Recent Advances on GPU Computing in Operations Research

Boyer, Vincent; Baz, Didier El

doi:10.1109/ipdpsw.2013.45

Cited by 25 publications

(14 citation statements)

References 34 publications

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“…It turns out that this hierarchy is highly consistent with the hierarchy of threads and different types of memory of CUDA framework. Table 3 Correspondence between the parallel hybrid GA components and the hierarchy of According to the problem description in Section 3, a target machine matrix X(k) stored on GPU global memory with n + n′ rows and g columns, is presented in (10).  if job j at stage s is assigned to a machine after the start time of the rescheduling point, element x js (k) is equal to a random integer representing the target machine handling job j at stage s. Similarly, elements y js (k) is also generated randomly from the range starting from 1 to the amount of unassigned operations.…”

Section: Fig3 Hierarchy Of Threads and Different Types Of Memory Ofmentioning

confidence: 99%

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

Luo

Fujimura

Baz

et al. 2019

Journal of Parallel and Distributed Computing

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Due to new government legislation, customers' environmental concerns and continuously rising cost of energy, energy efficiency is becoming an essential parameter of industrial manufacturing processes in recent years. Most efforts considering energy issues in scheduling problems have focused on static scheduling. But in fact, scheduling problems are dynamic in the real world with uncertain new arrival jobs after the execution time. This paper proposes a dynamic energy efficient flexible flow shop scheduling model using peak power value with the consideration of new arrival jobs. As the problem is strongly NP-hard, a priority based hybrid parallelGenetic Algorithm with a predictive reactive complete rescheduling approach is developed. In order to achieve a speedup to meet the short response in the dynamic environment, the proposed method is designed to be highly consistent with NVIDIA CUDA software model. Finally, numerical experiments are conducted and show that our approach can not only achieve better performance than the traditional static approach, but also gain competitive results by reducing the time requirements dramatically.

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Section: Fig3 Hierarchy Of Threads and Different Types Of Memory Ofmentioning

confidence: 99%

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

Luo

Fujimura

Baz

et al. 2019

Journal of Parallel and Distributed Computing

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“…The experimental results showed great speedups for the exterior point algorithm and quite worse for the revised simplex method. Other valuable attempts can also be found in [36][37][38] achieving very satisfactory speedups with C1060, S1070 and GTX 670 boards.…”

Section: Related Workmentioning

confidence: 99%

Simplex Parallelization in a Fully Hybrid Hardware Platform

Mamalis¹,

Perlitis²

2017

ijacsa

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Abstract-The simplex method has been successfully used in solving linear programming (LP) problems for many years. Parallel approaches have also extensively been studied due to the intensive computations required, especially for the solution of large LP problems. Furthermore, the rapid proliferation of multicore CPU architectures as well as the computational power provided by the massive parallelism of modern GPUs have turned CPU / GPU collaboration models increasingly into focus over the last years for better performance. In this paper, a highly scalable implementation framework of the standard full tableau simplex method is first presented, over a hybrid parallel platform which consists of multiple multicore nodes interconnected via a high-speed communication network. The proposed approach is based on the combined use of MPI and OpenMP, adopting a suitable column-based distribution scheme for the simplex tableau. The parallelization framework is then extended in such a way that it can exploit concurrently the full power of the provided resources on a multicore single-node environment with a CUDA-enabled GPU (i.e. using the CPU cores and the GPU concurrently), based on a suitable hybrid multithreading/GPU offloading scheme with OpenMP and CUDA. The corresponding experimental results show that the hybrid MPI+OpenMP based parallelization scheme leads to particularly high speed-up and efficiency values, considerably better than in other competitive approaches, and scaling well even for very large / huge linear problems. Furthermore, the performance of the hybrid multithreading/GPU offloading scheme is clearly superior to both the OpenMP-only and the GPU-only based implementations in almost all cases, which validates the worth of using both resources concurrently. The most important, when it is used in combination with MPI in a multi-node (fully hybrid) environment, it leads to substantial improvements in the speedup achieved for large and very large LP problems.

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“…Researchers and practitioners who develop and use metaheuristics always benefit from low cost computing power [26] GPU's are powerful accelerators, require less energy than other computing devices, are widely available and are relatively cheap. These user needs and GPU characteristics and the fact that GPU computing has been identified as a very promising direction in the field of Operations Research [4], motivated us to solve the QAP using the GPU. In our work, the intensive computational tasks in the tabu search algorithm are handled by the GPU leaving the CPU just with the tasks of reading and organizing input values and collecting output values.…”

Section: Gpu Computingmentioning

confidence: 99%

“…The use of parallel computing to solve QAP's started with the development of CPU parallel implementations such as the ones in [23,8,14]. In the last years, there has been also a shift to finding solutions using GPUs [26,4,12]. We chose to parallelize the tabu search metaheuristic because, it has been reported as the most efficient approximate method for solving the QAP [22,10,24,23].…”

Section: Introductionmentioning

confidence: 99%

ASIMD tabu searchimplementation for solving the quadratic assignment problem with GPU acceleration

Novoa¹,

Qasem²,

Chaparala³

2015

Proceedings of the 2015 XSEDE Conference on Scientific Advancements Enabled by Enhanced Cyberinfrastructure - XSEDE '15

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In the Quadratic Assignment Problem (QAP), n units (usually departments , machines, or electronic components) must be assigned to n locations given the distance between the locations and the flow between the units. The goal is to find the assignment that minimizes the sum of the products of distance traveled and flow between units. The QAP is a combinatorial problem difficult to solve to optimality even for problems where n is relatively small (e.g., n = 30). In this paper, we develop a parallel tabu search algorithm to solve the QAP and leverage the compute capabilities of current GPUs. The single instruction multiple data (SIMD) algorithm is implemented on the Stampede cluster hosted by the Texas Advanced Computing Center (TACC) at the University of Texas at Austin. We enhance our implementation by exploiting the dynamic parallelism made available in the Nvidia Kepler high performance computing architecture. On a series of experiments on the well-known QAPLIB data sets, our algorithm produces solutions that are as good as the best known ones posted in QAPLIB. The worst case percentage of accuracy we obtained was 0.83%. Given the applicability of QAP, our algorithm has very good potential to accelerate scholarly research in Engineering, in the fields of Operations Research and design of electronic devices. To the best of our knowledge, this work is the first to successfully parallelize the tabu search metaheuristic to solve the QAP with the recency-based feature, implemented serially in [10]. Our work is also the first to exploit GPU dynamic parallelism in a tabu search metaheuristic to solve the QAP.

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Recent Advances on GPU Computing in Operations Research

Cited by 25 publications

References 34 publications

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

Simplex Parallelization in a Fully Hybrid Hardware Platform

ASIMD tabu searchimplementation for solving the quadratic assignment problem with GPU acceleration

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