2025/09/19 by Hunter McCoy, Prashant Pandey, McCoy, Hunter +1
Computer Science · #Caching and Content Delivery #Cloud Computing and Resource Management #Data Structures and Algorithms (cs.DS) #Distributed #FOS: Computer and information sciences #Network Packet Processing and Optimization #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2509.16407
openalex publication_date 2025/09/19 · openalex created_date 2025/10/16 · openalex updated_date 2026/07/28
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.