Research Posters Archive

Two-Phase IO Enabling Large-Scale Performance Introspection

Authors: Ke Fan and Sidharth Kumar (University of Illinois, Chicago)

Abstract: Numerous sophisticated profiling and visualization tools have been developed to enable programmers to expose semantic information from their application components. However, effective and interactive exploration of the profiles of large-scale parallel programs remains a challenge due to the high I/O overheads of profiles and the difficulties in scaling downstream visualization tools. In this poster, we present a full-stack approach to a performance introspection framework that tackles key challenges in profiling and visualizing performance data at scale. Our novelty lies in a scalable and compact data model and a two-phase I/O system, which instill scalability into the profiler making it low overhead-- even at high process counts (< 5%). We then build a web-based, visual-analytic dashboard with linked views. Our profiling and visualization tools are both lightweight and easy-to-use, which strikes a balance between providing sophisticated features and operating quickly and efficiently at high process counts.

Best Poster Finalist (BP): yes

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