Power Analysis And Optimization Of The General Purpose Computing Of Graphics Processing Unit

With the development of Big Data the real-time processing of Large-scale streamsdata appears various application fields, such as social network analysis, abnormalstreams detection and abnormal context detection in network security and video qualityanalysis. Due to the general-purpose computing development of GPUs and the fact thatdata intensive computing is very suitable to the GPUs, both single GPU and GPUclusters have become significantly parallel computing schemes to process theLarge-scale real-time streams. Energy is an important computing resource in thereal-time processing that limits the system reliability and extensibility. So the powerconsumption management and optimization need to be solved imminently. This workbelongs to the green computing field.This paper mainly focuses on the power consumption management and optimization.We study the computing power consumption and optimization from power measurement,power consumption prediction, power-aware parallel strategies to GPU cluster powerconsumption control. Power measurement and prediction are the basic issue of thepower consumption management and optimization. The mainly research work is poweroptimization and real-time control and the difficult point is the tradeoff between thecomputing performance and the reliability. We firstly summarize the key techniques inGPGPU and discuss study methods and tools in program model, memory model,communication model and load balance based on the development of GPUs architecture.This work supports the power consumption optimization and research about the systemreliability. The contributions of this paper include as follows.1. We propose two different power consumption prediction schemes. The first one isto analyze power consumption feature from the PTX level and to count the dynamicinstruction number by unrolling the simple loop structure. This approach is simple andgeneral prediction model. The second prediction model is based on program slice fromthe program source code level. This method firstly decomposes the programs into manyslices and builds the slice prediction model by nonlinear regression and wavelet neuralnetwork. The contribution of the second model is that distinguishes the program controlstructure. And the branch-sparseness and branch-densense models are built respectivelyin order to improve the prediction accuracy.2. Aiming at Large-scale real data processing we propose two general parallel processstrategies on single GPU and can be applied into various algorithms. Here complex networks clustering algorithm is used to verfiy those parallel processing strategies.Additionally, we analyze the power consumption of the two different parallel strategiesand provide the application scenes. Finally, the fault detection and recovery mechanismare proposed to guarantee the system reliability.3. Power consumption optimization control system is designed based on the ModelPrediction control theory that may be adapted to the variation of workloads. Thiscontroller can reduce the redundancy power consumption in real-time computing. Webuild Honeynet to capture abnormal network packets to verify the validity of the powerconsumption control system.4. Reliability-aware power consumption controller is proposed by using maximizeentropy method to combine performance, reliability and power consumption as acomprehensive control variable. This control system reduces the reliability cost due tothe power state adjusting mechanism. This method can overcome the limitation of thetraditional approach that transforms the multi-objective function into single-objectivefunction and distinguish the solutions quality. This controller can dynamically adjust thepower state of the GPU cluster and achieve the best status in the performance, reliabilityand power consumption.