Realizing Hardware-Optimized General Tree-Based Data Structures for Heterogeneous System Classes

TL;DR

This paper explores how reordering nodes in tree-based data structures based on memory characteristics can significantly enhance performance, offering strategies and algorithms for both offline and online optimization.

Contribution

It introduces novel strategies and algorithms for reordering tree nodes to optimize performance across heterogeneous memory types, with practical decision strategies for reordering triggers.

Findings

Performance improvements up to 95% as offline optimization

Performance improvements up to 75% as online optimization

Demonstrates effectiveness across various memory types

Abstract

Tree-based data structures are ubiquitous across applications. Therefore, a multitude of different tree implementations exist. However, while these implementations are diverse, they share a tree structure as the underlying data structure. As such, the access patterns inside these trees are very similar, following a path from the root of the tree towards a leaf node. Similarly, many distinct types of memory exist. These types of memory all have different characteristics. Some of these have an impact on the overall system performance. While the concrete types of memory are varied, their characteristics can often be abstracted to have a similar effect on the performance. We show how the characteristics of different types of memories can be used to improve the performance of tree-based data structures. By reordering the nodes of a tree inside memory, the characteristics of memory can be exploited to optimize the performance. To this end, this paper presents different strategies for reordering nodes inside memory as well as efficient algorithms for realizing these strategies. It additionally provides strategies to decide when such a reordering operation should be triggered during operation. Further, this paper conducts experiments showing the performance impact of the proposed strategies. The experiments show that the strategies can improve the performance of trees by up to 95\% as offline optimization and 75\% as online optimization.