A Multi GPU Read Alignment Algorithm with Model-Based Performance Optimization

  • Aleksandr Drozd
  • Naoya Maruyama
  • Satoshi Matsuoka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7851)


This paper describes a performance model for read alignment problem, one of the most computationally intensive tasks in bioinformatics. We adapted Burrows Wheeler transform based index to be used with GPUs to reduce overall memory footprint. A mathematical model of computation and communication costs was developed to find optimal memory partitioning for index and queries. Last we explored the possibility of using multiple GPUs to reduce data transfers and achieved super-linear speedup. Performance evaluation of experimental implementation supports our claims and shows more than 10fold performance gain per device.


Index Size Read Alignment Memory Footprint Multiple GPUs Short Read Alignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aleksandr Drozd
    • 1
  • Naoya Maruyama
    • 1
  • Satoshi Matsuoka
    • 1
  1. 1.Tokyo Institute of TechnologyMeguro-kuJapan

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