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arxiv: 2606.26468 · v1 · pith:W7JBQSU5new · submitted 2026-06-25 · 💻 cs.AR · cs.DC· q-bio.GN· q-bio.QM

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

Nika Mansouri Ghiasi , Harun Mustafa , Talu G\"uloglu , Rakesh Nadig , Konstantina Koliogeorgi , Susana Rebolledo Ruiz , Marc Rautmann , Furkan Eris
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Mohammad Sadrosadati Jisung Park Onur Mutlu
This is my paper

Pith reviewed 2026-06-26 03:02 UTC · model grok-4.3

classification 💻 cs.AR cs.DCq-bio.GNq-bio.QM
keywords genome graphsin-storage processingSSDalgorithm-architecture co-designgraph-based genome analysisflash storagebatchingscheduling
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The pith

GRAINS moves graph-based genome analysis inside SSDs via co-design to cut data movement and deliver up to 47.8x speedup.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents GRAINS as the first system that runs large-scale genome graph analysis directly in storage. It achieves this by adapting analysis pipelines through storage-aware algorithm-architecture co-design that includes a new batching flow, in-flash processing of low-reuse data, and lightweight scheduling that re-purposes existing SSD structures. The goal is to exploit flash-die parallelism while avoiding transfers that dominate conventional approaches. If correct, the work shows that genome graphs' access patterns can be made storage-friendly enough to outperform both pure software and prior hardware-accelerated baselines in speed and energy. Readers care because genomic databases keep growing and external data movement remains the dominant cost.

Core claim

GRAINS is the first system for analysis with large-scale genome graphs in storage. Through storage-aware algorithm-architecture co-design it makes pipelines more storage-friendly and improves performance, energy-efficiency, and cost via in-storage and in-flash processing. The co-design rests on three elements: a new batching and execution flow based on unique features of genome graphs, in-flash and in-storage processing that avoids transferring low-reused flash pages, and an effective yet lightweight scheduling technique that leverages full flash-die parallelism by re-purposing existing SSD structures.

What carries the argument

Lightweight scheduling technique that re-purposes existing SSD structures to exploit flash-die parallelism for genome-graph access patterns.

If this is right

  • Delivers 2.7x-47.8x speedup and 4.4x-31.6x energy reduction over state-of-the-art software baselines.
  • Delivers 1.5x-17.0x speedup and 3.1x-20.7x energy reduction over a hardware-accelerated baseline.
  • Avoids transfer of low-reused flash pages through in-flash and in-storage processing.
  • Improves performance, energy efficiency, and cost by making graph pipelines more storage-friendly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same co-design pattern may apply to other low-reuse irregular graph workloads if analogous batching rules can be derived from their access patterns.
  • Wider adoption could shift genomic computing clusters away from high-bandwidth host memory toward denser flash-based accelerators.
  • SSD firmware vendors might expose similar re-purposable structures for domain-specific in-storage graph kernels beyond genomics.

Load-bearing premise

Unique access patterns of genome graphs allow a lightweight batching and scheduling scheme to fully exploit flash-die parallelism without new hardware or contention that negates the in-storage benefit.

What would settle it

Measure end-to-end runtime of GRAINS versus the hardware-accelerated baseline on genome graphs whose access patterns produce high contention inside the repurposed SSD structures; if speedup falls below 1x the claim does not hold.

Figures

Figures reproduced from arXiv: 2606.26468 by Furkan Eris, Harun Mustafa, Jisung Park, Konstantina Koliogeorgi, Marc Rautmann, Mohammad Sadrosadati, Nika Mansouri Ghiasi, Onur Mutlu, Rakesh Nadig, Susana Rebolledo Ruiz, Talu G\"uloglu.

Figure 1
Figure 1. Figure 1: An example de Bruijn graph and its compacted form. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: High-level overview of graph-based genome analysis. 2.3. Unique Characteristics of Genome Graphs The type of data encoded in genome graphs and the underlying goals of querying genome graphs exhibit unique features not encountered in conventional, non-genome graphs (e.g., social networks, web graphs, road networks). General-purpose graph data structures and methods do not account for these features, leading… view at source ↗
Figure 5
Figure 5. Figure 5: shows the throughput of the tools normalized to No-I/O, across different database sizes. The larger graph (G_MetaSUB) is the one discussed earlier. The smaller graph (G_SRArep) is generated from a representative subset of the Sequencing Read Archive’s [157] public portion. The resulting graph (with colors) is 161 GB for Fulgor and 231 GB for Meta￾Graph. We observe that, in all cases, I/O overhead is substa… view at source ↗
Figure 4
Figure 4. Figure 4: Normalized throughput of (i) Fulgor and (ii) MetaGraph under different storage configurations and query sizes. 4Note that smaller queries are also critical, as some applications (e.g., searching for antimicrobial resistance [143,148,264,271] or for a single gene of interest [121]) require searching a single or a few genes within a large graph [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: Overview of the DBG structures and k-mer query. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Graph color encoding used in GRAINS. 6. Input Query Processing In this step, GRAINS prepares reads for efficient graph queries in the next step (§7). We perform two new optimizations in￾spired by the characteristics of genome graphs and queries. Cross-Read K-Mer Batching. Instead of querying each read in a read set individually, we analyze k-mers across differ￾ent reads together. This way, we can batch k-m… view at source ↗
Figure 9
Figure 9. Figure 9: Overview of GRAINS’s query processing. tition the k-mers into batches corresponding to equal, disjoint ranges of Sizes values ( 3 ). Since these values serve as indices into Offsets, this partitioning enables an efficient pipeline: the sorting of one batch overlaps with the data transfer and Off￾sets lookups of the previous batch. Second, during sorting, we further compact k-mers by exploiting the fact tha… view at source ↗
Figure 10
Figure 10. Figure 10: provides an overview of GRAINS’s Offsets lookups. GRAINS receives Offsets indices and compacted k-mers from the host in chunks ( 1 ). The internal DRAM holds two small chunks: one incoming from the host and one used for query￾ing Offsets. For an SSD with 16 channels, 8 dies/channel, 4 planes/die, and 4-KiB pages, GRAINS needs two 2-MiB chunks. GRAINS reads the values from DRAM, maps them to physical addre… view at source ↗
Figure 11
Figure 11. Figure 11: Overview of GRAINS’s Strings lookups. 7.3. Accessing Graph Colors For each query k-mer that matches a unitig (§7.2), GRAINS retrieves the color of that unitig. Due to GRAINS’s efficient scheduling (§7.2), unitig IDs are already retrieved in the inter￾nal DRAM in page order. Thus, given the underlying color encoding (§5), the unitigs’ colors can be obtained via efficient sequential scans of the Color Bitma… view at source ↗
Figure 12
Figure 12. Figure 12: Overview of GRAINS’s Colors lookups. 7.4. Detailed Design of the IFP Processing Elements GRAINS’s IFP PEs follow a high-level flow: (i) a page is read into the die’s page buffer, (ii) the page’s content goes through ECCLITE, and (iii) the PE operates on the page buffer contents. IFP Hardware Units. GRAINS’s IFP PEs implement two lightweight functions: (i) selection, which extracts a targeted window of a p… view at source ↗
Figure 13
Figure 13. Figure 13: Speedups with different input query sets and SSDs. [PITH_FULL_IMAGE:figures/full_fig_p011_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Speedup of GRAINS on a low-cost system, i.e., GRN($), over baselines on low-cost and performance-optimized systems [PITH_FULL_IMAGE:figures/full_fig_p011_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Execution time breakdown. Effect of Different GRAINS Optimizations [PITH_FULL_IMAGE:figures/full_fig_p012_15.png] view at source ↗
Figure 17
Figure 17. Figure 17: Speedups with different numbers of SSDs. [PITH_FULL_IMAGE:figures/full_fig_p012_17.png] view at source ↗
Figure 16
Figure 16. Figure 16: Ablation tests of different GRAINS optimizations. [PITH_FULL_IMAGE:figures/full_fig_p012_16.png] view at source ↗
Figure 19
Figure 19. Figure 19: Speedups for read mapping. Alignment-Based Mapping. We integrate GRN, FG, and MG with SeGraM [231] to perform alignment on the candidate graph regions identified by each tool (we use the throughput of alignment operations reported by SeGraM [231]) [PITH_FULL_IMAGE:figures/full_fig_p013_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Speedups for read mapping with alignment. [PITH_FULL_IMAGE:figures/full_fig_p013_20.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents GRAINS, the first system for analysis on large-scale genome graphs inside storage devices. Through storage-aware algorithm-architecture co-design, it introduces a new batching and execution flow based on genome-graph features, performs in-flash and in-storage processing to avoid transferring low-reuse pages, and uses a lightweight scheduling technique that repurposes existing SSD structures to exploit flash-die parallelism. It reports 2.7×–47.8× speedup (4.4×–31.6× energy reduction) over state-of-the-art software baselines and 1.5×–17.0× speedup (3.1×–20.7× energy reduction) over a hardware-accelerated baseline.

Significance. If the performance claims are substantiated, the work could meaningfully advance in-storage computing for irregular, low-reuse graph workloads in genomics by showing that existing SSD hardware suffices without new silicon. The co-design explicitly targets the unique access patterns of genome graphs, distinguishing it from prior linear-genome or conventional-graph storage systems.

major comments (2)
  1. [scheduling technique and experimental evaluation] The headline speedups rest on the claim that the lightweight batching and scheduling scheme fully exploits flash-die parallelism for genome-graph accesses without contention. The manuscript provides no independent measurement (e.g., achieved die utilization or contention counters) under the actual workloads, leaving this load-bearing assumption unverified.
  2. [abstract and § on performance evaluation] The abstract and evaluation sections state large speedups and energy reductions but supply no workload details, baseline descriptions, error bars, or methodology for the reported ranges (2.7×–47.8× etc.). This makes it impossible to assess whether the gains are robust or affected by post-hoc choices.
minor comments (1)
  1. The abstract would benefit from naming the specific genome-analysis pipelines (e.g., variant calling or alignment) used to derive the co-design decisions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and commit to revisions that strengthen the manuscript.

read point-by-point responses

  1. Referee: [scheduling technique and experimental evaluation] The headline speedups rest on the claim that the lightweight batching and scheduling scheme fully exploits flash-die parallelism for genome-graph accesses without contention. The manuscript provides no independent measurement (e.g., achieved die utilization or contention counters) under the actual workloads, leaving this load-bearing assumption unverified.

    Authors: We agree that direct measurements of die utilization and contention would provide stronger verification of the scheduling claims. In the revised manuscript, we will add results from our SSD simulator (including utilization percentages and contention counters) for the genome-graph workloads to substantiate that the lightweight scheduler achieves the reported parallelism without significant contention. revision: yes

  2. Referee: [abstract and § on performance evaluation] The abstract and evaluation sections state large speedups and energy reductions but supply no workload details, baseline descriptions, error bars, or methodology for the reported ranges (2.7×–47.8× etc.). This makes it impossible to assess whether the gains are robust or affected by post-hoc choices.

    Authors: We acknowledge the lack of these details. We will expand the evaluation section with full workload descriptions (genome graph datasets and pipelines), baseline implementations, error bars from multiple runs, and explicit methodology for the speedup/energy ranges. The abstract will be updated with concise references to these details while respecting length limits. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on empirical measurements

full rationale

The paper describes a storage-aware co-design for genome graph analysis (GRAINS) and reports speedups from system-level benchmarks against software and hardware baselines. No equations, fitted parameters, or derivation steps appear in the provided text. Performance claims derive from direct experimental evaluation of the proposed batching, in-flash processing, and scheduling techniques rather than from any self-referential definitions, self-citation load-bearing premises, or renamings of known results. The central assumption about scheduling efficacy under genome-graph access patterns is an empirical claim subject to measurement, not a circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be extracted or audited.

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discussion (0)

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