CUDA Based Enhanced Differential Evolution: A Computational Analysis

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General purpose graphic programming unit (GPGPU) programming is a novel approach for solving parallel variable independent problems. The graphic processor core (GPU) gives the possibility to use multiple blocks, each of which contains hundreds of threads. Each of these threads can be visualized as a core onto itself, and tasks can be simultaneously sent to all the threads for parallel evaluations. This research explores the advantages of applying a evolutionary algorithm (EA) on the GPU in terms of computational speedups. Enhanced Differential Evolution (EDE) is applied to the generic permutative flowshop scheduling (PFSS) problem both using the central processing unit (CPU) and the GPU, and the results in terms of execution time is compared. INTRODUCTION During the later part of the past decade, a novel trend emerged where programmers started using the Graphics Processing Unit (GPU) for programming not graphic applications which usually was in the preview of the Central Processing Unit (CPU). The reasoning behind such a move was the possibility to achieving speedups of magnitude compared to optimized CPU implementations. GPU’s have evolved into fast, highly multi-threaded processors, with hundreds of cores and thousands of concurrent threads. These threads which can be invoked simultaneously, provide an excellent platform for parallel execution. A GPU is optimal when a problem has to be executed many times, can be isolated as a function and works independently on different data. One of the most challenging and computational demanding problems in engineering are the NP-Hard problems. These problems are computationally intractable, and often require the use of optimization algorithms. This research attempts to solve the challenging flowshop scheduling (FSS) problem using a novel Enhanced Differential Evolution (EDE) algorithm utilizing GPU programming. One of the most widespread programming architectures is the Compute Unified Device Architecture (CUDA) of Nvidia (NVIDIA, 2012). A number of research has been conducted on GPU programming involving evolutionary algorithms and these two architectures. Tabu Search has been used for the evaluating the FSS problem using CUDA by Czapinski and Barnes (2011). Genetic Algorithms (GA) has been been used to solve the traveling salesman problem by Chen et al. (2011), whereas a parallel GA approach has been done by Pospichal et al. (2010). The particle swarm algorithm has also been modified to be used by CUDA Mussi et al. (2011). More interestingly Genetic Programming has also found a niche in GPU programming (Robilliard et al., 2009). This research utilizes the Nvidia CUDA framework for GPU computation. The enhanced Differential Evolution (EDE) (Davendra and Onwubolu, 2009) is modified to the GPU framework and execution time for both the GPU and CPU variants are compared. This paper follows the following structure. Section 1 outlines the CUDA framework and syntax. Section 2 describes Differential Evolution (DE) and the EDE algorithms. The problem attempted in this research; flow shop scheduling is given in Section 3. Section 4 describes the code design on the GPU, whereas the experimentation and analysis (Section 5) compares the obtained results. The paper is concluded in Section 6. Proceedings 26th European Conference on Modelling and Simulation ©ECMS Klaus G. Troitzsch, Michael Mohring, Ulf Lotzmann (Editors) ISBN: 978-0-9564944-4-3 / ISBN: 978-0-9564944-5-0 (CD)