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arxiv: 2605.27691 · v1 · pith:L7ALIT55new · submitted 2026-05-26 · 💻 cs.DC

SOLANET: Distributed Neighbor Graph Construction on GPU-Accelerated Systems

Keita Iwabuchi , Trevor Steil , Benjamin W. Priest , Grace J. Li , Geoffrey Sanders , Roger Pearce This is my paper

Pith reviewed 2026-06-29 15:11 UTC · model grok-4.3

classification 💻 cs.DC
keywords neighbor graph constructiondistributed GPU systemsapproximate nearest neighborMPI one-sided operationsAMD APUdata partitioninglock-free algorithmscaling performance
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The pith

SOLANET builds large neighbor graphs by first creating local GPU graphs then refining them with remote ANN searches via MPI one-sided operations.

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

SOLANET addresses the challenge of building neighbor graphs for billions of data points across distributed GPU systems, where irregular computation has limited prior scaling efforts. The method partitions data to build approximate local graphs on each GPU and then improves those graphs by pulling remote data from other GPUs for additional searches. This matters because neighbor graphs underpin many analytics and AI tasks, and effective distributed construction would allow much larger datasets to be processed without single-node limits. The system also supplies a lock-free single-GPU algorithm optimized for AMD hardware and reports measured speedups at scale.

Core claim

SOLANET first constructs local graphs on each GPU after data partitioning and then refines them via approximate nearest neighbor searches over remote graphs pulled from other GPUs using MPI one-sided operations. It also provides a lock-free single-GPU neighbor graph construction algorithm for AMD GPUs. This yields better performance than prior single-GPU methods and demonstrates 11X speedup from 32 to 512 APUs for 1 billion data points and 6.9x speedup from 64 to 512 APUs for 2 billion points.

What carries the argument

The two-phase construction where local approximate graphs are built on partitioned data and then refined through remote ANN searches accessed with MPI one-sided operations.

If this is right

  • Neighbor graphs for 1-2 billion points can be built with near-linear scaling up to 512 APUs.
  • Single-GPU construction on AMD MI300A APUs runs faster than existing GPU-based approximate methods.
  • The lock-free single-GPU algorithm removes synchronization overhead during local graph building.
  • The same refinement approach can be applied to other distributed approximate graph tasks that start from partitioned local results.

Where Pith is reading between the lines

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

  • Further tuning of the remote search frequency or data pull size could extend scaling beyond 512 APUs on slower interconnects.
  • The partitioning-plus-refinement pattern might transfer to other irregular distributed algorithms that mix local computation with selective remote access.
  • Embedding this construction inside larger distributed analytics pipelines could reduce end-to-end time for graph-based learning workloads.

Load-bearing premise

That the costs of pulling remote graphs and running ANN searches on them stay low enough that the refinement step produces net speedups rather than being dominated by communication or load imbalance.

What would settle it

A runtime measurement on 512 APUs for 1 billion points that is not at least 11 times faster than the runtime on 32 APUs, or data showing that remote access time exceeds local computation time across the workload.

Figures

Figures reproduced from arXiv: 2605.27691 by Benjamin W. Priest, Geoffrey Sanders, Grace J. Li, Keita Iwabuchi, Roger Pearce, Trevor Steil.

Figure 1
Figure 1. Figure 1: Overview of nearest neighbor graph construction. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: ANN search-based distributed kNNG refinement with 8 MPI ranks. Assume that at least two GPUs are required to store the dataset. Only the activity of rank 0 is shown for simplicity. All ranks execute the same procedure [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Single APU kNNG construction recall-time trade￾off across six datasets. Each point corresponds to a specific combination of parameters. less than 1.e-04). We hypothesize that hipVS’s low recall was due to floating-point precision issues or design choices that are not well-suited for handling such small distance values. D. Distributed kNNG Construction Performance We evaluate SOLANET on datasets with at lea… view at source ↗
Figure 4
Figure 4. Figure 4: Strong scaling study results by SOLANET on the Tuolumne cluster (4 AMD MI300A APUs per node). NEO-DNND [29] [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance breakdown of the distributed neighbor [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
read the original abstract

Neighbor graphs capture relationships among data points and are widely used in data analytics and AI workloads. Many studies have explored approximate construction methods for single-node systems, including GPUs. However, extending this to distributed systems for larger data and further acceleration remains challenging due to irregular computation patterns. We present SOLANET, a GPU-accelerated distributed neighbor graph construction toolkit. SOLANET first constructs local graphs on each GPU after data partitioning and then refines them via approximate nearest neighbor (ANN) searches over remote graphs pulled from other GPUs using MPI one-sided operations. SOLANET also provides a lock-free single-GPU neighbor graph construction algorithm for AMD GPUs. Our single-GPU implementation outperforms a state-of-the-art GPU-based approximate neighbor graph construction implementation across multiple datasets on a single MI300A APU. Furthermore, SOLANET demonstrates 11X speedup from 32 to 512 APUs for 1 billion data points and 6.9x speedup from 64 to 512 APUs for 2 billion points.

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 / 0 minor

Summary. The paper presents SOLANET, a GPU-accelerated distributed toolkit for neighbor graph construction. Data is partitioned across APUs; each builds a local graph, which is then refined by approximate nearest-neighbor searches over remote shards fetched via MPI one-sided operations. A lock-free single-GPU construction algorithm for AMD GPUs is also described. The central empirical claims are single-GPU outperformance versus prior GPU baselines on MI300A APUs and strong-scaling speedups of 11× (32→512 APUs, 1 B points) and 6.9× (64→512 APUs, 2 B points).

Significance. If the reported speedups are reproducible with full experimental disclosure, the work would address a practically important scaling problem for billion-scale neighbor graphs in analytics and AI pipelines. The local-plus-remote-refinement strategy is a plausible way to exploit GPU compute while managing distributed memory, but the absence of any supporting methodology, timings, or balance metrics leaves the net-gain claim unverified.

major comments (2)
  1. [Abstract] Abstract: the headline speedups (11× and 6.9×) are stated without any description of datasets, baseline implementations, measurement methodology, error bars, per-phase timings, communication volumes, or load-balance statistics. Because these empirical results constitute the central claim, the manuscript cannot be evaluated for soundness.
  2. [Abstract] Abstract (distributed-construction paragraph): the assumption that partitioning plus local-graph construction plus remote ANN refinement via MPI one-sided pulls produces net compute gain (rather than being dominated by communication or imbalance) is asserted but unsupported by any quantitative evidence. At 512 APUs the per-APU data volume decreases while the number of remote probes increases; without communication or idle-time breakdowns the strong-scaling claim rests on an untested premise.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. The concerns focus on the level of detail in the abstract regarding experimental methodology and supporting evidence for the distributed scaling claims. We will revise the abstract and, where needed, the experimental presentation to address these points directly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline speedups (11× and 6.9×) are stated without any description of datasets, baseline implementations, measurement methodology, error bars, per-phase timings, communication volumes, or load-balance statistics. Because these empirical results constitute the central claim, the manuscript cannot be evaluated for soundness.

    Authors: We agree the abstract is too terse for the central claims. In the revised manuscript we will expand the abstract to name the datasets (synthetic uniform distributions and real-world corpora at 1 B and 2 B points), identify the single-GPU baseline, state that all timings are end-to-end wall-clock on MI300A APUs, and note that error bars, per-phase breakdowns, communication volumes, and load-balance statistics appear in the Experiments section with the associated figures. This change will make the abstract self-contained enough for initial evaluation while preserving its length constraints. revision: yes

  2. Referee: [Abstract] Abstract (distributed-construction paragraph): the assumption that partitioning plus local-graph construction plus remote ANN refinement via MPI one-sided pulls produces net compute gain (rather than being dominated by communication or imbalance) is asserted but unsupported by any quantitative evidence. At 512 APUs the per-APU data volume decreases while the number of remote probes increases; without communication or idle-time breakdowns the strong-scaling claim rests on an untested premise.

    Authors: The reported speedups are measured end-to-end and therefore already incorporate communication, imbalance, and idle time. Nevertheless, we accept that the abstract does not surface the supporting metrics. We will add one sentence to the abstract summarizing the measured communication-to-compute ratio and load-balance statistics at the 512-APU scale, and we will ensure the Experiments section contains explicit per-phase timing tables and MPI one-sided volume data so that readers can verify the net-gain premise. revision: yes

Circularity Check

0 steps flagged

Empirical scaling results contain no circular derivations or self-referential predictions

full rationale

The paper reports measured wall-clock speedups on specific hardware configurations (11× from 32→512 APUs at 1 B points; 6.9× from 64→512 at 2 B points) after describing a concrete implementation (local graph construction followed by MPI one-sided remote ANN refinement). No equations, fitted parameters, uniqueness theorems, or ansatzes appear in the provided text. All performance claims are direct experimental outcomes rather than derivations that reduce to their own inputs by construction. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an engineering systems paper whose central claims are measured speedups rather than mathematical derivations. No free parameters, axioms, or invented entities are identifiable from the abstract.

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discussion (0)

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