TY - GEN
T1 - PCS
T2 - 2019 International Conference on High Performance Computing and Simulation, HPCS 2019
AU - Ojika, David
AU - Gordon-Ross, Ann
AU - Lam, Herman
AU - Yoo, Shinjae
AU - Cui, Younggang
AU - Dong, Zhihua
AU - Van Dam, Kirstin Kleese
AU - Lee, Seyong
AU - Kurth, Thorsten
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators (graphics processing units (GPUs), fieldprogrammable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.), software becomes a critical design aspect. Exploiting this new computational power requires increased software design time and effort to make valuable scientific discovery in the face of the complicated programming environments introduced by these accelerators. To address these challenges, we propose unifying multiple programming models into a single programming environment to facilitate large-scale, accelerator-aware, heterogeneous computing for next-generation scientific applications. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. Focusing FPGAs, we describe the key concepts of the PCS platform and differentiate PCS from the current state-of-the-art, propose a new multi-FPGA architecture for graph-centric workloads (e.g., deep learning, etc.) with discussions on ongoing work.
AB - As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators (graphics processing units (GPUs), fieldprogrammable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.), software becomes a critical design aspect. Exploiting this new computational power requires increased software design time and effort to make valuable scientific discovery in the face of the complicated programming environments introduced by these accelerators. To address these challenges, we propose unifying multiple programming models into a single programming environment to facilitate large-scale, accelerator-aware, heterogeneous computing for next-generation scientific applications. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. Focusing FPGAs, we describe the key concepts of the PCS platform and differentiate PCS from the current state-of-the-art, propose a new multi-FPGA architecture for graph-centric workloads (e.g., deep learning, etc.) with discussions on ongoing work.
KW - FPGA
KW - heterogeneous computing
KW - programming environments
KW - programming model
KW - scientific computing
UR - http://www.scopus.com/inward/record.url?scp=85092029284&partnerID=8YFLogxK
U2 - 10.1109/HPCS48598.2019.9188108
DO - 10.1109/HPCS48598.2019.9188108
M3 - Conference contribution
AN - SCOPUS:85092029284
T3 - 2019 International Conference on High Performance Computing and Simulation, HPCS 2019
SP - 636
EP - 641
BT - 2019 International Conference on High Performance Computing and Simulation, HPCS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 July 2019 through 19 July 2019
ER -