Alkhamis, Mohammad2017-06-162017-06-1620172017-06-16http://hdl.handle.net/1828/8286cn.MOPS is a model-based algorithm used to quantitatively detect copy-number variations in next-generation, DNA-sequencing data. The algorithm is implemented as an R package and can speed up processing with multi-CPU parallelism. However, the maximum achievable speedup is limited by the overhead of multi-CPU parallelism, which increases with the number of CPU cores used. In this thesis, an alternative mechanism of process acceleration is proposed. Using one CPU core and a GPU device, the proposed solution, gcn.MOPS, achieved a speedup factor of 159× and decreased memory usage by more than half. This speedup was substantially higher than the maximum achievable speedup in cn.MOPS, which was ∼20×.enAvailable to the World Wide WebGPUGPGPUcn.MOPSgcn.MOPSCUDAC++parallel computingCNVThesis