Ultra Efficient Acceleration for De Novo Genome Assembly via Near-Memory Computing

De novo assembly of genomes for which there is no reference, is essential for novel species discovery and metagenomics. In this work, we accelerate two key performance bottlenecks of DBG-based assembly, graph construction and graph traversal, with a near-data processing (NDP) architecture based on 3D-stacking. The proposed framework distributes key operations across NDP cores to exploit a high degree of parallelism and high memory bandwidth. We propose several optimizations based on domain-specific properties to improve the performance of our design. We integrate the proposed techniques into an existing DBG assembly tool, and our simulation-based evaluation shows that the proposed NDP implementation can improve the performance of graph construction by 33x and traversal by 16x compared to the state-of-the-art.