Parallelization of bioinformatics methods using a computational grid

Parallelization of Bioinformatics Algorithm using Genetic Algorithm on Multicore platform

Nowadays, multicore processor and GPUs have entered the mainstream of microprocessor development. Multicore processors are widely used across many application domains including general-purpose, embedded, network, digital signal processing and graphics. The amount of performance gained by the use of a multicore processor depends very much on the software algorithms and implementation. In particu...

Bioinformatics Workflow using ASSIST on GRID

Workflows are very common in bioinformatics because they allow the elaboration of data by delegating the resources management to the information streaming. By the composition of different analysis tools, in fact, it is possible to follow the complex evolution of the biological process. The Web Services technology is very important in this context because allows the exposition of tools that can ...

A Resourceomic Grid for bioinformatics

In this work we revise the layered software architecture for the Knowledge Grid by explicitly introducing the concept of Resourceome, i.e. an “alive” ontology of resources. This approach is necessary to tackle the challenges posed by the “ome” status, reached by the world of bioinformatics resources. The resulting software architecture, a Resourceomic Grid, integrates components to support issu...

Sparse Grid Quadrature Methods for Computational Finance

Parallelization of Phylogenetic Tree Inference Using Grid Technologies

The maximum likelihood method is considered as one of the most reliable methods for phylogenetic tree inference. However, as the number of species increases, the approach quickly loses its applicability due to explosive exponential number of trees that need to be considered. An earlier work by one of the authors [3] demonstrated that, by decomposing the trees into fragments called splits, and c...