VISUAL ANALYTICS FOR LARGE COMMUNICATION TRACE DATA

Executions of modern parallel programs often yield complex communications among compute nodes of large-scale clusters of workstations or supercomputers. Analyzing communication patterns is becoming increasingly critical to performance optimiza-tion. As the scale and complexity of parallel applications drastically increases, visu-alization has become a feasible means to conduct analysis of massive communication patterns. However, most visualization tools fall short in showing comprehensive dy-namic communication graph and addressing the scalability issue. Our solution for analyzing dynamic communication patterns is based on an analytics framework cou-pled with a new visualization technique, named CommGram [29], 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 massiv