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