{"id":"W3157856659","doi":"10.48550/arxiv.2104.12350","title":"A PGAS Communication Library for Heterogeneous Clusters","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Partitioned global address space; Computer science; Programming language; Programming paradigm","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001001938,0.001270218,0.0007375291,0.00113097,0.001169801,0.001764873,0.00357611,0.0009891278,0.03165429],"category_scores_gemma":[0.002525371,0.0009414519,0.001226813,0.001651396,0.0005298326,0.002422684,0.002655047,0.002195264,0.0149498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000993113,"about_ca_system_score_gemma":0.002042882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002696811,"about_ca_topic_score_gemma":0.003636595,"domain_scores_codex":[0.9990001,0.0001676079,0.00008482089,0.0001217098,0.0004990691,0.000126722],"domain_scores_gemma":[0.9988744,0.0002848922,0.00008866675,0.0003687654,0.0002904212,0.00009284551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000817043,0.000280062,0.002042659,0.001253144,0.0002208541,0.0007684857,0.0006911627,0.1360779,0.02833394,0.1333421,0.2505144,0.4456582],"study_design_scores_gemma":[0.0002469266,0.0001379446,0.0005640286,0.0001292551,0.00006929658,0.0004845935,0.0001038358,0.33859,0.0342937,0.03703167,0.5882012,0.0001476238],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.00350962,0.0002931763,0.8837371,0.0002099531,0.000123211,0.0001743621,0.001568627,0.09107499,0.01930881],"genre_scores_gemma":[0.1311241,0.001529883,0.7359856,0.0007316637,0.0002234451,0.00168299,0.01183328,0.04405779,0.07283134],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03165429,"threshold_uncertainty_score":0.1058941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06169652555526881,"score_gpt":0.1987568342662123,"score_spread":0.1370603087109435,"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."}}