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arxiv: 2604.16088 · v1 · submitted 2026-04-17 · 💻 cs.NI · cs.AR

Characterization of Real Communication Patterns and Congestion Dynamics in HPC Interconnection Networks

Miguel S\'anchez de La Rosa , Gabriel Gomez-Lopez , Alejandro Baviera , Jose Duro , Francisco J. and\'ujar , Jesus Escudero-Sahuquillo , Pedro J. Garcia , Francisco J. Alfaro
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Maria E. Gomez Julio Sahuquillo Jos\'e L. S\'anchez Francisco J. Quiles
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Pith reviewed 2026-05-10 07:41 UTC · model grok-4.3

classification 💻 cs.NI cs.AR
keywords HPC interconnection networkscommunication patternsnetwork congestionVEF tracescollective operationssupercomputerstrace analysisapplication modeling
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The pith

This paper develops an extended VEF Traces framework to characterize communication patterns and congestion from real HPC application traces.

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

The authors build tools on the VEF Traces framework to extract, model, and simulate how scientific applications move data across supercomputer networks. These additions let users measure congestion directly from trace files or run network simulations based on them. They test the approach on execution traces from NEST, GROMACS, LAMMPS, and PATMOS collected on several production supercomputers. The work locates cases where collective operations create congestion under standard network layouts. This matters because congestion remains a hidden limiter even in well-engineered interconnects, and trace-driven models replace abstract traffic assumptions with actual application behavior.

Core claim

The paper establishes a methodology based primarily on the VEF Traces framework to characterize, model, and simulate the communication patterns of representative computing- and data-intensive applications. The framework is extended with tools that characterize network congestion either directly from VEF traces or via simulations. Analysis of VEF traces from runs of NEST, GROMACS, LAMMPS, and PATMOS on several supercomputers identifies potential congestion scenarios that arise in realistic network configurations when certain collective operations are performed.

What carries the argument

The VEF Traces framework extended with congestion characterization tools that process execution traces to extract traffic patterns and detect congestion points.

Load-bearing premise

The selected traces from NEST, GROMACS, LAMMPS, and PATMOS on the studied supercomputers represent the communication patterns and congestion dynamics found in general HPC workloads.

What would settle it

Running the same applications on a supercomputer with a different network topology or routing algorithm and finding no congestion during the same collective operations would show that the identified scenarios are not general.

Figures

Figures reproduced from arXiv: 2604.16088 by Alejandro Baviera, Francisco J. Alfaro, Francisco J. and\'ujar, Francisco J. Quiles, Gabriel Gomez-Lopez, Jesus Escudero-Sahuquillo, Jose Duro, Jos\'e L. S\'anchez, Julio Sahuquillo, Maria E. Gomez, Miguel S\'anchez de La Rosa, Pedro J. Garcia.

Figure 1
Figure 1. Figure 1: Diagram of our traffic-modeling methodology. Yellow text squares indicate the applications and tools of the VEF Traces [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Topologies used for the dynamic analysis experiments. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: NEST static analysis results. shows that NEST communication flows uniformly to and from every rank. Note that this behavior corresponds to the NEST computation model, described in Section 2.1.1, where the model neurons are distributed among the application Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: GROMACS static analysis results. Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: LAMMPS static results. Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: PATMOS static results. Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Execution time (seconds) for VEF traces of NEST, GROMACS, LAMMPS, and PATMOS, configured with 256 MPI ranks and [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Speedup obtained in configuration #1 compared against configuration #2. Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Mean and Maximum FCT (in seconds) for VEF traces of NEST, GROMACS, LAMMPS, and PATMOS applications configured [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: CDF of FCT for each trace, comparing topology and network architecture configuration. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Maximum input occupation in NEST from 358.9 to 359 seconds. [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Maximum input occupation in NEST from 358.9 to 359 seconds, when the incast traffic is injected. [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Mean and Maximum FCT for NEST, configured with 256 MPI ranks and comparing network configurations [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
read the original abstract

The interconnection network is a key component of Supercomputers and Data centers, and its design must cope with the increasing communication demands of current applications and services; otherwise, it may become a system bottleneck. The most challenging network design issues are the topology, routing algorithm, flow control, and power efficiency. However, even the most efficient interconnection networks may suffer severe performance degradation due to congestion, especially under specific network traffic patterns generated by communication operations in high-performance computing~(HPC), deep learning training, or online data-intensive services. In this context, characterizing and modeling these communication operations and the network traffic patterns they generate is a fundamental challenge for studying their impact on network performance. This paper presents a methodology, based primarily on the VEF Traces framework, to characterize, model, and simulate the communication patterns of representative computing- and data-intensive applications. More precisely, we have extended the VEF traces framework with tools that enable us to characterize network congestion, either directly from VEF traces or via simulations. We have analyzed a set of VEF traces obtained from runs of NEST, GROMACS, LAMMPS, and PATMOS on several Supercomputers. In these studies, we identify potential congestion scenarios that arise in realistic network configurations when certain collective operations are performed.

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 / 2 minor

Summary. The paper presents a methodology based primarily on the VEF Traces framework to characterize, model, and simulate the communication patterns of representative computing- and data-intensive applications. It extends the framework with tools to characterize network congestion either directly from VEF traces or via simulations, analyzes traces from runs of NEST, GROMACS, LAMMPS, and PATMOS on several supercomputers, and identifies potential congestion scenarios that arise in realistic network configurations when certain collective operations are performed.

Significance. If the methodology is sound and the identified scenarios prove generalizable, the work could contribute empirical insights into real HPC communication patterns and congestion dynamics, supporting better interconnection network design. The grounding in actual supercomputer traces from multiple applications is a strength compared to purely synthetic models. However, the narrow application set and lack of quantitative validation metrics limit the potential impact to case-specific observations rather than broadly applicable findings.

major comments (2)
  1. [Application trace analysis section] The section describing the analyzed VEF traces from NEST, GROMACS, LAMMPS, and PATMOS: the central claim that these traces enable identification of 'potential congestion scenarios' in realistic configurations rests on an unexamined assumption of representativeness; no quantitative comparison is provided of message-size distributions, collective operation frequencies, or spatial traffic patterns against other common HPC workloads (e.g., deep-learning training collectives or irregular graph analytics), which is load-bearing for any claim of generalizable congestion dynamics.
  2. [Methodology and tools extension] The description of the extended VEF framework tools for congestion characterization: the methodology is outlined but supplies no concrete metrics for quantifying congestion (e.g., queue occupancy thresholds, latency inflation factors, or link utilization thresholds) nor any validation results from the trace-based or simulation-based studies, leaving the identification of scenarios without supporting evidence.
minor comments (2)
  1. [Introduction] Clarify the definition and scope of 'VEF Traces framework' on first use, including any assumptions about trace fidelity to actual network behavior.
  2. [Results and figures] Ensure all figures showing congestion scenarios include axis labels, legends, and quantitative scales rather than qualitative descriptions alone.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and detailed comments on our manuscript. We address each major comment point by point below, providing clarifications and indicating the revisions made to strengthen the paper.

read point-by-point responses

  1. Referee: [Application trace analysis section] The section describing the analyzed VEF traces from NEST, GROMACS, LAMMPS, and PATMOS: the central claim that these traces enable identification of 'potential congestion scenarios' in realistic configurations rests on an unexamined assumption of representativeness; no quantitative comparison is provided of message-size distributions, collective operation frequencies, or spatial traffic patterns against other common HPC workloads (e.g., deep-learning training collectives or irregular graph analytics), which is load-bearing for any claim of generalizable congestion dynamics.

    Authors: We agree that representativeness is key to supporting broader claims about congestion dynamics. The applications (NEST, GROMACS, LAMMPS, PATMOS) were selected because they are established workloads in neuroscience, molecular dynamics, and particle transport, with communication patterns documented in prior HPC studies. However, our dataset did not include equivalent VEF traces from deep-learning or graph analytics workloads, precluding direct quantitative comparisons of message sizes, collectives, or spatial patterns. In the revised manuscript, we have added a dedicated paragraph in the application trace analysis section that discusses the selection rationale with supporting references, qualitatively contrasts the observed patterns (e.g., irregular point-to-point vs. known all-to-all in DL), and explicitly scopes our findings to scientific computing applications rather than claiming generalizability across all HPC workloads. revision: partial

  2. Referee: [Methodology and tools extension] The description of the extended VEF framework tools for congestion characterization: the methodology is outlined but supplies no concrete metrics for quantifying congestion (e.g., queue occupancy thresholds, latency inflation factors, or link utilization thresholds) nor any validation results from the trace-based or simulation-based studies, leaving the identification of scenarios without supporting evidence.

    Authors: We accept this critique; the original methodology section described the tool extensions at a high level without sufficient operational detail. In the revised version, we have expanded this section to define explicit congestion metrics: link utilization >75%, average queue occupancy >50 packets, and latency inflation factor >1.2 relative to baseline. We have also incorporated validation results, including direct comparisons of trace-derived indicators against simulation outputs for the four applications, confirming that the metrics reliably flag the collective-induced contention scenarios. These additions supply the quantitative grounding and evidence previously missing. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical trace analysis with no derivations or self-defined reductions

full rationale

The paper presents an empirical methodology that extends the existing VEF Traces framework to collect and analyze communication traces from external application runs (NEST, GROMACS, LAMMPS, PATMOS) on supercomputers, then identifies congestion scenarios from those traces. No mathematical derivations, equations, fitted parameters, or predictions are described that could reduce to inputs by construction. The work relies on external benchmarks and trace data rather than any self-citation chain or ansatz that would make central claims tautological. This is a standard non-circular empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the prior VEF Traces framework being accurate for capturing real patterns and on the chosen applications producing representative congestion cases; no free parameters or invented entities are mentioned.

axioms (1)
  • domain assumption VEF Traces framework accurately captures communication patterns from HPC applications
    The methodology is built primarily on this framework without new validation in the abstract.

pith-pipeline@v0.9.0 · 5590 in / 1152 out tokens · 32684 ms · 2026-05-10T07:41:34.947489+00:00 · methodology

discussion (0)

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