PLDI '96 Proceedings of the ACM SIGPLAN 1996 conference on Programming language design and implementation
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Edge profiling versus path profiling: the showdown
POPL '98 Proceedings of the 25th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Improving data-flow analysis with path profiles
PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
ESP: path-sensitive program verification in polynomial time
PLDI '02 Proceedings of the ACM SIGPLAN 2002 Conference on Programming language design and implementation
POPL '80 Proceedings of the 7th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Dataflow Frequency Analysis Based on Whole Program Paths
Proceedings of the 2002 International Conference on Parallel Architectures and Compilation Techniques
A Novel Probabilistic Data Flow Framework
CC '01 Proceedings of the 10th International Conference on Compiler Construction
An Efficient Online Path Profiling Framework for Java Just-In-Time Compilers
Proceedings of the 12th International Conference on Parallel Architectures and Compilation Techniques
A probabilistic pointer analysis for speculative optimizations
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Sound, complete and scalable path-sensitive analysis
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Profiling k-Iteration Paths: A Generalization of the Ball-Larus Profiling Algorithm
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
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Speculative optimizations are increasingly becoming popular for improving program performance by allowing transformations that benefit frequently traversed program paths. Such optimizations are based on dataflow facts which are mostly true, though not always safe. Probabilistic dataflow analysis frameworks infer such facts about a program, while also providing the probability with which a fact is likely to be true. We propose a new Probabilistic Dataflow Analysis Framework which uses path profiles and information about the nesting structure of loops to obtain improved probabilities of dataflow facts.