{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007400996,0.0007073282,0.0006336404,0.0004455508,0.0008175198,0.0006417597,0.001509598,0.000568616,0.001265238],"category_scores_gemma":[0.002899083,0.0004113088,0.0003437367,0.0004810565,0.0004907954,0.0008425685,0.001187932,0.0007343094,0.000235412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005288634,"about_ca_system_score_gemma":0.001013923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002046798,"about_ca_topic_score_gemma":0.002806685,"domain_scores_codex":[0.9993399,0.0001657315,0.00005776744,0.0001288431,0.0001632875,0.0001444165],"domain_scores_gemma":[0.9985794,0.0006147875,0.000179456,0.0002381513,0.0002404349,0.0001477018],"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":[0.0004857528,0.0001879236,0.001895794,0.000206039,0.000048192,0.0003416179,0.0002973212,0.8658982,0.02204852,0.01131544,0.002659309,0.09461596],"study_design_scores_gemma":[0.00003231441,0.00007958086,0.0002537995,0.000006373506,0.000007189123,0.00004495941,0.00004441567,0.9905342,0.004047222,0.004010881,0.0009306764,0.000008331119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1864626,0.0004094949,0.8078275,0.0001541875,0.0001028076,0.0001845099,0.0001243052,0.0008385176,0.003896077],"genre_scores_gemma":[0.680581,0.0001224626,0.3172717,0.00006699923,0.0000306993,0.0001677394,0.0003055704,0.0001399636,0.001313888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002046798,"threshold_uncertainty_score":0.004232585,"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."}}