Abstract
The need for power-aware computing has become increasingly apparent. Common power-aware platforms have placed the burden of optimizing energy consumption on the programmer. In many cases this is a complex task which requires more time from the programmer than is acceptable. Hence, auto-tuning for power-aware computing has been proposed to alleviate the programmer from this task. Previous research has been focusing on automatic tuning of individual applications. However, there has been little work that tunes multiple programs across an entire platform. The Single-Chip Cloud Computer (SCC) is an experimental processor created by Intel Labs. In this paper, we present a method that extends auto-tuning to consider the multi-programmed workload across the entire many-core platform of SCC. Using an algorithm based on Differential Evolution, we were able to reduce the energy-delay product of the workload by 58.5%.