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Record W2163189799 · doi:10.1109/hpcs.2008.10

Improving Communication Progress and Overlap in MPI Rendezvous Protocol over RDMA-enabled Interconnects

2008· article· en· W2163189799 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

VenueProceedings/Proceedings (International Symposium on High Performance Computing Systems and Applications) · 2008
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsRemote direct memory accessRendezvousComputer scienceInfiniBandPollingMessage passingComputer networkGigabit EthernetLatency (audio)Protocol (science)Low latency (capital markets)EthernetDistributed computingTelecommunications

Abstract

fetched live from OpenAlex

Overlapping computation with communication is a key technique to conceal the effect of communication latency on the performance of parallel applications. MPI is a widely used message passing standard for high performance computing. One of the most important factors in achieving a good level of overlap is the MPI ability to make progress on outstanding communication operations. In this paper, we address some of the communication progress shortcomings in the current polling and RDMA Read based Rendezvous protocol used for transferring large messages in MPI. We then propose a novel speculative Rendezvous protocol that uses RDMA Read and RDMA Write to effectively improve communication progress and consequently the overlap ability. Performance results based on a modified MPICH2 over 10-Gigabit iWARP Ethernet reveal a significant (80-100%) improvement in receiver side overlap and progress ability.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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