{"id":"W2161884835","doi":"10.1109/ispa.2008.128","title":"Energy Optimal Scheduling on Multiprocessors with Migration","year":2008,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Multiprocessor scheduling; Multiprocessing; Parallel computing; Scheduling (production processes); Schedule; Time complexity; Job shop scheduling; Regular polygon; Mathematical optimization; Algorithm; Flow shop scheduling; Mathematics; Operating system","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":[0.0006985136,0.0009911645,0.001059622,0.0004376484,0.0007278204,0.001007949,0.0008645572,0.0005704342,0.001573448],"category_scores_gemma":[0.001987666,0.0004890852,0.0006260701,0.001019246,0.0007910402,0.001363763,0.0009304087,0.0008080347,0.0002951079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165958,"about_ca_system_score_gemma":0.0009459624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002990465,"about_ca_topic_score_gemma":0.002817481,"domain_scores_codex":[0.9993784,0.000216256,0.00003241846,0.00009016248,0.0001134251,0.0001692787],"domain_scores_gemma":[0.9994167,0.0003099066,0.00009823703,0.0000736002,0.0000537302,0.00004779028],"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":[0.0001413813,0.00005279391,0.0002271265,0.00008856236,0.00001727791,0.000103206,0.00004619973,0.9494862,0.005086553,0.02274282,0.001252336,0.02075547],"study_design_scores_gemma":[0.00002926188,0.00004970646,0.0001609382,0.000006471933,0.000006697559,0.00002794093,0.00002339565,0.9669746,0.001640764,0.02990715,0.001165051,0.000008093481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1724575,0.001605358,0.8139495,0.0009809731,0.0001407099,0.0001212048,0.0001506131,0.0007160538,0.009878119],"genre_scores_gemma":[0.8244242,0.0007818606,0.1698051,0.0001633751,0.00007114995,0.0001694954,0.0001702403,0.0002216087,0.004193002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002990465,"threshold_uncertainty_score":0.008459628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01718374779244544,"score_gpt":0.2317946283652094,"score_spread":0.214610880572764,"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."}}