MetaStore: High-Performance Metagenomic Analysis via In-Storage Computing.

CoRR(2023)

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摘要
Metagenomics has led to significant advancements in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative abundances. These tasks require searching large metagenomic databases containing information on different species' genomes. Metagenomic analysis suffers from significant data movement overhead due to moving large amounts of low-reuse data from the storage system to the rest of the system. In-storage processing can be a fundamental solution for reducing data movement overhead. However, designing an in-storage processing system for metagenomics is challenging because none of the existing approaches can be directly implemented in storage effectively due to the hardware limitations of modern SSDs. We propose MetaStore, the first in-storage processing system designed to significantly reduce the data movement overhead of end-to-end metagenomic analysis. MetaStore is enabled by our lightweight and cooperative design that effectively leverages and orchestrates processing inside and outside the storage system. Through our detailed analysis of the end-to-end metagenomic analysis pipeline and careful hardware/software co-design, we address in-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning, 2) data/computation flow coordination, 3) storage technology-aware algorithmic optimizations, 4) light-weight in-storage accelerators, and 5) data mapping. Our evaluation shows that MetaStore outperforms the state-of-the-art performance- and accuracy-optimized software metagenomic tools by 2.7-37.2$\times$ and 6.9-100.2$\times$, respectively, while matching the accuracy of the accuracy-optimized tool. MetaStore achieves 1.5-5.1$\times$ speedup compared to the state-of-the-art metagenomic hardware-accelerated tool, while achieving significantly higher accuracy.
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