{"id":"W4414197066","doi":"10.1109/lcn65610.2025.11146375","title":"Performance Analysis of Communication Scheduling Schemes for Distributed Deep Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Deep learning; Popularity; Fair-share scheduling; Dynamic priority scheduling; Two-level scheduling; Round-robin scheduling; Telecommunications network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003828164,0.0008157681,0.0007937071,0.0006996925,0.0009963532,0.0008440917,0.001523502,0.0007100533,0.002349998],"category_scores_gemma":[0.01116761,0.0002947165,0.0002911937,0.0007837666,0.0008698956,0.001377476,0.00114336,0.001031608,0.0003351631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002800509,"about_ca_system_score_gemma":0.003249864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006639043,"about_ca_topic_score_gemma":0.006543145,"domain_scores_codex":[0.9982001,0.0004631742,0.00007389078,0.000336659,0.0003871279,0.0005391238],"domain_scores_gemma":[0.992975,0.003822524,0.0005691344,0.0007726945,0.001295388,0.0005653779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009588074,0.0002976136,0.003043485,0.0001037226,0.00004039437,0.00003736535,0.00007671333,0.928739,0.003944898,0.005959088,0.002995395,0.05380354],"study_design_scores_gemma":[0.00001612522,0.00009662574,0.0002676123,0.000002949172,0.000005026499,0.000007590272,0.00002951768,0.9969375,0.001225419,0.001276079,0.0001319026,0.000003663254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.790415,0.001378572,0.1947004,0.001309847,0.0002762107,0.0001709999,0.0002522883,0.002043696,0.009452966],"genre_scores_gemma":[0.9880742,0.00009863549,0.01091484,0.00006956346,0.00002020264,0.00004519567,0.00009174587,0.00004380995,0.000641688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006639043,"threshold_uncertainty_score":0.02031922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01319749737929269,"score_gpt":0.2737004708763754,"score_spread":0.2605029734970827,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}