Compiler-directed dynamic voltage/frequency scheduling for energy reduction in microprocessors
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
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
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
A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads
IISWC '10 Proceedings of the IEEE International Symposium on Workload Characterization (IISWC'10)
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
Performance and Power Analysis of ATI GPU: A Statistical Approach
NAS '11 Proceedings of the 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage
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
Gdev: first-class GPU resource management in the operating system
USENIX ATC'12 Proceedings of the 2012 USENIX conference on Annual Technical Conference
A measurement study of GPU DVFS on energy conservation
Proceedings of the Workshop on Power-Aware Computing and Systems
High-Resolution power profiling of GPU functions using low-resolution measurement
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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
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Graphics processing units (GPUs) provide significant improvements in performance and performance-per-watt as compared to traditional multicore CPUs. This energy-efficiency of GPUs has facilitated the use of GPUs in many application domains. Albeit energy efficient, GPUs consume non-trivial power independently of CPUs. Therefore, we need to analyze the power and performance characteristic of GPUs and their causal relation with CPUs in order to reduce the total energy consumption of the system while sustaining high performance. In this paper, we provide a power and performance analysis of GPU-accelerated systems for better understandings of these implications. Our analysis on a real system discloses that system energy can be reduced by 28% retaining a decrease in performance within 1% by controlling the voltage and frequency levels of GPUs. We show that energy savings can be achieved when GPU core and memory clock frequencies are appropriately scaled considering the workload characteristics. Another interesting finding is that voltage and frequency scaling of CPUs is trivial for total system energy reduction, and even should not be applied in state-of-the-art GPU-accelerated systems. We believe that these findings are useful to develop dynamic voltage and frequency scaling (DVFS) algorithms for GPU-accelerated systems.