WuKong: Hyper-heterogeneous Computing Platform for Big Data Analytics
COM1 Level 3
MR1, COM1-03-19
Abstract:
Modern data analytics requires a huge amount of computing power to analyze a massive amount of data. Therefore, heterogeneous data analytics platforms appear as an attempt to close the gap between compute power and data. For example, FPGA-based systems, GPU-based systems, SSD, and programmable switches. Even though these heterogeneous systems achieve better performance than CPU-only homogeneous systems due to the dying of Moore’s law, we still identify that simple heterogeneous systems cannot harvest the potential of each heterogeneous device. For example, GPU-based approaches suffer from severe IO issues and programmable switches, e.g., P4 switch, have very limited compute and memory capacities. To this end, we argue for hyper-heterogeneous computing for big data analytics. Therefore, we present WuKong, a hyper-heterogeneous computing platform for big data analytics. Our platform is centralized on FPGA-based SmartNIC, and explores its potential of co-optimization with any other heterogeneous devices, such as GPU, P4 switch, and SSD. The key idea of WuKong is to use FPGA to complement other heterogeneous devices via deep co-design, with a goal of “1+1 > 2”.
Biodata:
Dr. Zeke Wang is now a ZJU100 Young Professor at the Computer Scicence, Zhejiang University, China. Before that, He received his Ph.D. degree at Zhejiang University in 2011, and was a postdoc in NTU & NUS (2013-2017) and ETH Zurich (2017-2019). Its main research interest lies in building hyper-heterogeneous computing platform for various applications, such as distributed Deep Learning systems with in-network computing.