Aggregate flow-based performance fairness in CMPs

Abstract

In CMPs, multiple co-executing applications create mutual interference when sharing the underlying network-on-chip architecture. Such interference causes different performance slowdowns to different applications. To mitigate the unfairness problem, we treat traffic initiated from the same thread as an aggregate flow such that causal request/reply packet sequences can be allocated to resources consistently and fairly according to online profiled traffic injection rates. Our solution comprises three coherent mechanisms from rate profiling, rate inheritance, and rate-proportional channel scheduling to facilitate and realize unbiased workload-adaptive resource allocation. Full-system evaluations in GEM5 demonstrate that, compared to classic packet-centric and latest application-prioritization approaches, our approach significantly improves weighted speed-up for all multi-application mixtures and achieves nearly ideal performance fairness.