University of Illinois Urbana-Champaign

GPU acceleration of advanced K-mer counting for computational genomics

Li, Huiren

Content Files
LI-THESIS-2018.pdf

Permalink

https://hdl.handle.net/2142/101639

Description

Title
GPU acceleration of advanced K-mer counting for computational genomics
Author(s)
Li, Huiren
Issue Date
2018-05-15
Director of Research (if dissertation) or Advisor (if thesis)
Chen, Deming
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-09-27T16:28:01Z
Keyword(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.
Graduation Semester
2018-08
Type of Resource
text
Permalink
http://hdl.handle.net/2142/101639
Copyright and License Information
Copyright 2018 Huiren Li

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois