Estimating parallel runtimes for randomized algorithms in constraint solving

This paper presents a detailed analysis of the scalability and parallelization of Local Search algorithms for constraint-based and SAT (Boolean satisfiability) solvers. We propose a framework to estimate the parallel performance of a given algorithm by analyzing the runtime behavior of its sequentia...

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Vydáno v:Journal of heuristics Ročník 22; číslo 4; s. 613 - 648
Hlavní autoři: Truchet, Charlotte, Arbelaez, Alejandro, Richoux, Florian, Codognet, Philippe
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.08.2016
Springer Nature B.V
Springer Verlag
Témata:
Algorithms
Artificial Intelligence
Boolean
Calculus of Variations and Optimal Control; Optimization
Combinatorics
Computer Science
Distributed, Parallel, and Cluster Computing
Estimating techniques
Management Science
Mathematical analysis
Mathematics
Mathematics and Statistics
Methods
Operations Research
Operations Research/Decision Theory
Parallel processing
Performance evaluation
Propagation
Random variables
Studies
Parallel processing
Performance model
Randomized constraint solving
Constraint solving
ISSN:1381-1231, 1572-9397
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Shrnutí:This paper presents a detailed analysis of the scalability and parallelization of Local Search algorithms for constraint-based and SAT (Boolean satisfiability) solvers. We propose a framework to estimate the parallel performance of a given algorithm by analyzing the runtime behavior of its sequential version. Indeed, by approximating the runtime distribution of the sequential process with statistical methods, the runtime behavior of the parallel process can be predicted by a model based on order statistics. We apply this approach to study the parallel performance of a constraint-based Local Search solver (Adaptive Search), two SAT Local Search solvers (namely Sparrow and CCASAT), and a propagation-based constraint solver (Gecode, with a random labeling heuristic). We compare the performance predicted by our model to actual parallel implementations of those methods using up to 384 processes. We show that the model is accurate and predicts performance close to the empirical data. Moreover, as we study different types of problems, we observe that the experimented solvers exhibit different behaviors and that their runtime distributions can be approximated by two types of distributions: exponential (shifted and non-shifted) and lognormal. Our results show that the proposed framework estimates the runtime of the parallel algorithm with an average discrepancy of 21 % w.r.t. the empirical data across all the experiments with the maximum allowed number of processors for each technique.
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ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-015-9292-3