CommGram: A New Visual Analytics Tool for Large Communication Trace Data

Abstract

The performance of massively parallel program is often impacted by the cost of communication across computing nodes. Analysis of communication patterns is critical for understanding and optimizing massively parallel programs. Visualization can help identify potential communication bottlenecks by displaying message trace data. However, the visual clutter and temporal incoherence problems are typically incurred in existing visualization tools for a considerable number of processors. In this paper, we present a new tool, named CommGram, which supports visual analysis of communication patterns for massive parallel MPI programs. With the benefit of MPI trace library DUMPI of SST, our framework builds hierarchical clustering trees for computational community domain, and takes advantage of graphical user interface (GUI) to convey communication patterns at different levels of detail. The effectiveness of our tool is demonstrated using large-scale parallel applications.