TokenTLB+CUP: A Token-Based Page Classification with Cooperative Usage Prediction

Abstract

Discerning the private or shared condition of the data accessed by the applications is an increasingly decisive approach to achieving efficiency and scalability in multiand many-core systems. Since most memory accesses in both sequential and parallel applications are either private (accessed only by one core) or read-only (not written) data, devoting the full cost of coherence to every memory access results in sub-optimal performance and limits the scalability and efficiency of the multiprocessor. This paper introduces TokenTLB, a TLB-based page classification approach based on exchange and count of tokens. Token counting on TLBs is a natural and efficient way for classifying memory pages, and it does not require the use of complex and undesirable persistent requests or arbitration. In addition, classification is extended with Cooperative Usage Predictor (CUP), a token-based system-wide page usage predictor retrieved through TLB cooperation, in order to perform a classification unaffected by TLB size. Through cycle-accurate simulation we observed that TokenTLB spends 43.6 percent of cycles as private per page on average, and CUP further increases the time spent as private by 22.0 percent. CUP avoids 4 out of 5 TLB invalidations when compared to state-of-the-art predictors, thus proving far better prediction accuracy and making usage prediction an attractive mechanism for the first time.