Jieting Wu
Committee Members: Dr. Hongfeng Yu (Advisor)
Dr. David Swanson and Dr. Ying Lu
Tuesday, April 14, 2:00pm
347 Avery Hall
Abstract
Execution of modern parallel programs often yields complex communications among compute nodes of large-scale workstations or supercomputers. Analyzing communication patterns is becoming increasingly critical to performance optimization. As the scale and complexity of parallel applications drastically increases, visualization has become a feasible means to conduct analysis of massive communication patterns. However, most visualization tools fall short in showing comprehensive dynamic communication graphs and addressing the scalability issue. Our solution for analyzing dynamic communication patterns is based on an analytics framework coupled with a new visualization technique, named CommGram, that provides a flexible solution to the scalability issue. We can explore large communication data at different levels of detail, and detect potential communication bottlenecks of massive parallel programs. The conclusion of our studies is based on large-scale scientific applications that include end-to-end simulation pipelines and AMR-based simulations.