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Title:           BIMSA: accelerating long sequence alignment using processing-in-memory
Subject:         Doi: 10.1093/bioinformatics/btae631 Bioinformatics, 40, 11, 2024 Publication Date: 21/10/2024 Abstract Motivation Recent advances in sequencing technologies have stressed the critical role of sequence analysis algorithms and tools in genomics and healthcare research. In particular, sequence alignment is a fundamental building block in many sequence analysis pipelines and is frequently a performance bottleneck both in terms of execution time and memory usage. Classical sequence alignment algorithms are based on dynamic programming and often require quadratic time and memory with respect to the sequence length. As a result, classical sequence alignment algorithms fail to scale with increasing sequence lengths and quickly become memory-bound due to data-movement penalties.Results Processing-In-Memory (PIM) is an emerging architectural paradigm that seeks to accelerate memory-bound algorithms by bringing computation closer to the data to mitigate data-movement penalties. This work presents BIMSA (Bidirectional In-Memory Sequence Alignment), a PIM design and implementation for the state-of-the-art sequence alignment algorithm BiWFA (Bidirectional Wavefront Alignment), incorporating new hardware-aware optimizations for a production-ready PIM architecture (UPMEM). BIMSA supports aligning sequences up to 100K bases, exceeding the limitations of state-of-the-art PIM implementations. First, BIMSA achieves speedups up to 22.24× (11.95× on average) compared to state-of-the-art PIM-enabled implementations of sequence alignment algorithms. Second, achieves speedups up to 5.84× (2.83× on average) compared to the highest-performance multicore CPU implementation of BiWFA. Third, BIMSA exhibits linear scalability with the number of compute units in memory, enabling further performance improvements with upcoming PIM architectures equipped with more compute units and achieving speedups up to 9.56× (4.7× on average).Availability and implementation Code and documentation are publicly available at https://github.com/AlejandroAMarin/BIMSA.
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    Title: BIMSA: accelerating long sequence alignment using processing-in-memory
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  Checksum: 35e705ac
   Type: CRC32
  Checksum: dba5170bd4cf5575501badefc471b92d
   Type: MD5
  Checksum: c8decadb426b35bc773a2b2e782b6c70239ebcb1
   Type: SHA-1
  Checksum: 6c23efc7d48a7e993385e3a2d854e7191a4ff32dd7081a91d600f808259c1762
   Type: SHA-256