Processor resources required for an effective execution of an application vary across different sections. We propose to take advantage of clustering to turn-off resources that do not contribute to improve performance. First, we present a simple hardware scheme to dynamically compute the energy consumed by each processor block and the energy-delay2 product for a given interval of time. This scheme is used to compute the effectiveness of the current configuration in terms of energy-delay2 and evaluate the benefits of increasing/decreasing the number of active issue queues. Performance evaluation shows an average energy-delay2 product improvement of 18%, and up to 50% for some applications, in a quad-cluster architecture.

Peer Reviewed