A Parallelization Mixing OR-Tools/Gecode Solvers on Top of the Bobpp Framework

Menouer, Tarek; Cun, Bertrand Le

doi:10.1109/3pgcic.2013.42

Cited by 3 publications

(2 citation statements)

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“…The framework has been used to solve Quadratic Assignment Problems [12], Constrained Programming [22][23][24], and biochemical simulation [2]. The possible parallelizations are based on Pthreads, MPI or Athapascan/Kaapi libraries.…”

Section: Bobpp Frameworkmentioning

confidence: 99%

“…To investigate this question, we implemented the same B&B algorithm on three libraries with a varying level of abstraction: (1) Bobpp is a high-level library that greatly simplifies the programming effort; (2) TBB can be regarded as a medium-level abstraction in which the programmer manually has to deal with the task coding, but task execution and scheduling is automatically carried out by the runtime; and (3) Pthread is the lowest-level counterpart in which the user has to tackle both the task and scheduling coding. Bobpp has been chosen for this study because this framework has been recently used by researchers to solve optimization problems [2,[22][23][24]. It is a representative of specifically dedicated frameworks for parallel B&B of high abstraction level.…”

Section: Introductionmentioning

confidence: 99%

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On parallel Branch and Bound frameworks for Global Optimization

et al. 2017

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Branch and Bound (B&B) algorithms are known to exhibit an irregularity of the search tree. Therefore, developing a parallel approach for this kind of algorithms is a challenge. The efficiency of a B&B algorithm depends on the chosen Branching, Bounding, Selection, Rejection, and Termination rules. The question we investigate is how the chosen platform consisting of programming language, used libraries, or skeletons influences programming effort and algorithm performance. Selection rule and data management structures are usually hidden to programmers for frameworks with a high level of abstraction, as well as the load balancing strategy, when the algorithm is run in parallel. We investigate the question by implementing a multidimensional Global Optimization B&B algorithm with the help of three frameworks with a different level of abstraction (from more to less): Bobpp, Threading Building Blocks (TBB), and a customized Pthread implementation. The following has been found. The Bobpp implementation is easy to code, but exhibits the poorest scalability. On the contrast, the TBB and Pthread implementations scale almost linearly on the used platform. The TBB approach shows a slightly better productivity.

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