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...
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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 simulation testing of the SPEC-CPU2006 benchmarks with an enhanced CMP$im simulator, the proposed GMC prefetcher was shown to outperform existing prefetchers and to reduce the data-access latency effectively. The average Instructions Per Cycle (IPC) improvement of SPEC CINT2006 and CFP2006 benchmarks with GMC prefetching was over 55% and 44% respectively.
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 contextbased data prefetchin...
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