# Data-Parallel Hashing Techniques for GPU Architectures

arXiv: https://arxiv.org/abs/1807.04345 · HTML: https://mlsanity.com/paper/1807.04345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [ASH: A Modern Framework for Parallel Spatial Hashing in 3D Perception](https://mlsanity.com/paper/2110.00511/) (2021)

## Abstract (arXiv)

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.