GRAINS: Storage-Aware Algorithm-Architecture Co-Design Enabling High-Performance and Low-Cost Graph-Based Genome Analysis

2026-06-24

AI Summary

This work addresses the high storage access overhead in large-scale genomic graph analysis, which stems from low data reuse and limits the efficiency of existing tools in leveraging storage devices. The paper presents the first near-storage computing system tailored for genomic graph analytics, employing algorithm-architecture co-design to minimize data movement through on-chip and in-flash processing. By exploiting SSD hardware characteristics, the system implements a lightweight scheduling mechanism to enhance parallelism. Key innovations include a graph-aware batching scheme, a low-overhead scheduling strategy, and a holistic software-hardware co-optimization methodology. Experimental results demonstrate that the proposed system achieves 2.7–47.8× higher performance and 4.4–31.6× lower energy consumption compared to state-of-the-art software baselines, and outperforms existing hardware acceleration approaches by 1.5–17.0× in performance and 3.1–20.7× in energy efficiency.

Abstract

Graph-based representations of genome sequences have emerged as a powerful approach for representing massive genomic databases in an expressive and efficient way. Despite their benefits, analysis on large-scale genome graphs incurs significant data movement overhead from the storage system due to accessing large amounts of low-reuse data. Processing data directly inside the storage device can be a fundamental solution for mitigating this overhead. However, none of the existing tools for graph-based genome analysis can be efficiently used inside the storage system due to the limited internal hardware resources in modern SSDs. At the same time, prior storage-centric systems developed for (i) traditional, linear non-graph-based genome analysis or (ii) conventional, non-genomic graph analysis are not suitable for the unique data structures and access patterns of graph-based genome analysis. We propose GRAINS, the first system for analysis with large-scale genome graphs in storage. Through our detailed examination of typical analysis pipelines that operate on genome graphs, we perform storage-aware algorithm-architecture co-design to (i) make these pipelines more storage-friendly and (ii) further improve performance, energy-efficiency, and cost via in-storage and in-flash processing. GRAINS's co-design is based on three key aspects. First, we propose a new batching and execution flow, based on unique features of genome graphs. Second, via in-flash and in-storage processing, we avoid transferring low-reused flash pages. Third, to leverage the full parallelism of flash dies, we design an effective, yet lightweight, scheduling technique, enabled by re-purposing the existing SSD structures. GRAINS provides 2.7x-47.8x speedup (4.4x-31.6x energy reduction) over the state-of-the-art software baselines, and 1.5x-17.0x speedup (3.1x-20.7x energy reduction) over a hardware-accelerated baseline.

Problem

• genome graphs
• storage-aware computing
• data movement overhead
• in-storage processing
• algorithm-architecture co-design

Innovation

• storage-aware co-design
• in-storage processing
• genome graph analysis
• flash-based acceleration
• algorithm-architecture co-design