OLSATM: Online Learning Based State-Aware Task Migration on S-NUCA Many-Cores

Task migration maximizes performance while maintaining thermal safety in many-cores systems. Existing techniques exploit offline learning which requires tremendous training data and fixed-cycle migration which causes threads to miss the optimal migration timing. This paper presents Online Learning based State-Aware Task Migration (OLSATM). It pretrains a neural network (NN) with a small set of data and updates the model online to substitute the laborious data collection and model training of offline learning. It is state-aware and detects the timing when migration is needed, overcoming the shortcomings of periodical migration. Experimental results show that OLSATM enhances the performance by 3.7% and reduces the number of migration judgments by 26 % on average compared to the state-of-the-art task migration.