{"id":"W2061663604","doi":"10.1007/s11241-012-9158-9","title":"Memory-centric scheduling for multicore hard real-time systems","year":2012,"lang":"en","type":"article","venue":"Real-Time Systems","topic":"Real-Time Systems Scheduling","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Multi-core processor; Bottleneck; Scheduling (production processes); Two-level scheduling; Uniform memory access; Parallel computing; Distributed computing; Schedule; Dynamic priority scheduling; Embedded system; Memory management; Operating system; Semiconductor memory; Engineering","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":[0.0009146821,0.0004926933,0.0004674593,0.0004707398,0.0009654082,0.001065072,0.001414243,0.0003432455,0.002181214],"category_scores_gemma":[0.001960117,0.00033359,0.0002553497,0.0008029738,0.0003446433,0.0008712386,0.0008630884,0.0007149067,0.0002613986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009160378,"about_ca_system_score_gemma":0.002128448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002747298,"about_ca_topic_score_gemma":0.009052997,"domain_scores_codex":[0.9994741,0.0001276922,0.00003605704,0.00007909294,0.0001376977,0.0001453265],"domain_scores_gemma":[0.9990298,0.000256134,0.0001254384,0.0002339081,0.0002294988,0.0001253696],"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":[0.001864232,0.0003926082,0.002667347,0.0004629089,0.0001419147,0.0002454917,0.0003454072,0.6576437,0.04157452,0.03309243,0.01324835,0.248321],"study_design_scores_gemma":[0.00008176418,0.0002375285,0.0007435671,0.00001801932,0.00005019895,0.00007488756,0.00007501494,0.9704769,0.0120109,0.0108872,0.0053266,0.00001750581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3481851,0.00521723,0.6251531,0.0009452576,0.0006996056,0.000194805,0.0002251183,0.001974127,0.01740562],"genre_scores_gemma":[0.9265469,0.0003727366,0.06964372,0.0001240025,0.00009472391,0.00005894872,0.000117213,0.0001079032,0.002933838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002747298,"threshold_uncertainty_score":0.00729686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02522899895287399,"score_gpt":0.2600384088994585,"score_spread":0.2348094099465845,"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."}}