Genetic algorithms are a general problem-solving technique that has been widely used in computational biology. In this paper, we present a framework to map hierarchical parallel genetic algorithms for protein folding problems onto computational grids. By using this framework, the two level communication parts of hierarchical parallel genetic algorithms are separated. Thus both parts of the algorithm can evolve independently. This permits users to experiment with alternative communication models on different levels conveniently. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the framework to be built in a generic way at application level and thus provides good extensibility and flexibility. Experiments show that it can lead to significant runtime savings on PC clusters and computational grids.

This paper presents a prototype of a distributed 3D rendering system in a hierarchical Grid environment. 3D rendering with massive data sets is a computationally intensive task. In order to make full use of computational resources on Grids, a hierarchical system architecture is designed to run over multiple clusters. This architecture involves both sort-first and sort-last parallel rendering algorithms to achieve excellent scalability, rendering performance and load balance.