基于错误传播知识图的GPU程序软错误弹性预测OA

The reduction of internal unit sizes of computer makes the devices more susceptible to short-channel effects,current noise and electromagnetic interference,leading to transient bit-flip errors(also known as soft errors).Some of soft errors may result in silent data corruption,which is a serious threat to the reliability of computer systems.Therefore,it is urgent to predict and evaluate the impact of soft errors effectively.Compared with CPUs,the semantics of GPU are highly coupled with the parallel behavior between threads,and thus the propagation of soft errors is more complex.That makes it difficult for existing soft error resilience assessment methods to be applied at the GPU assembly level.To address the problem,a soft error resilience prediction model for GPU programs,EPKGP(soft error resilience prediction model with error propagation knowledge graph),is constructed based on an error propagation knowledge graph,so as to predict the soft error resilience of program fault points at SASS(streaming assembly)assembly level.Firstly,large-scale fault injection experiments are carried out to analyze and extract dynamic heuristic information from data,such as instruction types,bit-flip positions,bit-flip types and thread block numbers.Then,a SASS assembly code corpus is constructed to pre-train the Word2Vec model,mine abundant semantic knowledge and obtain static instruction semantic embedding features.Finally,this paper constructs an error propagation knowledge graph by virtue of the control flow and data dependency relationships between program instructions.The two heterogeneous features are fused as input and the graph convolutional network(GCN)is utilized to efficiently predict soft error resilience of GPU programs.Experiments show that compared with existing soft error prediction methods like G-SEPM,the accuracy of EPKGP is increased by up to 4.05 percentage points and the F1 score rises by up to 3.92 percentage points.