For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Bayesian Theory Based Adaptive Proximity Data Accessing for CMP Caches
Guohong LI Zhenyu LIU Sanchuan GUO Dongsheng WANG
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2013/06/01
Online ISSN: 1745-1337
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Circuit, System, and Computer Technologies)
Bayesian Decision, cache, multicore,
Full Text: PDF(2.5MB)>>
As the number of cores and the working sets of parallel workloads increase, shared L2 caches exhibit fewer misses than private L2 caches by making a better use of the total available cache capacity, but they induce higher overall L1 miss latencies because of the longer average distance between the requestor and the home node, and the potential congestions at certain nodes. We observed that there is a high probability that the target data of an L1 miss resides in the L1 cache of a neighbor node. In such cases, these long-distance accesses to the home nodes can be potentially avoided. In order to leverage the aforementioned property, we propose Bayesian Theory based Adaptive Proximity Data Accessing (APDA). In our proposal, we organize the multi-core into clusters of 2x2 nodes, and introduce the Proximity Data Prober (PDP) to detect whether an L1 miss can be served by one of the cluster L1 caches. Furthermore, we devise the Bayesian Decision Classifier (BDC) to adaptively select the remote L2 cache or the neighboring L1 node as the server according to the minimum miss cost. We evaluate this approach on a 64-node multi-core using SPLASH-2 and PARSEC benchmarks, and we find that the APDA can reduce the execution time by 20% and reduce the energy by 14% compared to a standard multi-core with a shared L2. The experimental results demonstrate that our proposal outperforms the up-to-date mechanisms, such as ASR, DCC and RNUCA.