When Parallel Performance Measurement and Analysis Meets In Situ Analytics and Visualization

Large scale parallel applications have evolved beyond the tipping point where there are compelling reasons to analyze, visualize and otherwise process output data from scientific simulations in situ rather than writing data to filesystems for post-processing. This modern approach to in situ integration is served by recently developed technologies such as Ascent, which is purpose-built to transparently integrate runtime analysis and visualization into many different types of scientific domains. The TAU Performance System (TAU) is a comprehensive suite of tools that have been developed to measure the performance of large scale parallel libraries and applications. TAU is widely-adopted and available on leading-edge HPC platforms, but has traditionally relied on post-processing steps to visualize and understand application performance. In this paper, we describe the integration of Ascent and TAU for two complementary purposes: Analyzing Ascent performance as it serves the visualization needs of scientific applications, and visualizing TAU performance data at runtime. We demonstrate the immediate benefits of this in situ integration, reducing the time to insight while presenting performance data in a perspective familiar to the application scientist. In the future, the integration of TAU's performance observations will enable Ascent to reconfigure its behavior at runtime in order to consistently stay within user-defined performance constraints while processing visualizations for complex and dynamic HPC applications.