{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000144642,0.0002252165,0.0002596708,0.000177386,0.0002067577,0.0003464791,0.00230286,0.0002458963,0.00001056773],"category_scores_gemma":[0.00002168154,0.0002850398,0.0002384696,0.0003474685,0.0000576717,0.0004526297,0.003140262,0.0003009622,0.000005856163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007580155,"about_ca_system_score_gemma":0.0001929218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002030666,"about_ca_topic_score_gemma":0.000005654167,"domain_scores_codex":[0.9985474,0.000197329,0.0001933291,0.0007722112,0.00005377136,0.0002359643],"domain_scores_gemma":[0.9977509,0.0001461401,0.0002385173,0.001632649,0.0001299129,0.0001019038],"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":[0.00001676501,0.00007905267,0.0001524486,0.00007706094,0.000071585,0.0000503698,0.0003167896,0.9637936,0.000004020562,0.03173743,0.002747869,0.0009530068],"study_design_scores_gemma":[0.0002428267,0.00003342027,0.00002430506,0.0001103155,0.00002386969,0.000006455678,0.00002092434,0.9804807,0.0004645057,0.01667619,0.001602073,0.0003144327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01497058,0.0002714208,0.9813687,0.000308148,0.0002131934,0.0003577022,0.00001028569,0.001073059,0.001426899],"genre_scores_gemma":[0.8269961,0.0005128847,0.1708511,0.000346001,0.00002937872,0.000003781786,0.0001336399,0.00002270068,0.001104438],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8120255,"threshold_uncertainty_score":0.9999602,"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."}}