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SMTS: Distributed, Visualized Constraint Solving

9 pagesPublished: October 23, 2018

Abstract

The inherent complexity of parallel computing makes development, resource monitor- ing, and debugging for parallel constraint-solving-based applications difficult. This paper presents SMTS, a framework for parallelizing sequential constraint solving algorithms and running them in distributed computing environments. The design (i) is based on a gen- eral parallelization technique that supports recursively combining algorithm portfolios and divide-and-conquer with the exchange of learned information, (ii) provides monitoring by visually inspecting the parallel execution steps, and (iii) supports interactive guidance of the algorithm through a web interface. We report positive experiences on instantiating the framework for one SMT solver and one IC3 solver, debugging parallel executions, and visualizing solving, structure, and learned clauses of SMT instances.

Keyphrases: Algorithm Portfolios, Distributed IC3, Distributed SMT, Divide and Conquer, web-based GUI

In: Gilles Barthe, Geoff Sutcliffe and Margus Veanes (editors). LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 57, pages 534--542

Links:
BibTeX entry
@inproceedings{LPAR-22:SMTS_Distributed_Visualized_Constraint,
  author    = {Matteo Marescotti and Antti Hyv\textbackslash{}"arinen and Natasha Sharygina},
  title     = {SMTS: Distributed, Visualized Constraint Solving},
  booktitle = {LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning},
  editor    = {Gilles Barthe and Geoff Sutcliffe and Margus Veanes},
  series    = {EPiC Series in Computing},
  volume    = {57},
  pages     = {534--542},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/k7BQ},
  doi       = {10.29007/fhgn}}
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