkrid K. High-performance Computing Using FPGAs. New York:Springer, 2013.
Recommended Citation
Shengzhong, FENG; Genguo, LI; Xuelei, LI; Fumin, QI; Dian, HUANG; Yi, WAN; and Jincheng, WU
(2019)
"Development Strategy of Emerging Applications of HPC,"
Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 34
:
Iss.
6
, Article 4.
DOI: https://doi.org/10.16418/j.issn.1000-3045.2019.06.005
Available at:
https://bulletinofcas.researchcommons.org/journal/vol34/iss6/4