Title :
Multi-GPU island-based genetic algorithm for solving the knapsack problem
Author_Institution :
ANU Coll. of Eng. & Comput. Sci., Australian Nat. Univ., Canberra, ACT, Australia
Abstract :
This paper introduces a novel implementation of the genetic algorithm exploiting a multi-GPU cluster. The proposed implementation employs an island-based genetic algorithm where every GPU evolves a single island. The individuals are processed by CUDA warps, which enables the solution of large knapsack instances and eliminates undesirable thread divergence. The MPI interface is used to exchange genetic material among isolated islands and collect statistical data. The characteristics of the proposed GAs are investigated on a two-node cluster composed of 14 Fermi GPUs and 4 six-core Intel Xeon processors. The overall GPU performance of the proposed GA reaches 5.67 TFLOPS.
Keywords :
genetic algorithms; graphics processing units; knapsack problems; message passing; multiprocessing systems; CUDA warps; Fermi GPU; MPI interface; genetic material; knapsack problem; multiGPU cluster; multiGPU island-based genetic algorithm; six-core Intel Xeon processors; thread divergence; two-node cluster; Biological cells; Genetic algorithms; Genetics; Graphics processing unit; Instruction sets; Kernel; Layout; CUDA; GA; GPU; MPI; island model; knapsack;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6256131
