A review of literature on parallel constraint solving

Gent, Ian P.; Miguel, Ian; Nightingale, Peter; McCreesh, Ciaran; Prosser, Patrick; Moore, Neil C. A.; Unsworth, Chris

doi:10.1017/s1471068418000340

“…We refer the interested reader to [28], for a nice review of the literature on parallel approaches to solving CSPs. In particular, the importance of enforcing consistency (at different levels) in CSP solvers is pointed out.…”

Section: Parallel Algorithmsmentioning

confidence: 99%

Tree projections and constraint optimization problems: Fixed-parameter tractability and parallel algorithms

Gottlob

¹

,

Greco

²

,

Scarcello

³

2018

Journal of Computer and System Sciences

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Tree projections provide a unifying framework to deal with most structural decomposition methods of constraint satisfaction problems (CSPs). Within this framework, a CSP instance is decomposed into a number of sub-problems, called views, whose solutions are either already available or can be computed efficiently. The goal is to arrange portions of these views in a tree-like structure, called tree projection, which determines an efficiently solvable CSP instance equivalent to the original one. However, deciding whether a tree projection exists is NP-hard. Solution methods have therefore been proposed in the literature that do not require a tree projection to be given, and that either correctly decide whether the given CSP instance is satisfiable, or return that a tree projection actually does not exist. These approaches had not been generalized so far to deal with CSP extensions tailored for optimization problems, where the goal is to compute a solution of maximum value/minimum cost. The paper fills the gap, by exhibiting a fixed-parameter polynomial-time algorithm that either disproves the existence of tree projections or computes an optimal solution, with the parameter being the size of the expression of the objective function to be optimized over all possible solutions (and not the size of the whole constraint formula, used in related works). Tractability results are also established for the problem of returning the best K solutions. Finally, parallel algorithms for such optimization problems are proposed and analyzed.Given that the classes of acyclic hypergraphs, hypergraphs of bounded treewidth, and hypergraphs of bounded generalized hypertree width are all covered as special cases of the tree projection framework, the results in this paper directly apply to these classes. These classes are extensively considered in the CSP setting, as well as in conjunctive database query evaluation and optimization.

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“…Parallel Constraint Solving Type systems are a particular flavor of constraint problem. Indeed, if we view parallel type checking as a parallel constraint satisfaction problem, we can look for guidance from previous work on parallel logic programming [10,17] and parallel constraint solvers [15,31,39]. Unfortunately, the datastructures and synchronization strategies employed in these works are extremely specialized to the constraint system being solved: for example, SAT solvers have developed a large body of specialized data structures and parallelization strategies [18,19].…”

Section: Or-parallelism With Stream Algebrasmentioning

confidence: 99%

Parallel type-checking with haskell using saturating LVars and stream generators

Newton

¹

,

Ağacan

²

,

Fogg³

et al. 2016

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Given the sophistication of recent type systems, unification-based type-checking and inference can be a time-consuming phase of compilation-especially when union types are combined with subtyping. It is natural to consider improving performance through parallelism, but these algorithms are challenging to parallelize due to complicated control structure and difficulties representing data in a way that is both efficient and supports concurrency. We provide techniques that address these problems based on the LVish approach to deterministic-by-default parallel programming. We extend LVish with Saturating LVars, the first LVars implemented to release memory during the object's lifetime. Our design allows us to achieve a parallel speedup on worst-case (exponential) inputs of Hindley-Milner inference, and on the Typed Racket type-checking algorithm, which yields up an 8.46× parallel speedup on 14 cores for type-checking examples drawn from the Racket repository.

show abstract

“…Parallel Constraint Solving Type systems are a particular flavor of constraint problem. Indeed, if we view parallel type checking as a parallel constraint satisfaction problem, we can look for guidance from previous work on parallel logic programming [10,17] and parallel constraint solvers [15,31,39]. Unfortunately, the datastructures and synchronization strategies employed in these works are extremely specialized to the constraint system being solved: for example, SAT solvers have developed a large body of specialized data structures and parallelization strategies [18,19].…”

Section: Or-parallelism With Stream Algebrasmentioning

confidence: 99%

Parallel type-checking with haskell using saturating LVars and stream generators

Newton

¹

,

Ağacan

²

,

Fogg³

et al. 2016

Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

2

0

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Given the sophistication of recent type systems, unification-based type-checking and inference can be a time-consuming phase of compilation-especially when union types are combined with subtyping. It is natural to consider improving performance through parallelism, but these algorithms are challenging to parallelize due to complicated control structure and difficulties representing data in a way that is both efficient and supports concurrency. We provide techniques that address these problems based on the LVish approach to deterministic-by-default parallel programming. We extend LVish with Saturating LVars, the first LVars implemented to release memory during the object's lifetime. Our design allows us to achieve a parallel speedup on worst-case (exponential) inputs of Hindley-Milner inference, and on the Typed Racket type-checking algorithm, which yields up an 8.46× parallel speedup on 14 cores for type-checking examples drawn from the Racket repository.

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A review of literature on parallel constraint solving

Cited by 21 publications

References 126 publications

Tree projections and constraint optimization problems: Fixed-parameter tractability and parallel algorithms

Tree projections and constraint optimization problems: Fixed-parameter tractability and parallel algorithms

Parallel type-checking with haskell using saturating LVars and stream generators

Parallel type-checking with haskell using saturating LVars and stream generators

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