{"id":"W2166345115","doi":"10.1109/icppw.2001.951913","title":"Feedback guided dynamic loop scheduling; A theoretical approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Loop (graph theory); Feedback loop; Workload; Inner loop; Scheduling (production processes); Dynamic priority scheduling; Loop fission; Loop tiling; Convergence (economics); Loop fusion; Control theory (sociology); Schedule; Mathematical optimization; Parallel computing; Mathematics; Artificial intelligence","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":[0.0002392076,0.0001344752,0.0001413513,0.00006832999,0.000109179,0.0001808421,0.0009088268,0.00007995362,0.0001842122],"category_scores_gemma":[0.00004539357,0.0001116181,0.00006536544,0.0003925648,0.00010485,0.0001758123,0.0002514289,0.0001332869,0.0002003263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000230219,"about_ca_system_score_gemma":0.00001324303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002126327,"about_ca_topic_score_gemma":1.051219e-7,"domain_scores_codex":[0.9988387,0.00006708649,0.0002229626,0.0003873604,0.0002106266,0.0002733187],"domain_scores_gemma":[0.9991506,0.00004617076,0.00004767308,0.0005813831,0.00007804685,0.00009616501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.995346e-7,0.0001778181,0.00004124949,0.000006723103,0.00001111488,0.000003570628,0.0001737798,0.009109468,0.0000339755,0.9709433,0.007229622,0.01226858],"study_design_scores_gemma":[0.0001540977,0.00002843396,0.00003691596,0.000005983326,0.00000246538,0.00003092341,0.000005101123,0.9885415,0.000194787,0.01059162,0.0002465955,0.0001616217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008665994,0.0001081641,0.8666847,0.001035204,0.00006096429,0.00009808468,1.703116e-7,0.001186521,0.1299596],"genre_scores_gemma":[0.4253871,0.00001895319,0.5723978,0.0004597819,0.00001338149,0.000005578344,9.72655e-7,0.000006183121,0.001710181],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.979432,"threshold_uncertainty_score":0.4551656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02255770833488799,"score_gpt":0.2585872057233237,"score_spread":0.2360294973884357,"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."}}