GPU-Based Dynamic Hyperspace Hash with Full Concurrency

Zhuo Ren, Yu Gu, Chuanwen Li, FangFang Li, Ge Yu
<span title="2021-06-17">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="" style="color: black;">Data Science and Engineering</a> </i> &nbsp;
AbstractHyperspace hashing which is often applied to NoSQL data-bases builds indexes by mapping objects with multiple attributes to a multidimensional space. It can accelerate processing queries of some secondary attributes in addition to just primary keys. In recent years, the rich computing resources of GPU provide opportunities for implementing high-performance HyperSpace Hash. In this study, we construct a fully concurrent dynamic hyperspace hash table for GPU. By using atomic operations
more &raquo; ... tead of locking, we make our approach highly parallel and lock-free. We propose a special concurrency control strategy that ensures wait-free read operations. Our data structure is designed considering GPU specific hardware characteristics. We also propose a warp-level pre-combinations data sharing strategy to obtain high parallel acceleration. Experiments on an Nvidia RTX2080Ti GPU suggest that GHSH performs about 20-100X faster than its counterpart on CPU. Specifically, GHSH performs updates with up to 396 M updates/s and processes search queries with up to 995 M queries/s. Compared to other GPU hashes that cannot conduct queries on non-key attributes, GHSH demonstrates comparable building and retrieval performance.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1007/s41019-021-00161-5</a> <a target="_blank" rel="external noopener" href="">fatcat:yuqqwwia2bbivfycocmus5e2eu</a> </span>
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