{"id":"W3118725232","doi":"10.1109/tpds.2020.3048373","title":"Partitioning-Based Scheduling of OpenMP Task Systems With Tied Tasks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Processor scheduling; Parallel computing; Scheduling (production processes); Distributed computing; Task (project management); Task analysis; Operating system; Schedule","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":[],"consensus_categories":[],"category_scores_codex":[0.0002033528,0.0002344266,0.0004064213,0.0000991853,0.0002586635,0.0002491478,0.0003812698,0.0001123299,0.000002683437],"category_scores_gemma":[0.00000788295,0.0002000188,0.00007018584,0.0005606947,0.00007817819,0.0002411073,0.000004501292,0.0001997403,0.000009335372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003178678,"about_ca_system_score_gemma":0.0001067635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001087587,"about_ca_topic_score_gemma":0.000002998592,"domain_scores_codex":[0.9982949,0.0001604817,0.0004876715,0.0004622189,0.0003320113,0.0002627556],"domain_scores_gemma":[0.9988652,0.000107408,0.0002443821,0.0003634327,0.0001937796,0.0002257926],"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":[0.00006837818,0.00008209726,0.00006667381,0.0001240413,0.0000457243,0.00000744609,0.0001281176,0.9979714,0.000141837,0.0009944575,0.0002464502,0.0001233776],"study_design_scores_gemma":[0.0009354631,0.0004140486,0.0000325883,0.0002138754,0.00002611759,0.00001730952,0.0001029238,0.9969391,0.000603102,0.00001166405,0.0004494979,0.0002542768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002072709,0.0002687104,0.9957972,0.0004451561,0.0001991022,0.0004563772,0.0001262927,0.0004898577,0.0001446511],"genre_scores_gemma":[0.9852994,0.00001990769,0.01437099,0.0001083625,0.00002839324,0.00009742515,0.00003200469,0.00001472821,0.00002880338],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9832267,"threshold_uncertainty_score":0.8156528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02394696952466485,"score_gpt":0.2349234776414482,"score_spread":0.2109765081167834,"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."}}