Abstract
Genomic sequencing is rapidly becoming a premier generator of Big Data, posing great computational challenges. Hence, acceleration of the algorithms used is of utmost importance. This paper presents a GPU-accelerated implementation of BWA-MEM, a widely used algorithm to map genomic sequences onto a reference genome. BWA-MEM contains three main computational functions: Seed Generation, Seed Extension and Output Generation. This paper discusses acceleration of the Seed Extension function on a GPU accelerator.
The GPU-based Extend kernel achieves three times higher performance and, by offloading the kernel onto an accelerator and overlapping its execution with the other functions, this results in an overall improvement to application-level execution time of up to 1.6x.
To ensure that using an accelerator always results in an overall performance improvement, especially when considering slower GPUs, an adaptive load balancing solution is introduced, which intelligently distributes work between host and GPU. This provides, compared to not using load balancing, up to +46 % more performance.
The GPU-based Extend kernel achieves three times higher performance and, by offloading the kernel onto an accelerator and overlapping its execution with the other functions, this results in an overall improvement to application-level execution time of up to 1.6x.
To ensure that using an accelerator always results in an overall performance improvement, especially when considering slower GPUs, an adaptive load balancing solution is introduced, which intelligently distributes work between host and GPU. This provides, compared to not using load balancing, up to +46 % more performance.
Original language | English |
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Title of host publication | Proceedings - 29th International Conference on Architecture of Computing Systems, ARCS 2016 |
Editors | Frank Hannig, João M. P. Cardoso, Thilo Pionteck, Dietmar Fey, Wolfgang Schröder-Preikschat, Jürgen Teich |
Publisher | Springer |
Pages | 130-142 |
Number of pages | 13 |
ISBN (Electronic) | 978-3-319-30695-7 |
ISBN (Print) | 978-3-319-30694-0 |
DOIs | |
Publication status | Published - 2016 |
Event | Architecture of Computing Systems, ARCS 2016: 29th International Conference - Nuremberg, Germany Duration: 4 Apr 2016 → 7 Apr 2016 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 9637 |
Conference
Conference | Architecture of Computing Systems, ARCS 2016 |
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Country/Territory | Germany |
City | Nuremberg |
Period | 4/04/16 → 7/04/16 |
Keywords
- Acceleration
- BWA-MEM
- GPU
- High performance genomics