FFTs in External or Hierarchical Memory
Proceedings of the Fourth SIAM Conference on Parallel Processing for Scientific Computing
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2007 courses
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
High performance discrete Fourier transforms on graphics processors
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Rodinia: A benchmark suite for heterogeneous computing
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
On the Robust Mapping of Dynamic Programming onto a Graphics Processing Unit
ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
Energy-aware high performance computing with graphic processing units
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Power and performance analysis of GPU-accelerated systems
HotPower'12 Proceedings of the 2012 USENIX conference on Power-Aware Computing and Systems
A measurement study of GPU DVFS on energy conservation
Proceedings of the Workshop on Power-Aware Computing and Systems
The energy case for graph processing on hybrid CPU and GPU systems
IA^3 '13 Proceedings of the 3rd Workshop on Irregular Applications: Architectures and Algorithms
ACM Transactions on Architecture and Code Optimization (TACO)
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Nowadays Graphic Processing Units (GPU) are gaining increasing popularity in high performance computing (HPC). While modern GPUs can offer much more computational power than CPUs, they also consume much more power. Energy efficiency is one of the most important factors that will affect a broader adoption of GPUs in HPC. In this paper, we systematically characterize the power and energy efficiency of GPU computing. Specifically, using three different applications with various degrees of compute and memory intensiveness, we investigate the correlation between power consumption and different computational patterns under various voltage and frequency levels. Our study revealed that energy saving mechanisms on GPUs behave considerably different than CPUs. The characterization results also suggest possible ways to improve the 'greenness' of GPU computing.