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CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU.

Hanyu Jiang1, Narayan Ganesan2

  • 1Department of Elec. and Comp. Engg, Stevens Institute of Technology, Hoboken, NJ, 07030, USA. hjiang5@stevens.edu.

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|February 28, 2016
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Summary
This summary is machine-generated.

This study introduces CUDAMPF, a GPU-accelerated framework that significantly speeds up HMMER sequence analysis. CUDAMPF enhances homology detection performance by optimizing computationally intensive stages like MSV/SSV and P7Viterbi.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • HMMER software is crucial for sensitive homologous sequence analysis.
  • The HMMER 3.x pipeline uses MSV/SSV, P7Viterbi, and Forward stages for homology detection.
  • While optimized for CPUs, intensive pipeline stages can benefit from parallel processing.

Purpose of the Study:

  • To accelerate computationally intensive stages of the HMMER pipeline using GPUs.
  • To develop a hardware-aware parallel framework for enhanced sequence alignment performance.

Main Methods:

  • Implemented a Multi-Tiered Parallel Framework (CUDAMPF) on CUDA-enabled GPUs.
  • Utilized SIMT and SIMD with warp-synchronism for high-throughput processing.
  • Incorporated hardware-aware resource allocation and runtime compilation (NVRTC) for dynamic kernel switching.

Main Results:

  • CUDAMPF achieves significant speedups for MSV/SSV and Viterbi algorithms.
  • Runtime compilation provides up to 2-3x speedup over static compilation.
  • Evaluations show up to 37.5-fold speedup on GPUs compared to high-end CPUs with 100% accuracy.

Conclusions:

  • CUDAMPF effectively accelerates HMMER's computational hotspots on GPUs.
  • The framework exploits hierarchical parallelism and optimizes resource utilization.
  • CUDAMPF offers substantial performance gains for sequence alignment applications.