Data-Parallel Hashing Techniques for GPU Architectures

TL;DR

This paper surveys data-parallel hashing techniques optimized for GPU architectures, identifying key performance factors and suggesting best practices for efficient sparse data management.

Contribution

It provides a comprehensive overview of current GPU hashing methods, highlighting performance factors and guiding future research directions.

Findings

Identified key factors influencing hashing performance on GPUs

Suggested best practices for designing efficient GPU hashing schemes

Pinpointed areas needing further research in GPU-based hashing

Abstract

Hash tables are one of the most fundamental data structures 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. Key factors affecting the performance of different hashing schemes are discovered and used to suggest best practices and pinpoint areas for further research.