A dependence graph pattern mining method for processor performance analysis

Abstract

As the complexity of processor microarchitecture and applications increases, obtaining performance optimization knowledge, such as critical dependent chains, becomes more challenging. To tackle this issue, this paper employs pattern mining methods to analyze the critical path of processor micro-execution dependence graphs. We propose a high average utility pattern mining algorithm called Dependence Graph Miner (DG-Miner) based on the characteristics of dependence graphs. DG-Miner overcomes the limitations of current pattern mining algorithms for dependence graph pattern mining by offering support for variable utility, candidate generation using endpoint matching, the adjustable upper bound, and the concise pattern judgment mechanism. Experiments reveal that, compared with existing upper bound candidate generation methods, the adjustable upper bound reduces the number of candidate patterns by 28.14% and the running time by 27% on average. The concise pattern judgment mechanism enhances the conciseness of mining results by 16.31% and reduces the running time by 39.82%. Furthermore, DG-Miner aids in identifying critical dependent chains, critical program regions, and performance exceptions.