Data-Parallel Hashing Techniques for GPU Architectures
Brenton J. Lessley, Hank Childs · IEEE Transactions on Parallel and Distributed Systems · 2019
Hash tables are a fundamental data structure for effectively storing and accessing sparse data, with widespread usage in domains ranging from computer graphics to machine learning. This study surveys the state-of-the-art research on data-parallel hashing techniques for emerging massively-parallel, many-core GPU architectures. This survey identifies key factors affecting the performance of different techniques and suggests directions for further research.