Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective optimization on GPU/CPU platforms

Moreno, Juan José Hurtado; Ortega, Gloria; Filatovas, Ernestas; Martínez, José A.

doi:10.1007/s10898-018-0669-3

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…The search strategy to find the front of solutions also influences the efficiency of the algorithm which is shown in the proposed method and its improved versions. Based on the BOS algorithm, combined with the development of high performance computing systems, paper [23] proposes two efficient parallel BOS algorithms, which are based on high performance processing units, one based on multicore processors (MCs), called MC-BOS and the other one based on GPUs, called GPU-BOS. MC-BOS runs on multicore processors, while GPU-BOS utilizes the GPU architecture to implement parallelism.…”

Section: Related Workmentioning

confidence: 99%

SETNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems

2020

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Non‐dominated sorting, used to find pareto solutions or assign solutions to differentfronts, is a key but time‐consuming process in multi‐objective evolutionary algorithms (MOEAs).The best‐case and worst‐case time complexity of non‐dominated sorting algorithms currentlyknown are O(MNlogN) and O(MN2); M and N represent the number of objectives and the populationsize, respectively. In this paper, a more efficient SET‐based non‐dominated sorting algorithm,shorted to SETNDS, is proposed. The proposed algorithm can greatly reduce the number ofcomparisons on the promise of ensuring a shorter running time. In SETNDS, the rank of a solutionto be sorted is determined by only comparing with the one with the highest rank degree in itsdominant set. This algorithm is compared with six generally existing non‐dominated sortingalgorithms—fast non‐dominated sorting, the arena’s principle sort, the deductive sort, the cornersort, the efficient non‐dominated sort and the best order sort on several kinds of datasets. Thecompared results show that the proposed algorithm is feasible and effective and its computationalefficiency outperforms other existing algorithms.

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Section: Related Workmentioning

confidence: 99%

SETNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems

2020

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“…The data processing time is too long, which limits the application in practice. 3 Therefore, efficient and fast processing of large data radix sorting, and constantly improving the performance of radix sorting on different computing platforms and environments have become an urgent problem to be solved in the field of application computing. 4 The Compute Unified Device Architecture (CUDA) uses the parallel computing engine of the NVIDIA Graphic Processing Unit (GPU) to achieve a more efficient computing solution than the CPU for solving many complex computing tasks.…”

Section: Introductionmentioning

confidence: 99%

“…However, the time overhead of radix sorting increases rapidly with the increase in data size and key length. The data processing time is too long, which limits the application in practice 3 . Therefore, efficient and fast processing of large data radix sorting, and constantly improving the performance of radix sorting on different computing platforms and environments have become an urgent problem to be solved in the field of application computing 4 …”

Section: Introductionmentioning

confidence: 99%

A radix sorting parallel algorithm suitable for graphic processing unit computing

Xiao

Guo

et al. 2020

Concurrency and Computation

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Radix sorting is an essential basic data processing operation in many computer fields. It has important practical significance to accelerate its performance through Graphic Processing Unit (GPU). The heterogeneous parallel computing technology attracts much attention and is widely applied for its effective computation efficiency and parallel real-time data processing capability. Taking advantage of the parallelism of GPU in numerical computation processing, a parallelization design method of the Binary_Least Significant Digit (LSD) first Radix Sorting (B_LSD_RS) algorithm based on Open Computing Language (OpenCL) is proposed. The radix sorting algorithm is divided into multiple kernel tasks, and the kernels are sequentially controlled by the event information transfer. The parallel algorithm is implemented and verified on the GPU + CPU heterogeneous platform. The experimental results show that compared with the performance of the B_LSD_RS sequential algorithm based on AMD Ryzen5 1600X CPU, B_LSD_RS parallel algorithm based on Open Multi-Processing (OpenMP) and B_LSD_RS parallel algorithm based on Compute Unified Device Architecture (CUDA), the B_LSD_RS parallel algorithm based on OpenCL obtained 28.86 times, 11.01 times and 2.14 times speedup in the NVIDIA GTX 1070 computing platform respectively, not only achieves high performance but also achieves performance portability among different GPU computing platforms.

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“…Nowadays, several researchers have focused on the parallel implementation of evolutionary algorithms (Van Veldhuizen et al 2003), as different operations of the evolutionary algorithms are independent from each other. Many of the approaches have been recently proposed which focus on the parallelization of non-dominated sorting (Smutnicki et al 2014;Gupta and Tan 2015;Ortega et al 2017;Moreno et al 2018;Mishra and Coello Coello 2018). Some of these approaches (Smutnicki et al 2014;Gupta and Tan 2015;Ortega et al 2017) focus on Fast Non-dominated Sorting (Deb et al 2002) for exploring the parallelism.…”

Section: Introductionmentioning

confidence: 99%

Parallelism in divide-and-conquer non-dominated sorting: a theoretical study considering the PRAM-CREW model

Mishra

Coello

2019

J Heuristics

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Non-dominated sorting is a crucial component of Pareto-based multi-and manyobjective evolutionary algorithms. As the number of objectives increases, the execution time of a multi-objective evolutionary algorithm increases, too. Since multi-objective evolutionary algorithms normally have a low data dependency, research-ers have increasingly adopted parallel programming techniques to reduce their execution time. Evidently, it is also desirable to parallelize non-dominated sorting. There are some recent proposals which focus on the parallelization of non-dominated sorting, with a particular emphasis on a very well-known approach called fast non-dominated sorting. In this paper, however, we explore the scope of parallelism in an approach called divideand-conquer based non-dominated sorting (DCNS), which we recently introduced. This paper explores the parallelism from a theoretical point of view. The parallelization of the DCNS approach has been explored considering the PRAM-CREW (Parallel Random Access Machine, Concurrent Read Exclusive Write) model. The analysis of parallel algorithms is usually carried out under the assumption that an unbounded number of processors are available. So, in our analysis, we have also considered the same assumption. The time and space complexities of the parallel version of the DCNS approach is obtained in different scenarios. The time complexity of the parallel version of the DCNS approach in different scenarios is proved to be O(log M + N ). We have also obtained the maximum number of processors which can be required by the parallel version of the DCNS approach. The comparison of the parallel version of the DCNS approach with respect to some other approaches is also performed.

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Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective optimization on GPU/CPU platforms

Cited by 10 publications

References 23 publications

SETNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems

SETNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems

A radix sorting parallel algorithm suitable for graphic processing unit computing

Parallelism in divide-and-conquer non-dominated sorting: a theoretical study considering the PRAM-CREW model

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