Publication | Closed Access
μC-States
35
Citations
93
References
2016
Year
Unknown Venue
Cluster ComputingGpu ArchitectureEngineeringGpu ClusterEdge ComputingBig Core DatapathCloud ComputingMany-core ArchitectureComputer ArchitectureComputer EngineeringSystems EngineeringGraphics Processing UnitsParallel ProgrammingComputer ScienceParallel ComputingManycore ProcessorGpu ComputingCore Count
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.
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