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Bounded expectations: resource analysis for probabilistic programs

Authors:

Quentin Carbonneaux,

Jan HoffmannAuthors Info & Claims

PLDI 2018: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 496 - 512

https://doi.org/10.1145/3192366.3192394

Published: 11 June 2018 Publication History

Abstract

This paper presents a new static analysis for deriving upper bounds on the expected resource consumption of probabilistic programs. The analysis is fully automatic and derives symbolic bounds that are multivariate polynomials in the inputs. The new technique combines manual state-of-the-art reasoning techniques for probabilistic programs with an effective method for automatic resource-bound analysis of deterministic programs. It can be seen as both, an extension of automatic amortized resource analysis (AARA) to probabilistic programs and an automation of manual reasoning for probabilistic programs that is based on weakest preconditions. An advantage of the technique is that it combines the clarity and compositionality of a weakest-precondition calculus with the efficient automation of AARA. As a result, bound inference can be reduced to off-the-shelf LP solving in many cases and automatically-derived bounds can be interactively extended with standard program logics if the automation fails. Building on existing work, the soundness of the analysis is proved with respect to an operational semantics that is based on Markov decision processes. The effectiveness of the technique is demonstrated with a prototype implementation that is used to automatically analyze 39 challenging probabilistic programs and randomized algorithms. Experiments indicate that the derived constant factors in the bounds are very precise and even optimal for some programs.

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Index Terms

Bounded expectations: resource analysis for probabilistic programs
1. Social and professional topics
  1. Professional topics
    1. History of computing
      1. History of programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages

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PLDI 2018: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2018

825 pages

ISBN:9781450356985

DOI:10.1145/3192366

General Chair:
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University of Maryland at College Park, USA
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PLDI '18
April 2018
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ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/3296979
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