Toward Aggregated Grain Graphs
Workshop: 4th International Workshop on Visual Performance Analytics – VPA 2017
Authors: Ananya Muddukrishna (Norwegian University of Science and Technology)
Abstract: Grain graphs simplify OpenMP performance analysis by visualizing performance problems from a fork-join perspective that is familiar to programmers. However, it is tedious to navigate and diagnose problems in large grain graphs with thousands of task and parallel for-loop chunk instances. We present an aggregation method that matches recurring patterns in grain graphs and groups related nodes together, reducing graphs of any size to one root group. The aggregated grain graph is then navigated by progressively uncovering groups and analyzing only those groups that have problems. This enhances productivity by enabling programmers to understand program structure and problems in large grain graphs with less effort than before.