{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003705347,0.00010546,0.00011485,0.00009444348,0.0003191289,0.0002616895,0.0003028259,0.00004519691,0.000004741671],"category_scores_gemma":[0.0001294908,0.0001074977,0.00001102553,0.0003196003,0.0001608317,0.001596239,0.000252781,0.0001387623,0.000006095075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001229877,"about_ca_system_score_gemma":0.00003629503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006508904,"about_ca_topic_score_gemma":0.000005149377,"domain_scores_codex":[0.9989963,0.0001779663,0.0002409985,0.0003077388,0.0001286402,0.0001483167],"domain_scores_gemma":[0.9990749,0.0003495932,0.0001423941,0.0002215029,0.0001517815,0.00005987295],"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":[0.00004706687,0.0002960853,0.002913997,0.0000608539,0.00002061726,0.00002477114,0.1727179,0.001180505,0.0001926939,0.2507152,0.001254775,0.5705755],"study_design_scores_gemma":[0.001000588,0.0002943977,0.005461804,0.0002574471,0.00001009509,0.0001868929,0.006606859,0.9383686,0.0007919394,0.03435651,0.01198797,0.0006769445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04903526,0.001038806,0.9448367,0.001213474,0.00010859,0.0001046985,4.295172e-7,0.0001679958,0.003494048],"genre_scores_gemma":[0.9124656,0.0005128775,0.08628633,0.0006721709,0.00001849323,0.00001646137,0.000002450822,0.000003548192,0.0000221],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.937188,"threshold_uncertainty_score":0.438363,"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."}}