Parallel Genetic Algorithm on the CUDA Architecture

Abstract. This paper deals with the mapping of the parallel island-based genetic algorithm with unidirectional ring migrations to nVidia CUDA software model. The proposed mapping is tested using Rosen-brock’s, Griewank’s and Michalewicz’s benchmark functions. The ob-tained results indicate that our approach leads to speedups up to seven thousand times higher compared to one CPU thread while maintaining a reasonable results quality. This clearly shows that GPUs have a potential for acceleration of GAs and allow to solve much complex tasks.

On the Design of a Parallel Object-Oriented Data Mining Toolkit On the Design of a Parallel Object-Oriented Data Mining Toolkit

Abstract As data mining techniques are applied to ever larger data sets, it is becoming clear that parallel processors will play an important role in reducing the turn around time for data analysis. In this paper, we describe the design of a parallel object-oriented toolkit for mining scientific data sets. After a brief discussion of our design goals, we describe our overall system design that uses data mining to find useful information in raw data in an iterative and interactive manner. Using decision trees as an example, we illustrate how the need to support flexibility and extensibility can make the parallel implementation of our algorithms very challenging. As this is work in progress, we also describe the solution approaches we are considering to address these challenges