WarpSpeed: A High-Performance Library for Concurrent GPU Hash Tables
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908607421874176 |
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| author | McCoy, Hunter Pandey, Prashant |
| author_facet | McCoy, Hunter Pandey, Prashant |
| contents | GPU hash tables are increasingly used to accelerate data processing, but their limited functionality restricts adoption in large-scale data processing applications. Current limitations include incomplete concurrency support and missing compound operations such as upserts.
This paper presents WarpSpeed, a library of high-performance concurrent GPU hash tables with a unified benchmarking framework for performance analysis. WarpSpeed implements eight state-of-the-art Nvidia GPU hash table designs and provides a rich API designed for modern GPU applications. Our evaluation uses diverse benchmarks to assess both correctness and scalability, and we demonstrate real-world impact by integrating these hash tables into three downstream applications.
We propose several optimization techniques to reduce concurrency overhead, including fingerprint-based metadata to minimize cache line probes and specialized Nvidia GPU instructions for lock-free queries. Our findings provide new insights into concurrent GPU hash table design and offer practical guidance for developing efficient, scalable data structures on modern GPUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16407 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WarpSpeed: A High-Performance Library for Concurrent GPU Hash Tables McCoy, Hunter Pandey, Prashant Distributed, Parallel, and Cluster Computing Data Structures and Algorithms GPU hash tables are increasingly used to accelerate data processing, but their limited functionality restricts adoption in large-scale data processing applications. Current limitations include incomplete concurrency support and missing compound operations such as upserts. This paper presents WarpSpeed, a library of high-performance concurrent GPU hash tables with a unified benchmarking framework for performance analysis. WarpSpeed implements eight state-of-the-art Nvidia GPU hash table designs and provides a rich API designed for modern GPU applications. Our evaluation uses diverse benchmarks to assess both correctness and scalability, and we demonstrate real-world impact by integrating these hash tables into three downstream applications. We propose several optimization techniques to reduce concurrency overhead, including fingerprint-based metadata to minimize cache line probes and specialized Nvidia GPU instructions for lock-free queries. Our findings provide new insights into concurrent GPU hash table design and offer practical guidance for developing efficient, scalable data structures on modern GPUs. |
| title | WarpSpeed: A High-Performance Library for Concurrent GPU Hash Tables |
| topic | Distributed, Parallel, and Cluster Computing Data Structures and Algorithms |
| url | https://arxiv.org/abs/2509.16407 |