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Record W2119156399 · doi:10.1109/ipdps.2009.5160895

Improving RDMA-based MPI eager protocol for frequently-used buffers

2009· article· en· W2119156399 on OpenAlex
Mohammad Javad Rashti, Ahmad Afsahi

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRemote direct memory accessInfiniBandComputer scienceCommunication sourceProtocol (science)Computer networkLatency (audio)Message passingOperating systemLow latency (capital markets)Parallel computing

Abstract

fetched live from OpenAlex

MPI is the main standard for communication in high-performance clusters. MPI implementations use the eager protocol to transfer small messages. To avoid the cost of memory registration and pre-negotiation, the eager protocol involves a data copy to intermediate buffers at both sender and receiver sides. In this paper, however, we propose that when a user buffer is used frequently in an application, it is more efficient to register the sender buffer and avoid the sender-side data copy. The performance results of our proposed eager protocol on MVAPICH2 over InfiniBand indicate that up to 14% improvement can be achieved in a single medium-size message latency, comparable to a maximum 15% theoretical improvement on our platform. We also show that collective communications such as broadcast can benefit from the new protocol by up to 19%. In addition, the communication time in MPI applications with high buffer reuse is improved using this technique.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.666
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.031
GPT teacher head0.316
Teacher spread0.284 · 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

Quick stats

Citations2
Published2009
Admission routes2
Has abstractyes

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