SIMD-ified R-tree Query Processing and Optimization

TL;DR

This paper explores how SIMD vectorization can significantly accelerate spatial query processing in R-tree indexes, achieving up to 9x latency improvements through optimized algorithms and data layouts.

Contribution

It introduces SIMD-optimized algorithms for spatial range select and join operations on R-trees, with two data layouts and various performance optimizations.

Findings

Up to 9x reduction in query latency with SIMD vectorization.

Effective data layouts for SIMD in R-tree nodes.

Performance improvements validated across different scenarios.

Abstract

The introduction of Single Instruction Multiple Data (SIMD) instructions in mainstream CPUs has enabled modern database engines to leverage data parallelism by performing more computation with a single instruction, resulting in a reduced number of instructions required to execute a query as well as the elimination of conditional branches. Though SIMD in the context of traditional database engines has been studied extensively, it has been overlooked in the context of spatial databases. In this paper, we investigate how spatial database engines can benefit from SIMD vectorization in the context of an R-tree spatial index. We present vectorized versions of the spatial range select, and spatial join operations over a vectorized R-tree index. For each of the operations, we investigate two storage layouts for an R-tree node to leverage SIMD instructions. We design vectorized algorithms for each of the spatial operations given each of the two data layouts. We show that the introduction of SIMD can improve the latency of the spatial query operators up to 9x. We introduce several optimizations over the vectorized implementation of these query operators, and study their effectiveness in query performance and various hardware performance counters under different scenarios.