Global-aware and Multi-order Context-based Prefetching for High-Performance Processors

Yong Chen, Huaiyu Zhu, Philip C Roth, Hui Jin, Xian-He He

Research output: Contribution to journalArticlepeer-review


Data prefetching is widely used in high-end computing systems to accelerate data accesses and to bridge the increasing performance gap between processor and memory. Context-based prefetching has become a primary focus of study in recent years due to its general applicability. However, current context-based prefetchers only adopt the context analysis of a single order, which suffers from low prefetching coverage and thus limits the overall prefetching effectiveness. Also, existing approaches usually consider the context of the address stream from a single instruction but not the context of the address stream from all instructions, which further limits the context-based prefetching effectiveness. In this study, we propose a new context-based prefetcher called the Global-aware and Multi-order Context-based (GMC) prefetcher. The GMC prefetcher uses multi-order, local and global context analysis to increase prefetching coverage while maintaining prefetching accuracy. In extensive simulatio
Original languageEnglish
Pages (from-to)16 pages, 355-370
JournalInternational Journal of High Performance Computing Applications
StatePublished - Nov 2011


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