Developing efficient parallel applications is critical to advancing scientific development but requires significant performance analysis and optimization. Performance analysis tools help developers manage the increasing complexity and scale of performance data, but often rely on the user to manually explore low-level data and are rigid in how the data can be manipulated. We propose a Python-based API, Chopper, which provides high-level and flexible performance analysis for both single and multiple executions of parallel applications. Chopper facilitates performance analysis and reduces developer effort by providing configurable high-level methods for common performance analysis tasks such as calculating load imbalance, hot paths, scalability bottlenecks, and causes of performance variability within a robust and mature Python environment that provides fluid access to lower-level data manipulations. We demonstrate how Chopper allows developers to quickly and succinctly explore performance.
Slides will be available for download here after the presentation.
Onur Cankur is a PhD student at the University of Maryland. He is working on high-performance computing (HPC) at Parallel Software and Systems Group with Abhinav Bhatele. His main interest is in performance analysis and modeling of large-scale parallel applications. He is also interested in resource usage analysis and modeling of HPC systems.