Last-Touch Correlated Data Streaming

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 lookahead due 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 practically-sized 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