Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Publication
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Recent research advocates address-correlating predictors to identify cache block addresses for prefetch. Unfortunately, address-correlating predictors require correlation data storage proportional in size to a program's active memory footprint. As a result, current proposals for this class of predictor are either limited in coverage due to constrained on-chip storage requirements or limited in prediction lookaheaddue to long off-chip correlation data lookup. In this paper, we propose Last-Touch Correlated Data Streaming (LT-cords), a practical address-correlating predictor. The key idea of LT-cords is to record correlation data off chip in the order they will be used and stream them into a practicallysized on-chip table shortly before they are needed, thereby obviating the need for scalable on-chip tables and enabling low-latency lookup. We use cycle-accurate simulation of an 8-way out-of-order superscalar processor to show that: (1) LT-cords with 214KB of on-chip storage can achieve the same coverage as a last-touch predictor with unlimited storage, without sacrificing predictor lookahead, and (2) LT-cords improves performance by 60% on average and 385% at best in the benchmarks studied. © 2007 IEEE.