Mixing Static and Dynamic Partitioning to Parallelize a Constraint Programming Solver

Menouer, Tarek; Rezgui, Mohamed; Cun, Bertrand Le; Régin, Jean‐Charles

doi:10.1007/s10766-015-0356-7

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Results are reported on a wide range of enumeration and optimisation problems, showing that the technique beats a multi-armed bandit portfolio. Menouer et al (2016) show that starting with a static decomposition, and then switching to dynamic work-stealing, yield better results than either technique on its own when parallelising the OR-Tools solver. Results using two twelve core machines are mixed, with speedups ranging from seven to ten.…”

Section: Heuristic-ignorant Decompositionsmentioning

confidence: 98%

A review of literature on parallel constraint solving

Gent¹,

Miguel²,

Nightingale³

et al. 2018

Theory and Practice of Logic Programming

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As multi-core computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multi-core computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation.

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Section: Heuristic-ignorant Decompositionsmentioning

confidence: 98%

A review of literature on parallel constraint solving

Gent¹,

Miguel²,

Nightingale³

et al. 2018

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

show abstract

“…share common paradigms, they are typically individually parallelised and there is little code reuse. One approach to minimise development effort is to parallelise existing sequential solvers [23]. Alternatively high level frameworks provide developers generic libraries to compose searches [1,28,8].…”

Section: The Challenges Of Exact Combinatorial Search At Hpc Scalementioning

confidence: 99%

Generic Exact Combinatorial Search at HPC Scale

MacGregor

Archibald

Trinder

2022

Preprint

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Exact combinatorial search is essential to a wide range of important applications, and there are many large problems that need to be solved quickly. Searches are extremely challenging to parallelise due to a combination of factors,e.g. searches are non-deterministic, dynamic pruning changes the workload, and search tasks have very different runtimes. YewPar is a C++/HPX framework that generalises parallel search by providing a range of sophisticated search skeletons. This paper demonstrates generic high performance combinatorial search, i.e. that a variety of exact combinatorial searches can be easily parallelised for HPC using YewPar. We present a new mechanism for profiling key aspects of YewPar parallel combinatorial search, and demonstrate its value. We exhibit, for the first time, generic exact combinatorial searches at HPC scale. We baseline YewPar against state-of-the-art sequential C++ and C++/OpenMP implementations. We demonstrate that deploying YewPar on an HPC system can dramatically reduce the runtime of large problems, e.g. from days to just 100 seconds. The maximum relative speedups we achieve for an enumeration search are near-linear up to 195(6825) compute-nodes(workers), super-linear for an optimisation search on up to 128(4480) (pruning reduces the workload), and sub-linear for decision searches on up to 64(2240) compute-nodes(workers).

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“…Even when parallel searches share common paradigms, they are typically individually parallelised and there is little code reuse. One approach to minimise development effort is to parallelise existing sequential solvers [23]. Alternatively high level frameworks provide developers generic libraries to compose searches [1,8,28].…”

Section: Global Knowledge Exchangementioning

confidence: 99%

Generic Exact Combinatorial Search at HPC Scale

MacGregor

Archibald

Trinder

2022

Int J Parallel Prog

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Exact combinatorial search is essential to a wide range of important applications, and there are many large problems that need to be solved quickly. Searches are extremely challenging to parallelise due to a combination of factors, e.g. searches are non-deterministic, dynamic pruning changes the workload, and search tasks have very different runtimes. YewPar is a C++/HPX framework that generalises parallel search by providing a range of sophisticated search skeletons.This paper demonstrates generic high performance combinatorial search, i.e. that a variety of exact combinatorial searches can be easily parallelised for HPC using YewPar. We present a new mechanism for profiling key aspects of YewPar parallel combinatorial search, and demonstrate its value. We exhibit, for the first time, generic exact combinatorial searches at HPC scale. We baseline YewPar against state-of-the-art sequential C++ and C++/OpenMP implementations. We demonstrate that deploying YewPar on an HPC system can dramatically reduce the runtime of large problems, e.g. from days to just 100s. The maximum relative speedups we achieve for an enumeration search are near-linear up to 195(6825) compute-nodes(workers), super-linear for an optimisation search on up to 128(4480) (pruning reduces the workload), and sub-linear for decision searches on up to 64(2240) compute-nodes(workers).

show abstract

Mixing Static and Dynamic Partitioning to Parallelize a Constraint Programming Solver

Cited by 5 publications

References 31 publications

A review of literature on parallel constraint solving

A review of literature on parallel constraint solving

Generic Exact Combinatorial Search at HPC Scale

Generic Exact Combinatorial Search at HPC Scale

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