WarpCore: A Library for fast Hash Tables on GPUs

TL;DR

WarpCore is a GPU-optimized hash table library that significantly outperforms existing solutions in speed and scalability, enabling high-throughput data processing and bioinformatics applications.

Contribution

The paper introduces WarpCore, a novel GPU hash table library that achieves unprecedented performance and scalability for large-scale data processing tasks.

Findings

Achieves up to 1.6 billion inserts/sec and 4.3 billion retrievals/sec on a single GPU.

Outperforms state-of-the-art solutions like cuDPP, SlabHash, and cuDF.

Enables over two orders-of-magnitude speedup in bioinformatics applications.

Abstract

Hash tables are ubiquitous. Properties such as an amortized constant time complexity for insertion and querying as well as a compact memory layout make them versatile associative data structures with manifold applications. The rapidly growing amount of data emerging in many fields motivated the need for accelerated hash tables designed for modern parallel architectures. In this work, we exploit the fast memory interface of modern GPUs together with a parallel hashing scheme tailored to improve global memory access patterns, to design WarpCore -- a versatile library of hash table data structures. Unique device-sided operations allow for building high performance data processing pipelines entirely on the GPU. Our implementation achieves up to 1.6 billion inserts and up to 4.3 billion retrievals per second on a single GV100 GPU thereby outperforming the state-of-the-art solutions cuDPP, SlabHash, and NVIDIA RAPIDS cuDF. This performance advantage becomes even more pronounced for high load factors of over $90\%$. To overcome the memory limitation of a single GPU, we scale our approach over a dense NVLink topology which gives us close-to-optimal weak scaling on DGX servers. We further show how WarpCore can be used for accelerating a real world bioinformatics application (metagenomic classification) with speedups of over two orders-of-magnitude against state-of-the-art CPU-based solutions. WC is written in C++/CUDA-C and is openly available at https://github.com/sleeepyjack/warpcore.