Storage-Centric System Designs for Enabling Fast, Efficient, and Low-Cost Genomic and Metagenomic Analyses
2026-08-31 • Hardware Architecture
Hardware ArchitectureDistributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors address challenges in handling large amounts of genomic and metagenomic data, which are important in many scientific and medical fields. They identify two main problems: moving huge data sets is slow and decompressing data for analysis takes extra work. Their solution is to build special storage systems that can analyze data directly where it is stored and keep it compressed but easy to access. This approach improves speed, energy use, and cost for genome data analysis. The authors hope their work will help more people use genomic data and inspire better systems for other data-heavy science areas.
genomic analysismetagenomic analysisdata compressionstorage-centric systemsdata movement bottleneckdata preparation bottleneckenergy efficiencyprecision medicinesequence datahigh-performance computing
Authors
Nika Mansouri Ghiasi
Abstract
Genomic and metagenomic analyses play critical roles in many fields, such as precision medicine, urgent clinical settings, discovering early warnings of communicable diseases, ensuring food safety through pathogen monitoring, agriculture, and scientific discovery. Due to the challenges of analyzing and storing massive volumes of genomic and metagenomic sequence data, significant efforts have been made to accelerate (meta)genomic analyses and store sequence data compressed. Despite the benefits of these techniques, we identify two major outstanding problems in accessing stored sequence data and supplying it to the analysis units: (i) the data movement bottleneck due to moving large amounts of low-reuse data from storage and the unnecessary burden on the rest of the system, and (ii) the data preparation bottleneck, where compressed sequence data needs to be first decompressed and formatted before analysis. In this dissertation, we present customized storage-centric systems, which efficiently (i) analyze (meta)genomic data inside the storage system, and (ii) enable highly-compressed storage and high-performance access of large-scale sequence data, thereby alleviating the overheads of data movement, computation, and data preparation. We demonstrate that the proposed systems significantly improve system performance, energy efficiency, and system cost-efficiency of (meta)genomic analysis. We hope that the storage-centric systems proposed in this dissertation facilitate the broader adoption of (meta)genomic analyses and inspire future research to fundamentally improve the performance, energy efficiency, and cost-effectiveness of other data-intensive application domains related to health and life sciences.