Application-specific feature selection and clustering approach with HPC system profiling data

Exascale computing, the next-generation computing environment, is expected to be applied to scientific and engineering applications. Accordingly, high-performance computing (HPC) technology is also being developed to improve the performance and high-speed parallelism of many-core processors. Previous researches on improving HPC performance have developed in the form of improving the overall system performance by analyzing the state of the system occurring in the range of the knowledge of expert. However, performance events occurring in a processor in a many-core environment have a large number of indicators, and it is difficult to analyze the correlation between them. In this paper, we propose an application-specific feature selection and clustering approach with HPC system profiling data. The proposed approach performs PCA-based feature selections for efficient performance analysis methods. In addition, the application-specific characteristics from profiling data can be analyzed by unsupervised learning. In our experiments, we evaluated highly parallel supercomputers with NAS parallel benchmark and were able to cluster applications efficiently.