论文标题
基于注意力的可变度预取料的细粒度地址细分
Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching
论文作者
论文摘要
机器学习算法已经通过准确预测未来的内存访问来表明有潜力提高预取性能。现有方法基于文本预测的建模，将预摘要视为序列预测的分类问题。但是，庞大而稀疏的内存地址空间导致了大型词汇，这使得这种建模不切实际。多个缓存线预取的输出的数量和顺序也与文本预测根本不同。我们提出了Transfetch，这是一种新颖的方法来建模预摘要。为了减少词汇量，我们使用细粒度的地址细分作为输入。为了预测未订购的未来地址集，我们使用三角位图用于多个输出。我们应用基于注意力的网络来了解输入和输出之间的映射。预测实验表明，与DELTA输入相比，地址分割的F1得分高26％ - 比Page 2006，SPEC 2017和GAP基准的PAGE和偏移输入高15％ - 24％。仿真结果表明，与没有预购相比，Transfetch可提高38.75％IPC的改善，表现优于最佳的基于规则的预摘要BOP，而基于ML的Prefetcher Voyager的表现优于6.64％。
Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling of text prediction, considering prefetching as a classification problem for sequence prediction. However, the vast and sparse memory address space leads to large vocabulary, which makes this modeling impractical. The number and order of outputs for multiple cache line prefetching are also fundamentally different from text prediction. We propose TransFetch, a novel way to model prefetching. To reduce vocabulary size, we use fine-grained address segmentation as input. To predict unordered sets of future addresses, we use delta bitmaps for multiple outputs. We apply an attention-based network to learn the mapping between input and output. Prediction experiments demonstrate that address segmentation achieves 26% - 36% higher F1-score than delta inputs and 15% - 24% higher F1-score than page & offset inputs for SPEC 2006, SPEC 2017, and GAP benchmarks. Simulation results show that TransFetch achieves 38.75% IPC improvement compared with no prefetching, outperforming the best-performing rule-based prefetcher BOP by 10.44%, and ML-based prefetcher Voyager by 6.64%.
