GPU-Oriented Memory Management and its Application
Xu Yandong;Hua Bei;School of Computer Science and Technology, University of Science and Technology of China;
As the Graphics Processing Units(GPU) continuously increase their computing power, memory bandwidth, and device memory capacity, it is possible for the GPU to take on all the work of a data storage node. Dynamic memory management is a necessary function for data storage applications; however, this work is extremely hard for the GPU as a large number of concurrent threads as well as the Single Instruction Multiple Data execution mode may introduce serious data racing and thread blocking. In this paper, a high efficient GPU memory allocator is implemented based on the architectural features of GPU and the memory management requirements of data storage applications, which effectively reduces memory allocation competition as well as memory allocation time. Based on the GPU memory allocator, this paper also transplants a lock-free hash table for CPU on the GPU, to speed up the index operation on GPU. Experiments show that our GPU memory allocator and lock-free hash table implementation have good performance.
【CateGory Index】： TP332