{"id":"W2320450249","doi":"10.2316/p.2010.676-037","title":"Low-Complex Task Scheduling Algorithms for Hierarchical Embedded Many-Core Architectures and Dynamic Applications","year":2010,"lang":"en","type":"article","venue":"Parallel and Distributed Computing and Networks","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Parallel computing; Processor scheduling; Dynamic priority scheduling; Many core; Scheduling (production processes); Distributed computing; Multi-core processor; Fair-share scheduling; Task (project management); Algorithm; Computer network; Mathematical optimization; Resource (disambiguation)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003682824,0.000283482,0.0003257248,0.0000987507,0.0007894644,0.0003485487,0.0003850958,0.0001858655,9.866123e-7],"category_scores_gemma":[0.00004277186,0.0002630692,0.00006094234,0.0002555415,0.0002154836,0.00006503156,0.0003979916,0.0004945494,4.569823e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008769684,"about_ca_system_score_gemma":0.00002644638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007634304,"about_ca_topic_score_gemma":0.000004305914,"domain_scores_codex":[0.9982827,0.00004744781,0.0003518092,0.0007074583,0.000126945,0.0004836],"domain_scores_gemma":[0.9987271,0.0004236235,0.0001488611,0.0003477508,0.00009145032,0.0002611448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007363057,0.0001994268,0.001290864,0.0001617478,0.0001010095,0.000008057174,0.0006251482,0.3938214,0.0006573056,0.05681634,0.0006092904,0.5456358],"study_design_scores_gemma":[0.0006013003,0.00006921264,0.003265689,0.00003592694,0.0000145069,0.00007753453,0.00001446621,0.9819328,0.0000115011,0.01299983,0.0006544339,0.0003227855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04338349,0.0005721395,0.9544961,0.0004280384,0.0001002557,0.0004516088,0.0000244659,0.0004982049,0.00004562752],"genre_scores_gemma":[0.6239935,0.00007399805,0.3754853,0.0001528577,0.0001261604,0.00003532456,0.0001129177,0.00001132321,0.000008674465],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5881114,"threshold_uncertainty_score":0.9999822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415919459102929,"score_gpt":0.2812737539031707,"score_spread":0.2671145593121414,"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."}}