high performance computing on graphics processing units: hgpu.org

The exponential growth in bioinformatics data generation and the stagnation of processor frequencies in modern processors stress the need for efficient implementations that fully exploit the parallel capabilities offered by modern computers. This thesis focuses on parallel algorithms and implementations for bioinformatics problems. Various types of parallelism are described and exploited. This thesis presents applications in genetics with a GPU parallel tool for QTL detection, in protein structure comparison with a multicore parallel tool for finding similar regions between proteins, and large graph analysis with a multi-GPU parallel implementation for a novel algorithm for the All-Pairs Shortest Path problem.