arXiv subjects

Talu Güloglu

Publications and source records attributed to Talu Güloglu.

2 recordsLinked to original sources

arXiv2026

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

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.

cs.AR↗

arXiv2025

SAGe: A Lightweight Algorithm-Architecture Co-Design for Mitigating the Data Preparation Bottleneck in Large-Scale Genome Sequence Analysis

Genome sequence analysis, which examines the DNA sequences of organisms, drives advances in many critical medical and biotechnological fields. Given its importance and the exponentially growing volumes of genomic sequence data, there are extensive efforts to accelerate genome sequence analysis. In this work, we demonstrate a major bottleneck that greatly limits and diminishes the benefits of state-of-the-art genome sequence analysis accelerators: the data preparation bottleneck, where genomic sequence data is stored in compressed form and needs to be first decompressed and formatted before an accelerator can operate on it. To mitigate this bottleneck, we propose SAGe, an algorithm-architecture co-design for highly-compressed storage and high-performance access of large-scale genomic sequence data. The key challenge is to improve data preparation performance while maintaining high compression ratios (comparable to genomic-specific compression algorithms) at low hardware cost. We address this challenge by leveraging key properties of genomic datasets to co-design (i) a lossless (de)compression algorithm, (ii) hardware that decompresses data with lightweight operations and efficient streaming accesses, (iii) storage data layout, and (iv) interface commands to access data. SAGe is highly versatile, as it supports datasets from different sequencing technologies and species. Due to its lightweight design, SAGe can be seamlessly integrated with a broad range of hardware accelerators for genome sequence analysis to mitigate their data preparation bottlenecks. Our results demonstrate that SAGe improves the average end-to-end performance and energy efficiency of two state-of-the-art genome sequence analysis accelerators by 3.0x-32.1x and 13.0x-34.0x, respectively, compared to when the accelerators rely on state-of-the-art software and hardware decompression tools.

cs.AR↗