Spectral analysis of executions of computer programs and its applications on performance analysis

This work is motivated by the growing intricacy of high performance computing infrastructures. For example, supercomputer MareNostrum (installed in 2005 at BSC) has 10240 processors and currently there are machines with more than 100.000 processors. The complexity of this systems increases the complexity of the manual performance analysis of parallel applications. For this reason, it is mandatory to use automatic tools and methodologies.The performance analysis group of BSC and UPC has a large experience in analyzing parallel applications. The approach of this group consists mainly in the analysis of tracefiles (obtained from parallel applications executions) using performance analysis and visualization tools, such as Paraver. Taking into account the general characteristics of the current systems, this method can sometimes be very expensive in terms of time and inefficient. To overcome these problems, this thesis makes several contributions.The first one is an automatic system able to detect the internal structure of executions of high performance computing applications. This automatic system is able to rule out nonsignificant regions of executions, to detect redundancies and, finally, to select small but significant execution regions. This automatic detection process is based on spectral analysis (wavelet transform, fourier transform, etc..) and works detecting the most important frequencies of the application's execution. These main frequencies are strongly related to the internal loops of the application' source code. Finally, it is important to state that an automatic detection of small but significant execution regions reduces remarkably the complexity of the performance analysis process.The second contribution is an automatic methodology able to show general but nontrivial performance trends. They can be very useful for the analyst in order to carry out a performance analysis of the application. The automatic methodology is based on an analytical model. This model consists in several performance factors. Such factors modify the value of the linear speedup in order to fit the real speedup. That is, if this real speedup is far from the linear one, we will detect immediately which one of the performance factors is undermining the scalability of the application. The second main characteristic of the analytical model is that it can be used to predict the performance of high performance computing applications. From several execution on a few of processors, we extract model's performance factors and we extrapolate these values to executions on higher number of processors. Finally, we obtain a speedup prediction using the analytical model.The third contribution is the automatic detection of the optimal sampling frequency of applications. We show that it is possible to extract this frequency using spectral analysis. In case of sequential applications, we show that to use this frequency improves existing results of recognized techniques focused on the reduction of serial application's instruction execution stream (SimPoint, Smarts, etc..). In case of parallel benchmarks, we show that the optimal frequency is very useful to extract significant performance information very efficiently and accurately.In summary, this thesis proposes a set of techniques based on signal processing. The main focus of these techniques is to perform an automatic analysis of the applications, reporting and initial diagnostic of their performance and showing their internal iterative structure. Finally, these methods also provide a reduced tracefile from which it is easy to start manual finegrain performance analysis. The contributions of the thesis are not reduced to proposals and publications. The research carried out these last years has provided a tool for analyzing applications' structure. Even more, the methodology is general and it can be adapted to many performance analysis methods, improving remarkably their efficiency, flexibility and generality.Postprint (published version