An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness
Proceedings of the 36th annual international symposium on Computer architecture
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
Rodinia: A benchmark suite for heterogeneous computing
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
An integrated GPU power and performance model
Proceedings of the 37th annual international symposium on Computer architecture
Statistical power modeling of GPU kernels using performance counters
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Power and Performance Characterization of Computational Kernels on the GPU
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Memory power management via dynamic voltage/frequency scaling
Proceedings of the 8th ACM international conference on Autonomic computing
Improving Throughput of Power-Constrained GPUs Using Dynamic Voltage/Frequency and Core Scaling
PACT '11 Proceedings of the 2011 International Conference on Parallel Architectures and Compilation Techniques
Understanding the future of energy-performance trade-off via DVFS in HPC environments
Journal of Parallel and Distributed Computing
Power and performance analysis of GPU-accelerated systems
HotPower'12 Proceedings of the 2012 USENIX conference on Power-Aware Computing and Systems
Speeding up k-Means algorithm by GPUs
Journal of Computer and System Sciences
GPUWattch: enabling energy optimizations in GPGPUs
Proceedings of the 40th Annual International Symposium on Computer Architecture
Proceedings of International Workshop on Adaptive Self-tuning Computing Systems
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Nowadays, GPUs are widely used to accelerate many high performance computing applications. Energy conservation of such computing systems has become an important research topic. Dynamic voltage/frequency scaling (DVFS) is proved to be an appealing method for saving energy for traditional computing centers. However, there is still a lack of firsthand study on the effectiveness of GPU DVFS. This paper presents a thorough measurement study that aims to explore how GPU DVFS affects the system energy consumption. We conduct experiments on a real GPU platform with 37 benchmark applications. Our results show that GPU voltage/frequency scaling is an effective approach to conserving energy. For example, by scaling down the GPU core voltage and frequency, we have achieved an average of 19.28% energy reduction compared with the default setting, while giving up no more than 4% of performance. For all tested GPU applications, core voltage scaling is significantly effective to reduce system energy consumption. Meanwhile the effects of scaling core frequency and memory frequency depend on the characteristics of GPU applications.