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Record W2112357522 · doi:10.1109/clustr.2002.1137731

Supporting MPI collective communication on network processors

2003· article· en· W2112357522 on OpenAlexaff
Q. Zhang, Chamath Keppitiyagama, Alan Wagner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMyrinetComputer scienceMessage Passing InterfaceMessage passingLatency (audio)InfiniBandOperating systemParallel computingTelecommunications

Abstract

fetched live from OpenAlex

We present work that extends our previous Myrinet port for LAM/MPI, MPI-NP, with collective communication primitives on the NIC. This work is another step in our experiment of making the NIC MPI aware. We believe that an MPI aware control program on the NIC can deliver a richer set of performance enhancements, not just restricted to better bandwidth/latency, to MPI applications. MPI collective communication involves considerable interactions between the communication subsystems of the nodes that are not of any direct interest to the application. By migrating these talkative components to the Myrinet network interface card we allow this dialog between the nodes to happen with minimum latency. We explore the advantage of supporting several MPI collective communication routines on the NIC. These include MPI /spl I.bar/Bcast (), MPI/spl I.bar/Barrier and MPI/spl I.bar/Comm/spl I.bar/Create ().

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2003
Admission routes1
Has abstractyes

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