Bayesian theory oriented Optimal Data-Provider Selection for CMP

Abstract

With the number of cores and working sets of parallel workloads soaring, shared L2 caches exhibit fewer misses than private L2 caches via making better use of the all available cache capacity. However, shared L2 caches induce higher overall L1 miss latencies because of longer average distance between requestor and home node, and potentially congestions at some nodes. We observe that there is a high probability that the requested data of an L1 miss resides in a neighbor node's L1 cache. In such cases, these long-distance accesses to the home nodes can be potentially avoided. In order to successfully leverage the aforementioned property, we propose Bayesian theory oriented Optimal Data-Provider Selection (ODPS). ODPS partitions the multi-core into clusters of 2×2 nodes, and introduces the Proximity Data Prober (PDP) to detect whether an L1 miss can be served by one L1 cache within the same cluster. Furthermore, we devise the Bayesian Decision Classifier (BDC) to intelligently and adaptively select a remote L2 cache or a neighboring L1 node as the data provider according to the minimal miss cost based on the Bayesian decision theory.