A Reuse-Degree Based Locality Classifier for Locality-Aware Data Replication

The last level cache (LLC) in shared configuration is widely used in the tiled chip multiprocessors (CMPs), which reduces the off-chip miss rate but incurs the long on-chip access latency. The state-of-the-art Locality-Aware Data Replication (LADR) scheme provides an effective tradeoff between capacity and latency through an in-hardware structure named locality classifier. However, the best Limited
₃
locality classifier (Limited
₃
) in LADR equally preserves locality information of 3 cores for all cache lines indiscriminately that is superfluous for some lines reused by less than 3 cores but incomplete for other lines reused by more than 3 cores, which not only wastes the storage space but also limits the performance improvement. In this paper, we propose a novel concept of Reuse-Degree (RD) for each LLC line, since the line is loaded into LLC, to represent the number of cores that have reused the line. Then, we divide cache lines into Not Reused Line (NRL, RD = 0), Single Reused Line (SRL, RD = 1) and Multiple Reused Line (MRL, RD >= 2) based on their RDs and find that a significant fraction of LLC lines are NRLs or SRLs at any time. Based on this observation, we design a Reuse-Degree based Locality Classifier (RD_LC) for LADR. Specifically, RD_LC decouples the locality classifier from the LLC tag array and introduces two kinds of locality information arrays, single locality information array (SLIA) and complete locality information array (CLIA). Besides, RD_LC allocates a locality information entry only for the reused cache lines (SRLs or MRLs) instead of all cache lines, and assigns an SLIA entry to SRLs and a CLIA entry to MRLs. Our proposal avoids a waste of the storage space and also maintains enough locality information for the accuracy of data replication decisions. Experimental results show that our RD_LC for LADR saves 51% of the storage overhead than that of the baseline Limited
₃
locality classifier with a performance improvement and a network traffic reduction by 7.56% and 3.33 % respectively.