Mathematical models and control algorithms for dynamic optimization of multicore platforms: A complex dynamics approach

The continuous increase in integration densities contributed to a shift from Dennard´s scaling to a parallelization era of multi-/many-core chips. However, for multicores to rapidly percolate the application domain from consumer multimedia to high-end functionality (e.g., security, healthcare, big data), power/energy and thermal efficiency challenges must be addressed. Increased power densities can raise on-chip temperatures, which in turn decrease chip reliability and performance, and increase cooling costs. For a dependable multicore system, dynamic optimization (power / thermal management) has to rely on accurate yet low complexity workload models. Towards this end, we present a class of mathematical models that generalize prior approaches and capture their time dependence and long-range memory with minimum complexity. This modeling framework serves as the basis for defining new efficient control and prediction algorithms for hierarchical dynamic power management of future data-centers-on-a-chip.