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Record W4416004483 · doi:10.1145/3731599.3767460

SmartNIC Data Exchange Framework

2025· article· W4416004483 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicNetwork Packet Processing and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsLeverage (statistics)Data exchangeVariety (cybernetics)Field (mathematics)Context (archaeology)Big dataInterface (matter)

Abstract

fetched live from OpenAlex

As the field of HPC grows ever larger, now more than ever, it is important to adapt to the rapidly evolving hardware landscape, leveraging the cutting edge and advancing beyond the limits of what is considered conventional computing. Smart Network Interface Cards (SmartNICs) are one such emerging technology that have the potential to overhaul classical computing paradigms. This paper will provide an overview of a novel data exchange framework which leverages SmartNICs to gather arbitrary host data from HPC systems and exchange it via three different methods with minimal system overhead. We discuss the latency with which the framework operates, along with the ways in which its varying configurations affect performance. Finally, we provide some context as to how the field of HPC will benefit from the introduction of SmartNICs, especially as they leverage the presented framework for a variety of future applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0040.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.324
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it