Solving the flow shop problem by parallel programming

Boejko, Wojciech

doi:10.1016/j.jpdc.2009.01.009

Cited by 26 publications

(14 citation statements)

References 11 publications

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“…Bozejko presents the use of HPC through massive parallelism to solve the job-shop problem through metaheuristics [13]. The results show that the parallel algorithms exhibit a linear speedup with the number of processors (although some considerations are made).…”

Section: Hpc Applications For Industry40: Lite-rature Reviewmentioning

confidence: 99%

Towards a high performance computing scalable implementation of Cyber Physical Systems

Lopes¹,

Putnik²,

Ferreira³

et al. 2019

FME Transactions

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Physical Systems (CPS) establish an interdependency between the physical world and the cyber world. When considering a real world setup, CPS will receive a considerable input from the physical world that demands a timely response. Without the computer capacity to handle the input data, a CPS will not be able to react in a very short term to changes in the environment and provide proper output. Current CPS are unable to react in near real-time to challenges when considering a realistic problem size. Our objective is to harness the performance of High Performance Computing systems to make CPS capable of handling the necessary computation demands in order to be able to interact with the physical world. We present a case study on scheduling algorithms to demonstrate the current limitations in the computing performance and point possible solutions to achieve it.

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Section: Hpc Applications For Industry40: Lite-rature Reviewmentioning

confidence: 99%

Towards a high performance computing scalable implementation of Cyber Physical Systems

Lopes¹,

Putnik²,

Ferreira³

et al. 2019

FME Transactions

View full text Add to dashboard Cite

show abstract

“…Based on the results of Janiak et al (2008), who showed that 90% of the processing time is spent in the evaluation function, Czapiński and Barnes (2011) designed an improved parallelization of TS for the PFSP. They use a limited parallelization of evaluations as proposed by Bożejko (2009). They propose to reorder the way a solution is coded in the GPU to ensure a coalesced memory access.…”

Section: Tabu Searchmentioning

confidence: 99%

GPU computing applied to linear and mixed-integer programming

Boyer

Baz

Salazar-Aguilar

2017

Advances in GPU Research and Practice

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Thanks to CUDA and OpenCL, Graphics Processing Units (GPUs) have recently gained considerable attention in science and engineering as accelerators for High Performance Computing (HPC). In this chapter, we show how the Operations Research (OR) community can take great benefit of GPUs. In particular, we present a survey of the main contributions to the field of GPU computing applied to linear and mixed-integer programming. The OR field is rich in complex problems and sophisticated algorithms that can take advantage of parallelization. However, all algorithms in the literature do not fit to the SIMT paradigm. Therefore, we highlight the main issues tackled by different authors to overcome the difficulties of implementation and the results obtained with their optimization algorithms via GPU computing.

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“…Among published parallel algorithms, based on classic metaheuristics, are e.g. Parallel Genetic Algorithm by Bożejko and Wodecki [2], Parallel Scatter Search by Bożejko and Wodecki [3] and by Bożejko [1], and Parallel Simulated Annealing with Genetic Enhancement by Czapiński [5].…”

Section: Gpu Implementation Of Tabu Search For Travelling Salesman Prmentioning

confidence: 99%

“…Furthermore, Bożejko [1] provides very detailed analysis of theoretical and empirical speed-ups obtained by parallelisation of PFSP evaluation.…”

Section: Gpu Implementation Of Tabu Search For Travelling Salesman Prmentioning

confidence: 99%

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

Czapiński

Barnes

2011

Journal of Parallel and Distributed Computing

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The introduction of NVidia's powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.

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Solving the flow shop problem by parallel programming

Cited by 26 publications

References 11 publications

Towards a high performance computing scalable implementation of Cyber Physical Systems

Towards a high performance computing scalable implementation of Cyber Physical Systems

GPU computing applied to linear and mixed-integer programming

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

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