摘要
为精准快速地获得GPU功耗数据,提出一种基于硬件性能计数事件的通用图形处理器(GPGPU)功耗估算方法。通过分析GPGPU程序运行时的功耗分布情况,选择一组与应用程序运行功耗密切相关的硬件性能计数事件集合,使用反向传播人工神经网络分析硬件性能计数事件与实时功耗间的关系,最终建立GPGPU功耗估算模型。实验结果表明,与多元线性回归的功耗估算模型相比,该模型具有更高的估算准确性和通用性。
In order to get the GPU power data quickly and accurately,this paper proposes a General Purpose Graphics Processing Unit（GPGPU） power estimation model based on hardware performance counting events.Through analysing power distribution during GPGPU program running,it selects a set of performance events which are closely related to application program running power.Then it figures out the relationship between hardware performance counting events and realtime power using Back Propagation Atificial Neural Network（BP—ANN）.Finally,it builds a GPGPU power estimation model.Experimental results indicate that compared with the Multiple Linear Regression（MLR） power estimation model,the proposed model has higher estimation accuracy and versatility.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第2期92-97,104,共7页
Computer Engineering
关键词
通用图形处理器
硬件性能计数事件
反向传播人工神经网络
交叉验证
功耗估算
General Purpose Graphics Processing Unit（GPGPU）
hardware performance counting event
Back Propagation Atificial Neural Network（BP-ANN）
cross-validation
power estimation
