Asymmetric chip multiprocessors are imminent in the multi-core era primarily due their potential for power-performance efficiency. In order for software to fully realize this potential, the scheduling of threads to cores must be automated to adapt to the changing program behavior. However, strict system abstraction layers limit the controllability and observability of low level hardware details, thereby, limiting the state-of-the-art systems to rely on manual or static mapping of threads to cores in an asymmetric multi-core. In this paper, we propose a self-adaptive scheduler that exploits program behavior at runtime by matching computational demands of threads to the capabilities of cores. We present a novel empirical model to predict the selection of an appropriate core (based on optimizing throughput, power or performance per watt) for changing program phases within threads. Thread migration is initiated when an optimal mapping of threads to cores is predicted. Results show that our predictive schedulers for the three target optimizations are within 10% of the ideal scheduler.
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