TY - GEN
T1 - Rethinking high performance computing system architecture for scientific big data applications
AU - Chen, Yong
AU - Chen, Chao
AU - Yin, Yanlong
AU - Sun, Xian He
AU - Thakur, Rajeev
AU - Gropp, William
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - The increasingly important data-intensive scientific discovery presents a critical question to the high performance computing (HPC) community-how to efficiently support these growing scientific big data applications with HPC systems that are traditionally designed for big compute applications? The conventional HPC systems are computing-centric and designed for computation-intensive applications. Scientific big data applications have growlingly different characteristics compared to big compute applications. These scientific applications, however, will still largely rely on HPC systems to be solved. In this research, we try to answer this question with a rethinking of HPC system architecture. We study and analyze the potential of a new decoupled HPC system architecture for data-intensive scientific applications. The fundamental idea is to decouple conventional compute nodes and dynamically provision as data processing nodes that focus on data processing capability. We present studies and analyses for such decoupled HPC system architecture. The current results have shown its promising potential. Its data-centric architecture can have an impact in designing and developing future HPC systems for growingly important data-intensive scientific discovery and innovation.
AB - The increasingly important data-intensive scientific discovery presents a critical question to the high performance computing (HPC) community-how to efficiently support these growing scientific big data applications with HPC systems that are traditionally designed for big compute applications? The conventional HPC systems are computing-centric and designed for computation-intensive applications. Scientific big data applications have growlingly different characteristics compared to big compute applications. These scientific applications, however, will still largely rely on HPC systems to be solved. In this research, we try to answer this question with a rethinking of HPC system architecture. We study and analyze the potential of a new decoupled HPC system architecture for data-intensive scientific applications. The fundamental idea is to decouple conventional compute nodes and dynamically provision as data processing nodes that focus on data processing capability. We present studies and analyses for such decoupled HPC system architecture. The current results have shown its promising potential. Its data-centric architecture can have an impact in designing and developing future HPC systems for growingly important data-intensive scientific discovery and innovation.
UR - http://www.scopus.com/inward/record.url?scp=85015214917&partnerID=8YFLogxK
U2 - 10.1109/TrustCom.2016.0248
DO - 10.1109/TrustCom.2016.0248
M3 - Conference contribution
AN - SCOPUS:85015214917
T3 - Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
SP - 1605
EP - 1612
BT - Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
Y2 - 23 August 2016 through 26 August 2016
ER -