Techniques in scalable and effective parallel performance analysis

Parallel Performance Tools;Scalability;Performance Analysis;High Performance Computing (HPC)

Lee, Chee Wai

关键词: Parallel Performance Tools;    Scalability;    Performance Analysis;    High Performance Computing (HPC);   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/14568/Lee_CheeWai.pdf?sequence=2&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Performance analysis tools are essential to the maintenance ofefficient parallel execution of scientific applications. As scientific applications are executed on larger and larger parallel supercomputers, it is clear that performance tools must employ more advanced techniques to keep up with the increasing data volume and complexity of the performance information generated by these applications as a result of scaling.In this thesis, we investigate the useful techniques in four mainthrusts to address various aspects of this problem. First, we studyhow some traditional performance analysis idioms can break down in the face of data from large processor counts and demonstrate techniques and tools that restore scalability. Second, we investigate how the volume of performance data generated can be reduced while keeping thecaptured information relevant for analysis and performance problemdetection. Third, we investigate the powerful new performance analysis idioms enabled by live access to performance information streams from a running parallel application. Fourth, we demonstrate how repeatedperformance hypothesis testing can be conducted, via simulationtechniques, scalably and with significantly reduced resourceconsumption. In addition, we explore the benefits of performance tool integration to the propagation and synergy of scalable performanceanalysis techniques in different tools.