A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks

TL;DR

This paper introduces a novel multi-signal algorithm variant for GPU-based parallelization of growing self-organizing networks, improving performance by leveraging intrinsic parallelism more effectively than traditional methods.

Contribution

The paper proposes a new algorithm variant tailored for GPU architectures that processes multiple signals simultaneously, enhancing the efficiency of growing self-organizing networks.

Findings

Better performance with smaller networks.

Effective utilization of GPU parallelism.

Improved surface reconstruction from point clouds.

Abstract

Among the many possible approaches for the parallelization of self-organizing networks, and in particular of growing self-organizing networks, perhaps the most common one is producing an optimized, parallel implementation of the standard sequential algorithms reported in the literature. In this paper we explore an alternative approach, based on a new algorithm variant specifically designed to match the features of the large-scale, fine-grained parallelism of GPUs, in which multiple input signals are processed at once. Comparative tests have been performed, using both parallel and sequential implementations of the new algorithm variant, in particular for a growing self-organizing network that reconstructs surfaces from point clouds. The experimental results show that this approach allows harnessing in a more effective way the intrinsic parallelism that the self-organizing networks algorithms seem intuitively to suggest, obtaining better performances even with networks of smaller size.