Abstract: Traditional performance analysis methods for high performance applications have shortcomings such as additional overhead during the analysis process and inaccurate analysis results, resulting in users spending more time and domain knowledge. To address these issues, this paper transforms the problem of program performance analysis into a multi-classification problem of unbalanced small sample datasets under high-dimensional features. By collecting 500 pieces of performance data that include seven types of metrics such as the number of process switches, memory utilization, and disk I/O load during program runtime, after data preprocessing such as PCA dimensionality reduction, a program performance problem analysis model is trained using a random forest classifier. Experimental validation shows that the model can identify five types of performance issues, including excessive memory utilization and heavy disk I/O load. To evaluate the effectiveness of the models guidance, this paper collects performance data generated by the HotSpot3D program and the LU-Decomposition program during runtime. Based on the models output guidance, the two validation programs are optimized at the runtime level and the compilation level. Experimental results indicate that the proposed method can effectively guide the optimization of program performance, with speedup ratios of 1.056 and 5.657 for the two programs, respectively.

Key words: Nmon, performance analysis, variational autoencoder, cluster, random forest