Comparative analysis of gpu stream processing between persistent and non-persistent kernels

Abstract

Employing GPU to accelerate stream data processing has shown to be a considerable success in recent research. The stream processing engines need to process continuous data. Persistent Thread (PT), where GPU threads remain in a loop throughout executions, rather than non-Persistent Thread (nonPT) kernels can give several advantages. PT provides high-performance low-latency data processing by reducing kernel launch overhead and hiding memory copy overhead to the GPU. In this paper, we comparatively analyze the performances of PT and nonPT in various aspects. We also figure out the cause of advantages and what needs to be considered in order to obtain actual performance gain. The evaluation was done with four distinct application scenarios and the result shows that PT yields at most x4.4 higher performance over nonPT under desirable conditions.

Younghoon Kim 김영훈

Associate Professor

My research interests include distributed systems, high-performance computing/networking and data-center-related technologies.