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BIMSA: accelerating long sequence alignment using processing-in-memory.

Alejandro Alonso-Marín1,2,3, Ivan Fernandez1,4, Quim Aguado-Puig1,3,5

  • 1Department of Computer Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain.

Bioinformatics (Oxford, England)
|October 21, 2024
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Summary
This summary is machine-generated.

This study introduces BIMSA, a Processing-In-Memory design for faster sequence alignment. BIMSA accelerates genomics research by reducing data movement bottlenecks in sequence analysis algorithms.

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Area of Science:

  • Genomics
  • Computer Architecture
  • Bioinformatics

Background:

  • Advances in sequencing technologies necessitate efficient sequence analysis tools.
  • Sequence alignment is a critical but often performance-bottlenecking step in genomics pipelines.
  • Classical algorithms struggle with large datasets due to memory and time complexity.

Purpose of the Study:

  • To develop a Processing-In-Memory (PIM) design for accelerating sequence alignment.
  • To implement and optimize the Bidirectional Wavefront Alignment (BiWFA) algorithm on a PIM architecture.
  • To overcome the limitations of existing PIM implementations for sequence alignment.

Main Methods:

  • Designed and implemented BIMSA (Bidirectional In-Memory Sequence Alignment) on the UPMEM PIM architecture.
  • Incorporated hardware-aware optimizations tailored for BiWFA.
  • Evaluated performance against state-of-the-art PIM and CPU implementations.

Main Results:

  • BIMSA achieves significant speedups: up to 22.24x over PIM-enabled algorithms and 5.84x over CPU implementations.
  • Supports aligning sequences up to 100K bases, surpassing current PIM capabilities.
  • Demonstrates linear scalability with memory compute units, promising future performance gains.

Conclusions:

  • BIMSA effectively accelerates sequence alignment using Processing-In-Memory.
  • The design offers substantial performance improvements for genomics and healthcare research.
  • BIMSA's scalability paves the way for next-generation PIM architectures in bioinformatics.