Run-Time Parallelization and Scheduling of Loops
IEEE Transactions on Computers
Improving the performance of runtime parallelization
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Probabilistic predicate transformers
ACM Transactions on Programming Languages and Systems (TOPLAS)
Concurrent constraint programming: towards probabilistic abstract interpretation
Proceedings of the 2nd ACM SIGPLAN international conference on Principles and practice of declarative programming
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Hybrid analysis: static & dynamic memory reference analysis
ICS '02 Proceedings of the 16th international conference on Supercomputing
Probabilistic Concurrent Constraint Programming: Towards a Fully Abstract Model
MFCS '98 Proceedings of the 23rd International Symposium on Mathematical Foundations of Computer Science
Abstract Interpretation of Probabilistic Semantics
SAS '00 Proceedings of the 7th International Symposium on Static Analysis
Toward efficient and robust software speculative parallelization on multiprocessors
Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming
The R-LRPD Test: Speculative Parallelization of Partially Parallel Loops
IPDPS '02 Proceedings of the 16th International Symposium on Parallel and Distributed Processing
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Parallelizing compilers have difficulty analysing and optimising complex code. To address this, some analysis may be delayed until run-time, and techniques such as speculative execution used. Furthermore, to enhance performance, a feedback loop may be setup between the compile time and run-time analysis systems, as in iterative compilation. To extend this, it is proposed that the run-time analysis collects information about the values of variables not already determined, and estimates a probability measure for the sampled values. These measures may be used to guide optimisations in further analyses of the program. To address the problem of variables with measures as values, this paper also presents an outline of a novel combination of previous probabilistic denotational semantics models, applied to a simple imperative language.