Power and Performance Management of GPUs Based Cluster

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.

[1]  A. George,et al.  Computational Density of Fixed and Reconfigurable Multi-Core Devices for Application Acceleration , 2008 .

[2]  Ye Zhao,et al.  Parallel 3D Image Segmentation of Large Data Sets on a GPU Cluster , 2009, ISVC.

[3]  Héctor Alaiz-Moretón,et al.  Technical Audit of an Electronic Polling Station: A Case Study , 2011, Int. J. E Serv. Mob. Appl..

[4]  Stuart D. Galup,et al.  Technological Applications and Advancements in Service Science, Management, and Engineering , 2012 .

[5]  Matthew Arrott.  National Center for Supercomputer Applications , 1991 .

[6]  Issachar Gilad,et al.  A Manpower Allocation Model for Service Jobs , 2012, Int. J. Serv. Sci. Manag. Eng. Technol..

[7]  Nicole B. Koppel,et al.  InformatIon SyStemS In the ServIce Sector , 2010 .

[8]  Hyesoon Kim,et al.  An integrated GPU power and performance model , 2010, ISCA.

[9]  Klaus Schulten,et al.  High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs , 2009, GPGPU-2.

[10]  Robert Strzodka,et al.  Exploring weak scalability for FEM calculations on a GPU-enhanced cluster , 2007, Parallel Comput..

[11]  William Gropp,et al.  EcoG: A Power-Efficient GPU Cluster Architecture for Scientific Computing , 2011, Computing in Science & Engineering.

[12]  Daniela Carlucci,et al.  Assessing and Managing Organizational Climate in Healthcare Organizations: An Intellectual Capital Based Perspective , 2012, Int. J. Inf. Syst. Serv. Sect..

[13]  Hyesoon Kim,et al.  An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness , 2009, ISCA '09.

[14]  Yale N. Patt,et al.  Feedback-driven threading: power-efficient and high-performance execution of multi-threaded workloads on CMPs , 2008, ASPLOS.

[15]  Ulrike von Luxburg,et al.  Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions , 2009, J. Mach. Learn. Res..

[16]  Yannis Charalabidis,et al.  Interoperability in Digital Public Services and Administration: Bridging E-Government and E-Business , 2010 .

[17]  Klaus Schulten,et al.  Accelerating Molecular Modeling Applications with GPU Computing , 2009 .

[18]  Christoph Schroth,et al.  Advancing Interoperability for Agile Cross-Organisational Collaborations: A Rule-Based Approach , 2010 .

[19]  Marijn Janssen,et al.  A Reference Architecture for Interoperable and Adaptive Processes , 2011 .

[20]  Sudhakar Yalamanchili,et al.  Modeling GPU-CPU workloads and systems , 2010, GPGPU-3.

[21]  Kevin Skadron,et al.  A flexible simulation framework for graphics architectures , 2004, Graphics Hardware.

[22]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[23]  Wook-Shin Han,et al.  Efficient feature weighting methods for ranking , 2009, CIKM.

[24]  Jun Fang,et al.  An Approach to Deploying SOA in Technological Information Integration: A Case Study , 2010, Int. J. Serv. Sci. Manag. Eng. Technol..

[25]  John E. Stone,et al.  GPU clusters for high-performance computing , 2009, 2009 IEEE International Conference on Cluster Computing and Workshops.

[26]  John E. Stone,et al.  Quantifying the impact of GPUs on performance and energy efficiency in HPC clusters , 2010, International Conference on Green Computing.

[27]  Arie E. Kaufman,et al.  GPU Cluster for High Performance Computing , 2004, Proceedings of the ACM/IEEE SC2004 Conference.

[28]  Zsófia Osváth,et al.  DOI: 10 , 2011 .

[29]  Ian D. Watson,et al.  An Introduction to Case-Based Reasoning , 1995, UK Workshop on Case-Based Reasoning.

[30]  Jason Cong,et al.  FCUDA: Enabling efficient compilation of CUDA kernels onto FPGAs , 2009, 2009 IEEE 7th Symposium on Application Specific Processors.

[31]  Volodymyr Kindratenko,et al.  QP: A Heterogeneous Multi-Accelerator Cluster , 2011 .

[32]  Song Huang,et al.  On the energy efficiency of graphics processing units for scientific computing , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.