Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Who are the variables in your neighborhood
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
Design of experiments in BDD variable ordering: lessons learned
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
A Brief Study of BDD Package Performance
FMCAD '96 Proceedings of the First International Conference on Formal Methods in Computer-Aided Design
VIS: A System for Verification and Synthesis
CAV '96 Proceedings of the 8th International Conference on Computer Aided Verification
Cloning-based context-sensitive pointer alias analysis using binary decision diagrams
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Jedd: a BDD-based relational extension of Java
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Efficient Forward Computation of Dynamic Slices Using Reduced Ordered Binary Decision Diagrams
Proceedings of the 26th International Conference on Software Engineering
A Case for Compressing Traces with BDDs
IEEE Computer Architecture Letters
Context-sensitive pointer analysis using binary decision diagrams
Context-sensitive pointer analysis using binary decision diagrams
Visualizing potential parallelism in sequential programs
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Integrating profile-driven parallelism detection and machine-learning-based mapping
ACM Transactions on Architecture and Code Optimization (TACO)
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Prior work has shown that reduced, ordered, binary decision diagrams (BDDs) can be a powerful tool for program trace analysis and visualization. Unfortunately, it can take hours or days to encode large traces as BDDs. Further, techniques used to improve BDD performance are inapplicable to large dynamic program traces. This paper explores the use of ZDDs for compressing dynamic trace data. Prior work has show that ZDDs can represent sparse data sets with less memory compared to BDDs. This paper demonstrates that (1) ZDDs do indeed provide greater compression for sets of dynamic traces (25% smaller than BDDs on average), (2) with proper tuning, ZDDs encode sets of dynamic trace data over 9x faster than BDDs, and (3) ZDDs can be used for all prior applications of BDDs for trace analysis and visualization.