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

2025-01-29

AI Summary

To address memory-access performance bottlenecks in tree structures on heterogeneous hardware systems—caused by mismatches between tree layouts and hierarchical memory characteristics—this paper proposes a hardware-aware, generic tree node layout method. Our approach introduces: (1) the first unified node reordering strategy explicitly optimized for hardware attributes including latency, bandwidth, and spatial/temporal locality; and (2) a dual-mode triggering mechanism supporting both offline pre-optimization and online dynamic re-optimization, guided by runtime performance monitoring to enable cross-memory-tier adaptive layout adjustments. Experimental evaluation across diverse heterogeneous platforms demonstrates average performance improvements of 95% for offline-optimized layouts and 75% for online-adaptive layouts over conventional approaches. The method exhibits strong generalizability across tree types and hardware configurations, and delivers practical utility for memory-intensive tree-based applications.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• Tree Data Structure
• Hardware Resource Utilization
• Memory Type Optimization

Innovation

• Tree Data Structure Optimization
• Memory-aware Ordering
• Runtime Adjustment Strategy