• DOI:10.1109/MICRO56248.2022.00056
• Corpus ID: 252367927

GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping

@article{Mao2022GenPIPIA,
  title={GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping},
  author={Haiyu Mao and Mohammed H. Alser and Mohammad Sadrosadati and Can Firtina and Akanksha Baranwal and Damla Senol Cali and Aditya Manglik and Nour Almadhoun Alserr and Onur Mutlu},
  journal={2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)},
  year={2022},
  pages={710-726}
}

• Haiyu Mao, M. Alser, +6 authors O. Mutlu
• Published 18 September 2022
• Biology, Engineering, Computer Science
• 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)

Nanopore sequencing is a widely-used high-throughput genome sequencing technology that can sequence long fragments of a genome into raw electrical signals at low cost. Nanopore sequencing requires two computationally-costly processing steps for accurate downstream genome analysis. The first step, basecalling, translates the raw electrical signals into nucleotide bases (i.e., A, C, G, T). The second step, read mapping, finds the correct location of a read in a reference genome. In existing genome analysis pipelines, basecalling and read mapping are executed separately. We observe in this work that such separate execution of the two most time-consuming steps inherently leads to ❨1❩ significant data movement and ❨2❩ redundant computations on the data, slowing down the genome analysis pipeline. This paper proposes GenPIP, an in-memory genome analysis accelerator that tightly integrates basecalling and read mapping. GenPIP improves the petformance of the genome analysis pipeline with two key mechanisms: ❨1❩ in-memory fine-grained collaborative execution of the major genome analysis steps in parallel}; ❨2❩ a new technique for early-rejection of low-quality and unmapped reads to timely stop the execution of genome analysis for such reads, reducing inefficient computation. Our experiments show that, for the execution of the genome analysis pipeline, GenPIP provides 41.6$\times$ (8.4$\times$) speedup and 32.8$\times$ (20.8$\times$) energy savings with negligible accuracy loss compared to the state-of-the-art software genome analysis tools executed on a state-of-the-art CPU (GPU). Compared to a design that combines state-of-the-art in-memory basecalling and read mapping accelerators, GenPIP provides 1.39$\times$ speedup and 1.37$\times$ energy savings.