Improving the Effectiveness of Context-based Prefetching with Multi-order Analysis
Authors: Y. Chen, H. Zhu, H. Jin, X.-H. Sun
Date: September, 2010
Venue: The 3rd International Workshop on Parallel Programming Models and Systems Software for High-End Computing (P2S2), San Diego, CA, USA
Type: Workshop
Abstract
Data prefetching is an effective way to accelerate data access in high-end computing systems and to bridge the increasing performance gap between processor and memory. In recent years, the context-based data prefetching has received intensive attention because of its general applicability. In this study, we provide a preliminary analysis of the impact of orders on the effectiveness of the context-based prefetching. Motivated by the observations from the analytical results, we propose a new context-based prefetching method named Multi-Order Context-based (MOC) prefetching to adopt multi-order context analysis to increase the context-based prefetching effectiveness. We have carried out simulation testing with the SPEC-CPU2006 benchmarks via an enhanced CMP$im simulator. The simulation results show that the proposed MOC prefetching method outperforms the existing single-order prefetching and reduces the data-access latency effectively.