Files in this item



application/pdfLI-THESIS-2018.pdf (303kB)
(no description provided)PDF


Title:GPU acceleration of advanced K-mer counting for computational genomics
Author(s):Li, Huiren
Advisor(s):Chen, Deming
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):k-mer counting
GPU acceleration
Abstract:k-mer counting is a popular pre-processing step in many bioinformatic algorithms. KMC2 is one of the most popular tools for k-mer counting. In this work, we leverage the computational power of the GPU to accelerate KMC2. Our goal is to reduce the overall runtime of many genome analysis tasks that use k-mer counting as an essential step. We achieved 4.03x speedup using one GTX 1080 Ti with one CPU (Xeon E5-2603) thread and 5.88x speedup using one GPU with four CPU threads over KMC2 running on a single CPU thread. This speedup is significant because accelerating k-mer counting is challenging due to reasons like serialized portions of code and overhead of disk operations.
Issue Date:2018-05-15
Rights Information:Copyright 2018 Huiren Li
Date Available in IDEALS:2018-09-27
Date Deposited:2018-08

This item appears in the following Collection(s)

Item Statistics