2016 International Conference on Parallel Architecture and Compilation Techniques (PACT) (2016)
Sept. 11, 2016 to Sept. 15, 2016
Onur Kayiran , Advanced Micro Devices, Inc., United States
Adwait Jog , College of William and Mary, United States
Ashutosh Pattnaik , Pennsylvania State University, United States
Rachata Ausavarungnirun , Carnegie Mellon University, United States
Xulong Tang , Pennsylvania State University, United States
Mahmut T. Kandemir , Pennsylvania State University, United States
Gabriel H. Loh , Advanced Micro Devices, Inc., United States
Onur Mutlu , ETH Zürich, Switzerland
Chita R. Das , Pennsylvania State University, United States
To improve the performance of Graphics Processing Units (GPUs) beyond simply increasing core count, architects are recently adopting a scale-up approach: the peak throughput and individual capabilities of the GPU cores are increasing rapidly. This big-core trend in GPUs leads to various challenges, including higher static power consumption and lower and imbalanced utilization of the datapath components of a big core. As we show in this paper, two key problems ensue: (1) the lower and imbalanced datapath utilization can waste power as an application does not always utilize all portions of the big core datapath, and (2) the use of big cores can lead to application performance degradation in some cases due to the higher memory system contention caused by the more memory requests generated by each big core. This paper introduces a new analysis of datapath component utilization in big-core GPUs based on queuing theory principles. Building on this analysis, we introduce a fine-grained dynamic power- and clock-gating mechanism for the entire datapath, called μC-States, which aims to minimize power consumption by turning off or tuning-down datapath components that are not bottlenecks for the performance of the running application. Our experimental evaluation demonstrates that μC-States significantly reduces both static and dynamic power consumption in a big-core GPU, while also significantly improving the performance of applications affected by high memory system contention. We also show that our analysis of datapath component utilization can guide scheduling and design decisions in a GPU architecture that contains heterogeneous cores.
Graphics processing units, Pipelines, Power demand, Registers, Memory management, Queueing analysis
O. Kayiran et al., "μC-States: Fine-grained GPU datapath power management," 2016 International Conference on Parallel Architecture and Compilation Techniques (PACT), Haifa, Israel, 2016, pp. 17-30.