Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
A Bibliography of Parallel Debuggers, 1990 Edition
A Bibliography of Parallel Debuggers, 1990 Edition
Event graph visualization for debugging large applications
SPDT '96 Proceedings of the SIGMETRICS symposium on Parallel and distributed tools
Using interaction networks for visualisation of message passing
SIGCSE '97 Proceedings of the twenty-eighth SIGCSE technical symposium on Computer science education
High-Level Views of Distributed Executions: Convex Abstract Events
Automated Software Engineering
Concurrent single stepping in event-visualization tools
Cluster Computing
Single stepping in event-visualization tools
CASCON '96 Proceedings of the 1996 conference of the Centre for Advanced Studies on Collaborative research
CASCON '94 Proceedings of the 1994 conference of the Centre for Advanced Studies on Collaborative research
An Open Visual Model for Object-Oriented Operating Systems
IWOOOS '95 Proceedings of the 4th International Workshop on Object-Orientation in Operating Systems
Vector time and causality among abstract events in distributed computations
Distributed Computing
Stacked-widget visualization of scheduling-based algorithms
Proceedings of the 4th ACM symposium on Software visualization
Debugging complex software systems by means of pathfinder networks
Information Sciences: an International Journal
Tracking causality by visualization of multi-agent interactions using causality graphs
ProMAS'07 Proceedings of the 5th international conference on Programming multi-agent systems
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A visualization tool that provides an aggregate view of execution through a graph of events called the causality graph, which is suitable for systems with hundreds or thousands of processors, coarse-grained parallelism, and for a language that makes communication and synchronization explicit, is discussed. The methods for computing causality graphs and stepping through an execution with causality graphs are described. The properties of the abstraction algorithms and super nodes, the subgraphs in causality graphs, are also discussed.