A GPU-based interactive bio-inspired visual clustering

Abstract

In this work, we present an interactive visual clustering approach for the exploration and analysis of vast volumes of data. Our proposed approach is a bio-inspired collective behavioral model to be used in a 3D graphics environment. Our paper illustrates an extension of the behavioral model for clustering and a parallel implementation, using Compute Unified Device Architecture to exploit the computational power of Graphics Processor Units (GPUs). The advantage of our approach is that, as data enters the environment, the user is directly involved in the data mining process. Our experiments illustrate the effectiveness and efficiency provided by our approach when applied to a number of real and synthetic data sets

OpenABL: A Domain-Specific Language for Parallel and Distributed Agent-Based Simulations

“…FlameGPU [19] is an extension of Flame that executes agent-based models on GPU architectures. Other GPU implementations have focused on bio-inspired visual clustering [20] and on efficient compression of agent direction [21]. Several authors have performed a comparison among ABS toolkits, both sequential [22] and parallel [23].…”

Easy and efficient agent-based simulations with the OpenABL language and compiler

“…FlameGPU [15] extends Flame enabling the execution of agent-based models on GPU architectures. Other GPU implementations have focused on bio-inspired visual clustering [48] and on efficient compression of agent direction [49]. Piccione et al [50] presented an API for Parallel Discrete Event Simulations.…”