A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks
This work addresses performance bottlenecks in self-organizing networks for applications like surface reconstruction from point clouds, but it is incremental as it builds on existing parallelization methods.
The paper tackles the parallelization of growing self-organizing networks by introducing a new algorithm variant designed for GPU parallelism, processing multiple input signals simultaneously. Experimental results show this approach achieves better performance with smaller networks compared to standard sequential implementations.
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.