{"id":"W2520360696","doi":"10.1109/hpcsim.2016.7568398","title":"MultiObjective GPU design space exploration optimization","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; University of Victoria","keywords":"Pareto principle; Computer science; Multi-objective optimization; Range (aeronautics); Mathematical optimization; Pareto optimal; Power (physics); Space (punctuation); Design space exploration; Optimal design; General-purpose computing on graphics processing units; Software; Graphics; Mathematics; Engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005848717,0.0008423698,0.0006733083,0.0006068812,0.0003091018,0.0007444797,0.0006249656,0.000948324,0.001694111],"category_scores_gemma":[0.001539667,0.0003884512,0.0006686851,0.000446898,0.000494955,0.0005046976,0.0006054447,0.0005605147,0.0002004379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009424624,"about_ca_system_score_gemma":0.0008083073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002744459,"about_ca_topic_score_gemma":0.002689599,"domain_scores_codex":[0.999769,0.00008989999,0.000006809601,0.00003548511,0.00006441353,0.00003446151],"domain_scores_gemma":[0.9996006,0.0002498101,0.00004182199,0.00003123322,0.0000606578,0.00001591308],"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":[0.00000960611,0.000009912269,0.0001959546,0.00001115948,0.000007198803,0.0000103187,0.000006369197,0.9958135,0.0004591803,0.0005226785,0.00007817063,0.002875955],"study_design_scores_gemma":[0.000003195597,0.00001309278,0.00008495589,0.000002523053,0.000002215451,0.000004507661,0.000005375819,0.9989558,0.0002494282,0.0005497959,0.0001275727,0.000001363331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3273411,0.0005907947,0.6511485,0.0004168786,0.00003666732,0.0001485562,0.0002621122,0.0004872076,0.01956806],"genre_scores_gemma":[0.87723,0.000191711,0.1174924,0.00008063666,0.00000880127,0.000311896,0.000178249,0.0001025877,0.00440386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002744459,"threshold_uncertainty_score":0.006838143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066416367756733,"score_gpt":0.2600107880712285,"score_spread":0.2293466243936611,"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."}}