Multi-GPU island-based genetic algorithm for solving the knapsack problem
J Jaros�- 2012 IEEE Congress on Evolutionary Computation, 2012 - ieeexplore.ieee.org
2012 IEEE Congress on Evolutionary Computation, 2012•ieeexplore.ieee.org
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�…
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�…
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.
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