为了使用可扩展哈希表进行快速的数据访问，需要高效地更新索引以维护哈希表 . 文中提出了一种基于 GPU 的可扩展哈希算法 gEHT. 该算法充分利用 GPU 的并行计算能力，并采用表重用、预分裂技术，无锁地扩展和收缩表、插入和删除数据，实现了高并发地创建哈希表、更新索引和检索数据 . 实验结果表明，该算法的查询数据、维护哈希表和更新索引性能优于其他多核 CPU 的线性哈希及可扩展哈希算法，尤其是在高负载的情况下.

In order to use an extended hash table for fast data accessing ， an efficient index updating to maintain the hash table is necessary. Proposed in this his paper is a GPU-based extendible hashing algorithm named gEHT ， which takes full advantage of GPU ’ s parallel computing power ， and adopts list reuse as well as pre-split technology to extend and merge lists and to insert and delete data in a lock-free manner. Thus ， high levels of concurrency for hash table creation ， index updating and data retrieval are realized. Experimental results show that gEHT is superior to some other linear hashing and extendible hashing algorithms on the basis of multi-core CPU in terms of querying data ， maintaining hash table and updating index ， especially in the case of heavy load.