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Record W2141244992 · doi:10.1109/lcn.2004.20

An evaluation of the Myrinet/GM2 two-port networks

2005· article· en· W2141244992 on OpenAlexafffund
Reza Zamani, Ying Qian, Ahmad Afsahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMyrinetRemote direct memory accessComputer scienceMessage passingPort (circuit theory)Bandwidth (computing)Parallel computingOverhead (engineering)ReuseMessage Passing InterfaceComputationOperating systemComputer networkProgramming language

Abstract

fetched live from OpenAlex

It is important to systematically assess the features and performance of the new interconnects for high performance clusters. This work presents the performance of the two-port Myrinet networks at the GM2 and MPI layers using a complete set of microbenchmarks. We also present the communication characteristics and the performance of the NAS multi-zone benchmarks and SMG2000 application under the MPI and MPI-OpenMP programming paradigms. We found that the host overhead is very small in our cluster, and the Myrinet is sensitive to the buffer reuse patterns. Our applications achieved a better performance for MPI than the mixed-mode. All the applications studied use only nonblocking communications, thus are able to overlap their communications with the computations. Our experiments show that the two-port communication at the GM and MPI levels (except for the RDMA read, and overlap) outperforms the one-port communication for the bandwidth. However, this did not translate into a considerable improvement at least for our 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.312
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations11
Published2005
Admission routes2
Has abstractyes

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