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Lecture/Seminar Graduate Student Seminar: Accelerating cn.MOPS with GPU 2017-05-05 11:15:00
Description

Speaker: Mohammad Alkhamis
Supervisor: Dr. Amirali Baniasadi

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 accelerate processing with multi-CPU parallelism. For very large data sets, however, cn.MOPS requires many CPU cores and a large memory space to significantly reduce execution time. In this thesis, an alternative mechanism of parallelism is proposed. Using one CPU core and a GPU device, the proposed solution, gcn.MOPS, delivered the equivalent performance of cn.MOPS running on 237× CPU cores and decreased memory usage by more than half.

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Location ENGINEERING OFFICE WING
430
Times 11:15:00 to 12:15:00
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