Automated performance optimization based on post-mortem parallel performance analysis

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Post-mortem parallel performance analysis allows to identify performance bottlenecks in the parallel program execution. Despite recent advances in performance analysis automation, a significant human effort is still required to take advantage of the analysis results and optimize programs. The thesis will investigate new automation techniques for static and dynamic performance optimization, leveraging the program execution analysis tools and libraries developed in the APT group. These tools employ artificial neural networks to automatically identify performance anomalies and their likely causes, but still require an expert programmer to intervene in order to improve performance. This is a time-consuming, error-prone task which this project aims to automate.The aim of the project is to investigate the possibility of replacing heuristic approaches for dynamic program optimization, in particular targeting the problems of work scheduling and data placement in non-uniform memory access architecture systems as well as in large scale distributed memory systems, with more robust machine learning techniques that can automatically adapt to either new execution environments or to dynamically evolving execution conditions (e.g. node failure requiring to re-balance the load, dynamic application behaviour such as in mesh refinement creating run-time imbalance, etc.). Previous work at The University of Manchester has shown that it is possible to automatically detect the occurrence of such events and, to some extent, to identify the causes of performance degradation. This presents an exciting opportunity to build on top of this research and to automate the last missing piece.The main source of the input data to the optimizer will be ComPerf and this thesis will investigate how identified bottlenecks can be mitigated. It can either involve recompiling the application code, modification of the program binary or changing the schedule and data placement in the parallel execution. In a first stage, a set of benchmarks will be selected to establish the execution baseline. It will include publicly available applications, but also can be extended with applications that better shows effectiveness of the applied optimizations. Large-scale applications from the EU project EuroEXA (754337), such as weather and climate modeling, physics and life science simulation. Three leading metrics will be used to asses the optimizer: throughput, latency and power efficiency.The use of Artificial Neural Networks has a potential to be a suitable tool to automate the performance optimization process as they require minimum assumptions and are able to effectively generalize based on incomplete data. One of the possible training settings is to use execution traces and initial application information as an input and a reference optimization as the target. Finally, this thesis aims to show that such approaches can be used to generalize over different architectures, from off-the-shelf Intel platforms to the EuroEXA supercomputer prototype.