Accelerating swarm intelligence algorithms with GPU-computing

Abstract

Swarm intelligence describes the ability of groups of social animals and insects to exhibit highly organized and complex problem-solving behaviors that allow the group as a whole to accomplish tasks which are beyond the capabilities of any individual. This phenomenon found in nature is the inspiration for swarm intelligence algorithms—systems that utilize the emergent patterns found in natural swarms to solve computational problems. In this paper, we will show that due to their implicitly parallel structure, swarm intelligence algorithms of all sorts can benefit from GPU-based implementations. To this end, we present the ClusterFlockGPU algorithm, a swarm intelligence data mining algorithm for partitional cluster analysis based on the flocking behaviors of birds and implemented with CUDA. Our results indicate that ClusterFlockGPU is competitive with other swarm intelligence and traditional clustering methods. Furthermore, the algorithm exhibits a nearly linear time complexity with respect to the number of data points being analyzed and running time is not affected by the dimensionality of the data being clustered, thus making it well-suited for high-dimensional data sets. With the GPU-based implementation adopted here, we find that ClusterFlockGPU is up to 55x times faster than a sequential implementation and its time complexity is significantly reduced to nearly O(n).