Effective Adaptive Computing Environment Management via Dynamic Optimization

To minimize the surging power consumption of microprocessors, adaptive computing environments (ACEs) where microarchitectural resources can be dynamically tuned to match a program's runtime requirement and characteristics are becoming increasingly common. Adaptive computing environments usually have multiple configurable hardware units, necessitating exploration of a large number of combinatorial configurations in order to identify the most energy-efficient configuration. In this paper, we propose a scheme for efficient management of multiple configurable units, utilizing the inherent capabilities of dynamic optimization systems. Most dynamic optimizers typically detect dominant code regions (hotspots). We develop an ACE management scheme where hotpot boundaries are used for phase detection and adaptation. Since hotspots are of variable sizes and are often nested, program phase behavior which is hierarchical in nature is automatically captured in this technique. To demonstrate th...

• Type: Conference
• Year: 2005
• Where: CGO
• Authors: Shiwen Hu, Madhavi Gopal Valluri, Lizy Kurian John
• Keywords: Adaptive Computing Environments, CGO 2005, L2 Cache, Multiple Configurable Hardware, Software Engineering