Energy Efficiency for Clustered Heterogeneous Multicores

Abstract

Heterogeneous multicore systems clustered in multiple Voltage Frequency Islands (VFIs) are the next-generation solution for power and energy efficient computing systems. Due to the heterogeneity, the power consumption and execution time of a task changes not only with Dynamic Voltage and Frequency Scaling (DVFS), but also according to the task-to-island assignment, presenting major challenges for power management and energy minimization techniques. This paper focuses on energy minimization of periodic real-time tasks (or performance-constrained tasks) on such systems, in which the cores in an island are homogeneous and share the same voltage and frequency, but different islands have different types and numbers of cores and can be executed at other voltages and frequencies. We present an efficient algorithm to minimize the total energy consumption while satisfying the timing constraints of all tasks. Our technique consists of the coordinated selection of the voltage and frequency levels for each island, together with a task partitioning strategy that considers the energy consumption of the task executing on different islands and at different frequencies, as well as the impact of the frequency and the underlying core architecture to the resulting execution time. Every task is then mapped to the most energy efficient island for the selected voltage and frequency levels, and to a core inside the island such that the workloads of the cores in a VFI are balanced. We experimentally evaluate our technique and compare it to state-of-the-art solutions, resulting in average in 25 percent less energy consumption (and up to 87 percent for some cases), while guaranteeing that all tasks meet their deadlines.