Paper Info
Title
Parallelization and fault-tolerance of evolutionary computation on many-core processors.
Abstract
We report on fault-tolerant technology for use with high-speed parallel evolutionary computation on many-core processors. In particular, for distributed GA models which communicate between islands, we propose a method where an island's ID number is added to the header of data transferred by this island for use in fault detection, and we evaluate this method using Deceptive functions and Sudoku puzzles. As a result, we show that it is possible to detect single stuck-at faults with practically negligible overheads in applications where the time spent performing genetic operations is large compared with the data transfer speed between islands. We also show that it is still possible to obtain an optimal solution when a single stuck-at fault is assumed to have occurred, and that increasing the number of parallel threads has the effect of making the system less susceptible to faults and more sustainable.
Year
DOI
Venue
2013
10.1109/CEC.2013.6557883
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
fault tolerant computing,genetic algorithms,multiprocessing systems,parallel processing,Sudoku puzzle,deceptive function,distributed GA model,evolutionary computation,fault tolerance,genetic algorithm,many-core processor,parallel thread,parallelization,stuck-at fault detection,Evolutionary Computation,Fault-tolerance,Genetic Algorithms,Many-core Processors,Parallelization
Data transmission,Computer science,Fault detection and isolation,Parallel computing,Evolutionary computation,Thread (computing),Fault tolerance,Header,Genetic algorithm,Overhead (business),Distributed computing
Conference
Citations 
PageRank 
References 
2
0.37
0
Authors
2
Name
Order
Citations
PageRank
Yuji Sato14818.14
Mikiko Sato22211.53