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Previous research shows that LRU replacement policy is not efficient when applications exhibit a distant re-reference interval. Recently proposed RRIP policy improves performance for such workloads. However, RRIP lacks of access recency information, which may confuse the replacement policy to make accurate prediction. Consequently, RRIP is not robust for recency-friendly workloads. This paper proposes an Adaptive Insertion and Re-reference Prediction (AI-RRP) policy which evicts data based on both re-reference prediction value and the access recency information. To make the replacement policy more adaptive across different workloads and different phases during execution, Dynamic AI-RRP (DAI-RRP) is proposed which adjusts the insertion position and prediction value for different access patterns. Simulation results show DAI-RRP reduces CPI over LRU and Dynamic RRIP by an average of 8.3% and 4.1% respectively on a single-core processor with a 1MB 16-way set last-level cache (LLC). Evaluations on quad-core CMP with a 4MB shared LLC show that DAI-RRP outperforms LRU and Dynamic RRIP (DRRIP) on the weighted speedup metric by an average of 13.2% and 26.7% respectively. Furthermore, compred to LRU, DAI-RRP requires similar hardware, or even less hardware for high-associativity cache.
Date of Conference: 27-30 Oct. 2010