Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Exploring weak scalability for FEM calculations on a GPU-enhanced cluster
Parallel Computing
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units
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
The Journal of Machine Learning Research
On the energy efficiency of graphics processing units for scientific computing
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Efficient feature weighting methods for ranking
Proceedings of the 18th ACM conference on Information and knowledge management
Parallel 3D Image Segmentation of Large Data Sets on a GPU Cluster
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Modeling GPU-CPU workloads and systems
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
An integrated GPU power and performance model
Proceedings of the 37th annual international symposium on Computer architecture
Quantifying the impact of GPUs on performance and energy efficiency in HPC clusters
GREENCOMP '10 Proceedings of the International Conference on Green Computing
EcoG: A Power-Efficient GPU Cluster Architecture for Scientific Computing
Computing in Science and Engineering
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Power consumption in GPUs based cluster became the major obstacle in the adoption of high productivity GPU accelerators in the high performance computing industry. The power consumed by GPU chips represent about 75% of the total GPU based cluster power consumption. This is due to the fact that the GPU cards are often configured at peak performance, and consequently, they will be active all the time. In this paper, the authors present a holistic power and performance management framework that reduces power consumption of the GPU based cluster and maintains the system performance within an acceptable predefined threshold. The framework dynamically scales the GPU cluster to adapt to the variation of incoming workload's requirements and increase the idleness of the of GPU devices, allowing them to transition to low-power state. The proposed power and performance management framework in GPU cluster demonstrated 46.3% power savings for GPU workload while maintaining the cluster performance. The overhead of the proposed framework is insignificant on the normal application\system operations and services.