Causality filters: a tool for the online visualization and steering of parallel and distributed programs

Abstract

Interactive program steering is a promising technique for improving the performance of parallel and distributed applications. Steering decisions are typically based on visual presentations of some subset of the computation's current state, a historical view of the computation's behavior or views of metrics based on the program's performance. As in any endeavor good decisions require accurate information. However the distributed nature of the collection process may result in distortions in the portrayal of the program's execution. These distortions stem from the merging of streams of information from distributed collection points into a single stream without enforcing the ordering relationships that held among the program components that produced the information. An ordering filter placed at the point at which the streams are merged can ensure a valid ordering, leading to more accurate visualizations and better informed steering decisions. In this paper we describe the implementation of such filters in the Falcon interactive steering toolkit, and present a methodology for their specification for automated generation.