GPU acceleration for the web browser based evolutionary computing system

This paper presents a novel approach for the acceleration of distributed computing system entirely based on web browsers. We propose two strategies of embedding GPU kernels into the JavaScript code that is run by clients' machines (computing agents) participated in the computing grid, and analyze the speed increase resulting from the application of these methods. The computational experiments are performed on the basis of the two standard optimization problems: a travelling salesman problem and a flowshop scheduling problem. According to the obtained results the calculation of a fitness function accelerated by GPU may bring up to 50% reduction in execution time, while a local search process accelerated by GPU may be reduced tenfold.

[1]  Thomas Sttzle,et al.  Applying iterated local search to the permutation ow shop problem , 1998 .

[2]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[3]  Juan Julián Merelo Guervós,et al.  Asynchronous distributed genetic algorithms with Javascript and JSON , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[4]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[5]  Marian Bubak,et al.  Distributed Computing on an Ensemble of Browsers , 2013, IEEE Internet Computing.

[6]  Jerzy Duda,et al.  Distributed Evolutionary Computing System Based on Web Browsers with JavaScript , 2012, PARA.

[7]  E. Talbi.  Parallel combinatorial optimization , 2006 .

[8]  Juan Julián Merelo Guervós,et al.  Browser-based distributed evolutionary computation: performance and scaling behavior , 2007, GECCO '07.

[9]  Christos D. Tarantilis,et al.  Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm , 2009, Comput. Oper. Res..

[10]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[11]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[12]  El-Ghazali Talbi,et al.  GPU Computing for Parallel Local Search Metaheuristic Algorithms , 2013, IEEE Transactions on Computers.

[13]  É. Taillard.  Some efficient heuristic methods for the flow shop sequencing problem , 1990 .

[14]  Wojciech Bozejko,et al.  Parallel Calculating of the Goal Function in Metaheuristics Using GPU , 2009, ICCS.

[15]  Yannis A. Dimitriadis,et al.  Grid Characteristics and Uses: A Grid Definition , 2003, European Across Grids Conference.