EASEA: specification and execution of evolutionary algorithms on GPGPU

Maitre, Ogier; Krüger, Frédéric; Querry, Stephane; Lachiche, Nicolas; Collet, Pierre

doi:10.1007/s00500-011-0718-z

Cited by 33 publications

(10 citation statements)

References 29 publications

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“…Thus, several researchers have presented ideas to modify existing optimization algorithms running on CPUs for the new GPU architecture: genetic algorithms (Cavuoti et al 2013), cellular genetic algorithms (Vidal and Alba 2010), particle swarm optimization (Rabinovich et al 2012) and others (Langdon 2010;Maitre et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Solving optimization problems using a hybrid systolic search on GPU plus CPU

2016

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In recent years, graphics processing units (GPUs) have emerged as a powerful architecture for solving a broad spectrum of applications in very short periods of time. However, most existing GPU optimization approaches do not exploit the full power available in a CPU-GPU platform. They have a tendency to leave one of them partially unused (usually the CPU) and fail to establish an accurate exchange of information that could help solve the target problem efficiently. Thus, better performance is expected from devising a hybrid CPU-GPU parallel algorithm that combines the highly parallel stream processing power of GPUs with the higher power of multi-core architectures. We have developed a hybrid methodology to efficiently solve optimization problems. We use a hybrid CPU-GPU architecture, to benefit from running it, in parallel, on both the CPU and the GPU. Our experiments over a heterogeneous set of combinatorial optimization problems with increasing dimensionality show a time gain of up to 365× in our proposal, while demonstrating high numerical accuracy. This work is intended to open up a new line of research that matches both architectures with new algorithms and cooperation techniques.

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Section: Introductionmentioning

confidence: 99%

Solving optimization problems using a hybrid systolic search on GPU plus CPU

2016

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“…Due to advancement in computer systems processors, the utilization of such systems is the need of the hour. As evolutionary algorithms are inherent in parallel nature, the parallel development of optimization algorithms will take benefit of it [28][29][30]. Gong et al presented a survey on the parallel implementation of evolutionary algorithms [31].…”

Section: Introductionmentioning

confidence: 99%

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Mane

Adamuthe

Omane³

2018

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system's computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.

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“…With the advent of General Purpose GPU (GPGPUs), researchers have been evolving Evolutionary Computations [13][14][15][16] for parallel implementation. Similar advancements in the field of genetic programming are quickly adopted by GA researchers.…”

Section: Introductionmentioning

confidence: 99%