Machine Learning-based Thermally-Safe Cache Contention Mitigation in Clustered Manycores

We present the first technique that mitigates cache contention under thermal constraints in clustered manycores. We show by means of extensive experiments that significant performance gains in this scenario can be achieved. The background is that concurrently-running applications on manycore clusters compete for the shared cache, slowing down their execution. In addition, heavy parallel computations on physically-close cores increase temperatures to non-sustainable levels, which in turn triggers a throttle down of voltage/frequency levels and hence performance is compromised. These problems are not unknown, but as our analysis shows, tackling them independently is sub-optimal. We introduce the first task migration technique that jointly mitigates cache contention while enforcing the thermal constraint at the same time. It works in conjunction with cluster-level dynamic voltage and frequency scaling. Our technique needs to predict the impact of task migration on performance considering cache contention. Since it is impossible to derive an analytical model for cache contention that is both sufficiently accurate and practically feasible to implement, we employ an accurate, yet lightweight neural network (NN) model. As a result, we can operate the manycore system at higher performance while safely staying within thermal constraints. We report a significant step forward in this paper and unveil new potentials for performance optimization.