gcn.MOPS: accelerating cn.MOPS with GPU
Date
2017-06-16
Authors
Alkhamis, Mohammad
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Abstract
cn.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×.
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Keywords
GPU, GPGPU, cn.MOPS, gcn.MOPS, CUDA, C++, parallel computing, CNV