Productivity-Aware Design and Implementation of Distributed Tree-Based Search Algorithms

Carneiro, Tiago; Melab, Nouredine

doi:10.1007/978-3-030-22734-0_19

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…A first detail that is it worth to mention concerns the parameters found by the heuristic for the N-Queens problem. The previous experience shows us that the dynamic distributed load balancing scheme presents the best performance for both the N-Queens and the FSP problems [4], [9]. In turn, on the Aion cluster, which is AMD-based, ChapelBB shows the best performance enumerating the solutions of the N-Queens using guided as the load balancing scheme.…”

Section: Resultsmentioning

confidence: 97%

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A Local Search for Automatic Parameterization of Distributed Tree Search Algorithms

Carneiro

Koutsantonis

Melab

et al. 2022

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Tree-based search algorithms applied to combinatorial optimization problems are highly irregular and timeconsuming when solving instances of NP-Hard problems. Due to their parallel nature, algorithms for this class of complexity have been revisited for different architectures over the years. However, parallelization efforts have always been guided by the performance objective setting aside productivity. Using Chapel's high productivity for the design and implementation of distributed tree search algorithms keeps the programmer from lower-level details, such as communication and load balancing. However, the parameterization of such parallel applications is complex, consisting of several parameters, even if a high-productivity language is used in their conception. This work presents a local searchbased heuristic for automatic parameterization of ChapelBB, a distributed tree search application for solving combinatorial optimization problems written in Chapel. The main objective of the proposed heuristic is to overcome the limitation of manual parameterization, which covers a limited feasible space. The reported results show that the heuristic-based parameterization increases up to 30% the performance of ChapelBB on 2048 cores (4096 threads) solving the N-Queens problem and up to 31% solving instances of the Flow-shop scheduling problem to the optimality.

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Section: Resultsmentioning

confidence: 97%

“…Finally, both intra-and interlocale levels of parallelism are exploited by using Chapel parallel iterators. For more details about ChapelBB, the interested reader may refer to [4], [9].…”

Section: B the Master Locale And The Initial Searchmentioning

confidence: 99%

A Local Search for Automatic Parameterization of Distributed Tree Search Algorithms

Carneiro

Koutsantonis

Melab

et al. 2022

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

Self Cite

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“…The Chapel codes written for this paper can actually run on distributed systems by performing straightforward modifications [26,25]), which represents a potentially significant gain in productivity. Chapel is currently used by a portion of the HPC community, which is more familiar to lowerlevel languages.…”

Section: Discussionmentioning

confidence: 99%

A comparative study of high-productivity high-performance programming languages for parallel metaheuristics

Gmys

Carneiro

Melab

et al. 2020

Swarm and Evolutionary Computation

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Parallel metaheuristics require programming languages that provide both, high performance and a high level of programmability. This paper aims at providing a useful data point to help practitioners gauge the difficult question of whether to invest time and effort into learning and using a new programming language. To accomplish this objective, three productivity-aware languages (Chapel, Julia, and Python) are compared in terms of performance, scalability and productivity. To the best of our knowledge, this is the first time such a comparison is performed in the context of parallel metaheuristics. As a test-case, we implement two parallel metaheuristics in three languages for solving the 3D Quadratic Assignment Problem (Q3AP), using thread-based parallelism on a multi-core shared-memory computer. We also evaluate and compare the performance of the three languages for a parallel fitness evaluation loop, using four different test-functions with different computational characteristics. Besides providing a comparative study, we give feedback on the implementation and parallelization process in each language.

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Productivity-Aware Design and Implementation of Distributed Tree-Based Search Algorithms

Cited by 2 publications

References 12 publications

A Local Search for Automatic Parameterization of Distributed Tree Search Algorithms

A Local Search for Automatic Parameterization of Distributed Tree Search Algorithms

A comparative study of high-productivity high-performance programming languages for parallel metaheuristics

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