Near-optimal replacement policies for shared caches in multicore processors

Abstract

An optimal replacement policy that minimizes the miss rate in a private cache was proposed several decades ago. It requires knowing the future access sequence the cache will receive. There is no equivalent for shared caches because replacement decisions alter this future sequence. We present a novel near-optimal policy for minimizing the miss rate in a shared cache that approaches the optimal execution iteratively. During each iteration, the future access sequence is reconstructed on every miss interleaving the future per-core sequences, taken from the previous iteration. This single sequence feeds a classical private-cache optimum replacement policy. Our evaluation on a shared last-level cache shows that our proposal iteratively converges to a near-optimal miss rate that is independent of the initial conditions, within a margin of 0.1%. The best state-of-the-art online policies achieve around 65% of the miss rate reduction obtained by our near-optimal proposal. In a shared cache, miss rate optimization does not imply the optimization of other metrics. Therefore, we also propose a new near-optimal policy to maximize fairness between cores. The best state-ofthe-art online policy achieves 60% of the improvement in fairness seen with our near-optimal policy. Our proposals are useful both for setting upper performance bounds and inspiring implementable mechanisms for shared caches.

Scalable Hierarchical Instruction Cache for Ultralow-Power Processors Clusters

“…Finally, it is noteworthy to mention that most recent work on instruction caches for multi-core architectures proposes alternative replacement policies suitable for specific application domains [23] [24] or interference-free techniques for real-time systems [25] [26]. Our work is focused on the architectural design and practical trade-offs of integrating a hierarchical cache in an ultra-low-power multi-core platform.…”