Power efficient resource scaling in partitioned architectures through dynamic heterogeneity

The ever increasing demand for high clock speeds and the desire to exploit abundant transistor budgets have resulted in alarming increases in processor power dissipation. Partitioned (or clustered) architectures have been proposed in recent years to address scalability concerns in future billion-transistor microprocessors. Our analysis shows that increasing processor resources in a clustered architecture results in a linear increase in power consumption, while providing diminishing improvements in single-thread performance. To preserve high performance to power ratios, we claim that the power consumption of additional resources should be in proportion to the performance improvements they yield. Hence, in this paper, we propose the implementation of heterogeneous clusters that have varying delay and power characteristics. A cluster's performance and power characteristic is tuned by scaling its frequency and novel policies dynamically assign frequencies to clusters, while attempting to either meet a fixed power budget or minimize a metric such as Energy×Delay2 (ED2). By increasing resources in a power-efficient manner, we observe a 11% improvement in ED2 and a 22.4% average reduction in peak temperature, when compared to a processor with homogeneous units. Our proposed processor model also provides strategies to handle thermal emergencies that have a relatively low impact on performance.