Bigger Is Not Better: The Fastest Static GPU Index Is Also Lightweight!

TL;DR

This paper demonstrates that optimized binary search on GPUs can outperform complex index structures in speed while maintaining minimal memory usage, challenging the belief that simplicity is inferior.

Contribution

The authors introduce advanced, architecture-specific binary search variants for GPUs that outperform complex index structures in speed and memory efficiency.

Findings

Binary search variants outperform state-of-the-art GPU indexes by up to 3.8x.

Optimized binary search maintains the smallest memory footprint.

GPU-specific optimizations significantly improve search performance.

Abstract

Sorting and binary searching a dense array can be considered the simplest and most space efficient form of indexing. This holds especially on GPUs as they exhibit exceptional sorting performance. However, the popular opinion is that such a primitive approach cannot compete with large, highly-sophisticated GPU index structures in terms of lookup performance, and hence, should not actually be considered in practice. In this work, we will investigate whether binary search actually still deserves this bad reputation or whether it can be a fast and space-minimal alternative to more heavy-weight index structures, in particular when utilizing all the advancements of current highly-parallel GPU architectures. To find out, we introduce advanced variants of binary search to GPUs and equip them with a set of established low-level optimizations. These architecture-specific optimizations aim at getting the most out of binary search by (a) greatly reducing the overall amount of GPU memory accesses required during search, (b) exploiting the enormous benefits of memory access coalescing on a GPU, and (c) maximizing scalability by reordering the dataset into a more favorable layout. By comparing our optimized search strategies against nine state-of-the-art GPU index structures under several static indexing workloads, we demonstrate that they not only outperform all competitors (except for hashing-based approaches) by a factor of up to 3.8, but also maintain the smallest possible memory footprint.