High Performance Seed-and-Extend Algorithms for Genomics

Doctoral thesis (2020)

Authors

N. Ahmed Quantum & Computer Engineering
Department
Quantum & Computer Engineering
Copyright: © 2020 N. Ahmed
DOI: https://doi.org/10.4233/uuid:7e916f03-09cc-4510-9914-03a44b339462
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Published Date

2020

Language

English

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Department

Quantum & Computer Engineering

Abstract

Recent advances in DNA sequencing technology have opened new doors for scientists to use genomic data analysis in a variety of applications that directly affect human lives. However, the analysis of unprecedented volumes of sequencing data being produced represents a formidable computational challenge. The conventional CPU-only computing paradigm is not sufficient to analyze exponentially growing sequencing data in a cost-effective and timely manner. Heterogeneous computing systems with GPU and FPGA based accelerators have become easily accessible and are increasingly being used to process massive amounts of data due to their better performance-to-cost ratio than CPU-only platforms. Furthermore, highly optimized analysis algorithms are required to extract the maximum computational power of these computing systems.