电子学报
電子學報
전자학보
Acta Electronica Sinica
2015年
10期
1904-1910
,共7页
能耗控制%GPU集群%能量消减%模型预测
能耗控製%GPU集群%能量消減%模型預測
능모공제%GPU집군%능량소감%모형예측
power consumption control%graphic processing unit (GPU)clusters%power capping%model prediction control
随着大数据技术的发展，GPU 集群作为一种高效的并行系统被应用到大规模数据实时计算中。能量是实时计算时重要的资源，GPU 集群的能耗优化及实时消减成为一个具有挑战性的问题。从集群全局角度引入模型预测控制策略，并建立闭环反馈机制的多输入多输出控制器。通过调整计算频率和改变活跃流多处理器来改变能耗状态，利用反馈和滚动优化机制完成对未来的控制预判，实现消减冗余能耗的目标。实验表明：控制模型的精度和节能效果优于基准模型，而且具有较好的稳定性，适合应用到大规模数据实时计算中。
隨著大數據技術的髮展，GPU 集群作為一種高效的併行繫統被應用到大規模數據實時計算中。能量是實時計算時重要的資源，GPU 集群的能耗優化及實時消減成為一箇具有挑戰性的問題。從集群全跼角度引入模型預測控製策略，併建立閉環反饋機製的多輸入多輸齣控製器。通過調整計算頻率和改變活躍流多處理器來改變能耗狀態，利用反饋和滾動優化機製完成對未來的控製預判，實現消減冗餘能耗的目標。實驗錶明：控製模型的精度和節能效果優于基準模型，而且具有較好的穩定性，適閤應用到大規模數據實時計算中。
수착대수거기술적발전，GPU 집군작위일충고효적병행계통피응용도대규모수거실시계산중。능량시실시계산시중요적자원，GPU 집군적능모우화급실시소감성위일개구유도전성적문제。종집군전국각도인입모형예측공제책략，병건립폐배반궤궤제적다수입다수출공제기。통과조정계산빈솔화개변활약류다처리기래개변능모상태，이용반궤화곤동우화궤제완성대미래적공제예판，실현소감용여능모적목표。실험표명：공제모형적정도화절능효과우우기준모형，이차구유교호적은정성，괄합응용도대규모수거실시계산중。
With the development of Big Data technology GPU cluster as a high efficiency parallel system applies into the Large-scale data computing field.Energy is a significant computation resource.So power consumption optimization control and cap-ping in real-time becomes a challenge issue.The Model Prediction Control strategy is introduced and a Multi-Input Multi-Output controller is built by using a closed loop feedback principle from the whole cluster perspective.Power consumption status is changed by scaling frequency and adjusting active stream multi-processors.Then the feedback and the periodic optimization mechanisms can predict the control behaviors in the future control cycles.This achieves the goal that reduces redundancy energy.The results demon-strate that the proposed model has more accuracy and comsumes less energy than the others.And it has better control stability.So it has better adaptability and obvious advantage in the Large-scale data real-time computing.
