GPGPU for Real-Time Data Analytics

Bingsheng He, Huynh Phung Huynh, Rick Siow Mong Goh

Abstract: The demand for real-time data analytics (RTDA) has been on the rise in the past decades and is ever-growing with the proliferation of different data collection devices.GPGPU (General-Purpose computation on Graphics Processing Units) is an emerging research area in HPC (high performance computing). With the massive computation power and high memory bandwidth, GPUs have become a sharp weapon to address the performance requirement of RTDA. Designed as co-processors, GPUs pose a number of technical challenges for RTDA in terms of efficiency and programmability. On the one hand, while new generation GPUs can have over an order of magnitude higher memory bandwidth and higher computation power (in terms of GFLOPS) than CPUs, novel GPGPU algorithmic design and implementation are a must to unleash the hardware power. On the other hand, writing a correct and efficient GPU program is still challenging in general, and even more difficult for RTDA with streaming updates and real-time multi-tasking.