PolyPC: Polymorphic Parallel Computing Framework on Embedded Reconfigurable System

报告人:黄苗青(University of Arkansas)
时  间:2017年7月18日下午1点30分(周二)
地  点:张江校区微电子楼389会议室

With the help of parallelism provided by the fine-grained architecture, hardware accelerators on Field Programmable Gate Arrays (FPGAs) can significantly improve the performance of many applications. However, designers are typically required to have excellent hardware programming skills and unique optimization techniques to fully explore the potential of FPGA resources. In this work, we propose the PolyPC (Polymorphic ParallelComputing) framework that aims to improve the productivity while achieving the performance speedup. The PolyPC framework implements a custom hardware platform on which the PolyPC framework extends vendor-provided tools to convert OpenCL-like programs into executables by using high-level synthesis (HLS) tools. The PolyPC framework is evaluated regarding performance, area efficiency, and multitasking. The results show a maximum of 66 folds of speedup over a dual-core ARM processor, and 1,043 folds of speedup over a high-performance MicroBlaze soft processor, with 125 folds of area efficiency. In addition, it delivers a significant improvement in response time to high-priority PolyTasks with the priority-aware scheduling.

Miaoqing Huang is an Associate Professor in the Department of Computer Science and Computer Engineering at the University of Arkansas. He received his B.S. degree in 1998 from the Fudan University and his Ph.D. degree in 2009 from the George Washington University, respectively. He joined the University of Arkansas in January 2010 as an Assistant Professor. His research interests include reconfigurable computing, computer architecture, hardware design, and high-performance computing. He has published more than 70 journal and conference papers in these areas. He has served in technical program committees of more than 20 international conferences such as FCCM, FPL, FPT, and Supercomputing. He also regularly reviews papers for more than 20 journals such as IEEE TC, IEEE TPDS, and ACM TRETS.



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