Cuckoo Node Hashing on GPUs

Abstract

The hash table finds numerous applications in many different domains, but its potential for non-coalesced memory accesses and execution divergence characteristics impose optimization challenges on GPUs. We propose a novel hash table design, referred to as Cuckoo Node Hashing, which aims to better exploit the massive data parallelism offered by GPUs. At the core of its design, we leverage Cuckoo Hashing, one of known hash table design schemes, in a closed-address manner, which, to our knowledge, is the first attempt on GPUs. We also propose an architecture-aware warp-cooperative reordering algorithm that improves the memory performance and thread divergence of Cuckoo Node Hashing and efficiently increases the likelihood of coalesced memory accesses in hash table operations. Our experiments show that Cuckoo Node Hashing outperforms and scales better than existing state-of-the-art GPU hash table designs such as DACHash and Slab Hash with a peak performance of 5.03 billion queries/second in static searching and 4.34 billion insertions/second in static building.