An iterative clustering based approach for parallel performance analysis

With the development of supercomputers, the CPU core numbers of which come to several hundreds of thousands, and on which the complexity of the applications run are increasing. Therefore, in order to optimize the source code of the programs, developers of parallel applications need to measure the performance of parallel applications and make a useful analysis, so that they can improve the performance of the applications. However, due to a substantial increasing of the CPU core numbers, performance measurement will produce vast amounts of performance data, and then, how to deal with massive data is a very critical problem for parallel performance analysis. A new approach, named Iterative based Clustering Approach for Parallel Performance Analysis (ICAPPA), is proposed for parallel performance analysis in this paper. In this approach, clustering method of data mining technique, which is used to processing massive data, will be carried out iteratively for the result in some conditions after previous clustering, to find out the dominating functions and processes of the parallel performance. And Bayesian Information Criteria (BIC) is applied to evaluate the result of clustering method. By using BIC score, whether iterative clustering applied to the result is reliable or not can be decided. And at the end of this paper, the validity of that approach is verified by experimental analysis.