Towards Accelerated Genome Informatics on Parallel HPC Platforms: The ReneGENE-GI Perspective

  • Santhi NatarajanEmail author
  • Krishna Kumar N.
  • Debnath Pal
  • S. K. Nandy


Genome Informatics (GI) involves accurate computational investigations of strongly correlated subsystems that demands inter-disciplinary approaches for problem solving. With the growing volume of genomic sequencing data at an alarming rate, High Performance Computing (HPC) solutions offer the right platform to address the computational needs. GI requires algorithm-architecture co-design of parallel and accelerated biocomputing involving reconfigurable hardware like FPGAs and graphics accelerators or GPUs, to bridge the gap between growing data volumes and compute capabilities. Such platforms offer high degrees of parallelism and scalability, while accelerating the multi-stage GI computational pipeline. Amidst such high computing power, it is the choice of algorithms and implementations in the entirety of the GI pipeline that decides the precision of bio-computing in revealing biologically relevant information. Through this paper, we present ReneGENE-GI, an innovatively engineered GI pipeline. This paper details the performance analysis of ReneGENE-GI’s Comparative Genomics Module (CGM), the compute intensive stage of the pipeline. This module comes in two flavours, designed to run on GPUs and FPGAs respectively, hosted on HPC platforms. The pipeline uses a very efficient reference indexing algorithm based on the dynamic Monotonic Minimal Perfect Hashing Function (MMPH), allowing an absolute indexing for the reference genome, thus avoiding heuristics. Alignment time for our FPGA version is about one-tenth the time taken by our single GPU implementation, which itself is 2.62x faster than CUSHAW2-GPU (the GPU CUDA implementation of CUSHAW). With the single-GPU implementation demonstrating a speed up of 150+ x over standard heuristic aligners in the market like BFAST, the FPGA version of our CGM is several orders faster than the competitors, offering precision over heuristics.


Genome informatics High performance computing Reconfigurable hardware GPU FPGA Accelerator hardware NGS Short read mapping Sequencing 



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Santhi Natarajan
    • 1
    Email author
  • Krishna Kumar N.
    • 1
  • Debnath Pal
    • 1
  • S. K. Nandy
    • 1
  1. 1.Indian Institute of ScienceBangaloreIndia

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