geneticalgorithms 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 ...
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geneticalgorithms 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.
Computational biology research is now faced with the burgeoning number of genome data. The rigorous postprocessing of this data requires an increased role for high-performance computing ( HPC). Because the development...
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Computational biology research is now faced with the burgeoning number of genome data. The rigorous postprocessing of this data requires an increased role for high-performance computing ( HPC). Because the development of HPC applications for computational biology problems is much more complex than the corresponding sequential applications, existing traditional programming techniques have demonstrated their inadequacy. Many high level programming techniques, such as skeleton and pattern-based programming, have therefore been designed to provide users new ways to get HPC applications without much effort. However, most of them remain absent from the mainstream practice for computational biology. In this paper, we present a new parallel pattern-based system prototype for computational biology. The underlying programming techniques are based on generic programming, a programming technique suited for the generic representation of abstract concepts. This allows the system to be built in a generic way at application level and, thus, provides good extensibility and flexibility. We show how this system can be used to develop HPC applications for popular computational biology algorithms and lead to significant runtime savings on distributed memory architectures.
The paper presents the recent developments in hierarchicalparallel Evolutionary algorithms to speed up optimisation of aerodynamic shapes. One is the implementation of different models in different layers of a Parall...
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The paper presents the recent developments in hierarchicalparallel Evolutionary algorithms to speed up optimisation of aerodynamic shapes. One is the implementation of different models in different layers of a parallelgenetic Algorithm. The other is Asynchronous hierarchical Evolution Strategy. These methods are employed to reconstruct a one-dimensional transonic nozzle and a two-dimensional aerofoil shape. Considerable speed up is achieved as a result.
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