TY - GEN
T1 - PFSI.sw
T2 - 28th IEEE International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2017
AU - Li, Binyang
AU - Li, Bo
AU - Qian, Depei
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - Sea ice model is a typical high performance computing problem. CPU and GPU based parallel method has been proposed to accelerate the simulation process, but it is still hard to meet the large-scale calculation demand due to the compute-intensive nature of the model. Sunway TaihuLight supercomputer use the SW26010 processor as its computing unit and achieves high performance for large-scale scientific computing. In this paper we present a programming framework (PFSI.sw) for sea ice model algorithms based on Sunway many-core processor. Based on this framework, programmer can exploit the parallelism of existing sea ice model algorithms and achieve good performance. Several strategies are introduced to this framework, data dividing, data transfer as well as the load balance are the main aspects we currently concerned. This framework has been implemented and tested with two sea ice model algorithms by using real world dataset on Sunway many-core processors. The experiment demonstrates comparable performance to the traditional parallel implementation on Sunway many-core processor and our framework improves the performance up to 40%.
AB - Sea ice model is a typical high performance computing problem. CPU and GPU based parallel method has been proposed to accelerate the simulation process, but it is still hard to meet the large-scale calculation demand due to the compute-intensive nature of the model. Sunway TaihuLight supercomputer use the SW26010 processor as its computing unit and achieves high performance for large-scale scientific computing. In this paper we present a programming framework (PFSI.sw) for sea ice model algorithms based on Sunway many-core processor. Based on this framework, programmer can exploit the parallelism of existing sea ice model algorithms and achieve good performance. Several strategies are introduced to this framework, data dividing, data transfer as well as the load balance are the main aspects we currently concerned. This framework has been implemented and tested with two sea ice model algorithms by using real world dataset on Sunway many-core processors. The experiment demonstrates comparable performance to the traditional parallel implementation on Sunway many-core processor and our framework improves the performance up to 40%.
UR - https://www.scopus.com/pages/publications/85028070157
U2 - 10.1109/ASAP.2017.7995268
DO - 10.1109/ASAP.2017.7995268
M3 - 会议稿件
AN - SCOPUS:85028070157
T3 - Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors
SP - 119
EP - 126
BT - 2017 IEEE 28th International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 July 2017 through 12 July 2017
ER -