{"id":"W2893723147","doi":"10.1002/cpe.4862","title":"Communication‐aware message matching in MPI","year":2018,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Message queue; Message passing; Message Passing Interface; Speedup; Asynchronous communication; Scalability; Parallel computing; Queue; Matching (statistics); Distributed computing; Computer network; Operating system","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":[0.002054591,0.0006971389,0.0005901654,0.001053758,0.001337282,0.001636315,0.002833599,0.0007948959,0.003210831],"category_scores_gemma":[0.005739491,0.0004703224,0.0005753897,0.001315275,0.0007526998,0.00252984,0.002364431,0.001308087,0.000987266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095688,"about_ca_system_score_gemma":0.001599333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002224796,"about_ca_topic_score_gemma":0.001903215,"domain_scores_codex":[0.9969554,0.0007028257,0.0002893261,0.0003368964,0.001311077,0.0004044974],"domain_scores_gemma":[0.9966462,0.000794158,0.0002662322,0.001284174,0.0008428117,0.0001663722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001598681,0.0004889513,0.008973264,0.0007458672,0.000229632,0.0005034702,0.001022059,0.1667146,0.1028513,0.1272977,0.03622032,0.5533541],"study_design_scores_gemma":[0.0001547125,0.0002708308,0.001502446,0.00004588062,0.0001128679,0.0001979535,0.0001322457,0.7501735,0.1215912,0.05367168,0.07202291,0.0001237282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04919304,0.0005483663,0.9154059,0.0006968683,0.0002189121,0.0002336819,0.0002217113,0.02298957,0.01049186],"genre_scores_gemma":[0.6125526,0.0003094657,0.3736954,0.0004141472,0.000162194,0.0004076887,0.0008444092,0.002119917,0.009494143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003210831,"threshold_uncertainty_score":0.01086581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462688115613935,"score_gpt":0.3474426032332346,"score_spread":0.3228157220770952,"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."}}