{"id":"W4295786315","doi":"10.1145/3555819.3555857","title":"Efficient Process Arrival Pattern Aware Collective Communication for Deep Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Scalability; Process (computing); Context (archaeology); Distributed computing; Premise; Artificial intelligence; Data science; Machine learning","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":[0.00008759218,0.00006559083,0.00007174061,0.00003156822,0.0004987583,0.000009149354,0.0001154225,0.00001103665,0.00005050042],"category_scores_gemma":[0.00001709647,0.00007083845,0.00002706162,0.000134656,0.000006815247,0.0000200625,0.00005849591,0.0002014968,0.000001714972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009786776,"about_ca_system_score_gemma":0.000007335294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.693091e-7,"about_ca_topic_score_gemma":0.00000286473,"domain_scores_codex":[0.9995792,0.00002866559,0.000091792,0.00009394577,0.00007409404,0.0001323133],"domain_scores_gemma":[0.9997225,0.0001199214,0.00002154652,0.00008816265,0.00002660492,0.00002123071],"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":[0.000007518289,0.000008719629,0.0001134549,0.00002674304,0.000005589604,4.584709e-7,0.001041893,0.9898176,0.000622108,0.00001484705,0.000016266,0.00832487],"study_design_scores_gemma":[0.0002340318,0.00005533258,0.0001208811,0.000005892269,0.000004037126,0.000004697148,0.001806028,0.991164,0.005865143,0.0001291193,0.0005068946,0.0001039716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6487659,0.00008787525,0.3492895,0.00002582623,0.00009599679,0.0002572612,0.000002479165,0.0003309978,0.001144215],"genre_scores_gemma":[0.9992217,0.000001243176,0.0003425849,0.00003693459,0.00001956512,0.0001267531,0.00001243493,0.00001876856,0.0002200625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3504558,"threshold_uncertainty_score":0.3836096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0142351561767134,"score_gpt":0.2536917634747152,"score_spread":0.2394566072980018,"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."}}