Solving very large instances of the scheduling of independent tasks problem on the GPU

Pinel, Frédéric; Dorronsoro, Bernabè; Bouvry, Pascal

doi:10.1016/j.jpdc.2012.02.018

Cited by 49 publications

(31 citation statements)

References 34 publications

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“…The performance of MinMin can be improved by using a local search algorithm [33]. To improve the run time execution MinMin can be easily parallelized.…”

Section: F(x) F(x) Bestmentioning

confidence: 99%

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Algorithms solving the Internet shopping optimization problem with price discounts

Musiał¹,

Pecero²,

López-Locés³

et al. 2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The Internet shopping optimization problem arises when a customer aims to purchase a list of goods from a set of web-stores with a minimum total cost. This problem is NP-hard in the strong sense. We are interested in solving the Internet shopping optimization problem with additional delivery costs associated to the web-stores where the goods are bought. It is of interest to extend the model including price discounts of goods. The aim of this paper is to present a set of optimization algorithms to solve the problem. Our purpose is to find a compromise solution between computational time and results close to the optimum value. The performance of the set of algorithms is evaluated through simulations using real world data collected from 32 web-stores. The quality of the results provided by the set of algorithms is compared to the optimal solutions for small-size instances of the problem. The optimization algorithms are also evaluated regarding scalability when the size of the instances increases. The set of results revealed that the algorithms are able to compute good quality solutions close to the optimum in a reasonable time with very good scalability demonstrating their practicability.

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“…The performance of MinMin can be improved by using a local search algorithm [33]. To improve the run time execution MinMin can be easily parallelized.…”

Section: F(x) F(x) Bestmentioning

confidence: 99%

“…To improve the run time execution MinMin can be easily parallelized. There are different Algorithms solving the Internet shopping optimization problem with price discounts parallel implementations of the min-min scheduling algorithm reported in literature [33,34], both CPU and GPU parallel implementations.…”

Section: F(x) F(x) Bestmentioning

confidence: 99%

Algorithms solving the Internet shopping optimization problem with price discounts

Musiał¹,

Pecero²,

López-Locés³

et al. 2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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show abstract

“…Pinel et al have considered the parallel solution of scheduling of independant tasks problems in [38]. They have also proposed implementations on GPU of the Min-Min heuristic and GraphCell a parallel cellular genetic algorithm (two new parallel recombination operators are also proposed in the paper).…”

Section: B Scheduling Problems 1) Branch and Boundmentioning

confidence: 99%

Recent Advances on GPU Computing in Operations Research

Boyer

Baz

2013

2013 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum

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Abstract-In the last decade, Graphics Processing Units (GPUs) have gained an increasing popularity as accelerators for High Performance Computing (HPC) applications. Recent GPUs are not only powerful graphics engines but also highly threaded parallel computing processors that can achieve sustainable speedup as compared with CPUs. In this context, researchers try to exploit the capability of this architecture to solve difficult problems in many domains in science and engineering. In this article, we present recent advances on GPU Computing in Operations Research. We focus in particular on Integer Programming and Linear Programming.

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“…It can be manually parallelized with some effort, and for a specific architecture only (i.e. for the GPU [24]). …”

Section: B the Min-min Heuristic For The Independent Task Mapping Prmentioning

confidence: 99%

Savant: Automatic parallelization of a scheduling heuristic with machine learning

Pinel

Bouvry

Dorronsoro

et al. 2013

2013 World Congress on Nature and Biologically Inspired Computing

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Abstract-This paper investigates the automatic parallelization of a heuristic for an NP-complete problem, with machine learning. The objective is to automatically design a new concurrent algorithm that finds solutions of comparable quality to the original heuristic. Our approach, called Savant, is inspired from the Savant syndrome. Its concurrency model is based on mapreduce. The approach is evaluated with the well-known Min-Min heuristic. Simulation results on two problem sizes are promising, the produced algorithm is able to find solutions of comparable quality.

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Solving very large instances of the scheduling of independent tasks problem on the GPU

Cited by 49 publications

References 34 publications

Algorithms solving the Internet shopping optimization problem with price discounts

Algorithms solving the Internet shopping optimization problem with price discounts

Recent Advances on GPU Computing in Operations Research

Savant: Automatic parallelization of a scheduling heuristic with machine learning

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