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Energy Efficient For Cloud Based GPU Using DVFS With Snoopy Protocol

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Energy Efficient For Cloud Based GPU Using DVFS With Snoopy Protocol

Prof. Avinash Sharma | Anchal Pathak

Prof. Avinash Sharma | Anchal Pathak "Energy Efficient For Cloud Based GPU Using DVFS With Snoopy Protocol" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18412.pdf

GPU design trends show that the register file size will continue to increase to enable even more thread level parallelism. As a result register file consumes a large fraction of the total GPU chip power. It explores register file data compression for GPUs to improve power efficiency. Compression reduces the width of the register file read and writes operations, which in turn reduces dynamic power. This work is motivated by the observation that the register values of threads within the same warp are similar, namely the arithmetic differences between two successive thread registers is small. Compression exploits the value similarity by removing data redundancy of register values. Without decompressing operand values some instructions can be processed inside register file, which enables to further save energy by minimizing data movement and processing in power hungry main execution unit. Evaluation results show that the proposed techniques save 25% of the total register file energy consumption and 21% of the total execution unit energy consumption with negligible performance impact. Performance and energy efficiency are major concerns in cloud computing data centers. More often, they carry conflicting requirements making optimization a challenge. Further complications arise when heterogeneous hardware and data center management technologies are combined. For example, heterogeneous hardware such as General Purpose Graphics Processing Units (GPGPUs) improved performance at the cost of greater power consumption while virtualization technologies improve resource management and utilization at the cost of degraded performance.

GPU, Energy Consumption, Data Redundancy, Compression, Cloud Computing, General Purpose Graphics Processing Units.


Volume-2 | Issue-6 , October 2018

2456-6470

IJTSRD18412