Hybrid Access Cache Indexing Framework Adapted to GPU
Author:
Affiliation:

Clc Number:

Fund Project:

Strategic Priority Research Program of the Chinese Academy of Sciences (Y8XD373105); Key Research Program of Frontier Sciences, Chinese Academy of Sciences (ZDBS-LY-JSC038)

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Hash tables, as a type of data indexing structure that provides efficient data access based on key values, are widely used in various computer applications, especially in system software, databases, and high performance that require high performance Computing field. In network, cloud computing and IoT services, hash tables have become the core system components of cache systems. However, with the large-scale increase in the amount of large-scale data, performance bottlenecks have gradually emerged in systems designed with a multi-core CPU as the core of the hash table structure. There is an urgent need to further improve the high performance and scalability of the hash table. With the increasing popularity of general-purpose graphics processing units (GPUs) and the substantial improvement of hardware computing capabilities and concurrency performance, various types of system software tasks with parallel computing as the core have been optimized on the GPU and have achieved considerable performance promotion. Due to the sparseness and randomness, using the existing parallel structure of the hash table directly on the GPU will inevitably bring high-frequency memory access and frequent bus data transmission, which affects the performance of the hash table on the GPU. This study focuses on the analysis of memory access, hit rate, and index overhead of hash table indexes in the cache system. A hybrid access cache index framework CCHT (cache cuckoo hash table) adapted to GPU is proposed and provided. The cache strategy required by index and index overhead allows concurrent execution of write and query operations, maximizing the use of the computing performance and concurrency characteristics of GPU hardware, reducing memory access and bus transferring overhead. Through GPU hardware implementation and experimental verification, CCHT has better performance than other cache indexing hash table while ensuring cache hit rate.

    Reference
    Related
    Cited by
Get Citation

张鸿骏,武延军,张珩,张立波.一种适应GPU的混合访问缓存索引框架.软件学报,2020,31(10):3038-3055

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:February 10,2020
  • Revised:April 04,2020
  • Adopted:
  • Online: June 11,2020
  • Published: October 06,2020
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063