{"id":"W2951091066","doi":"10.1145/3307650.3322212","title":"Opportunistic computing in GPU architectures","year":2019,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Defense Advanced Research Projects Agency; Advanced Micro Devices; National Science Foundation","keywords":"Computer science; Overhead (engineering); Parallel computing; Cache; General-purpose computing on graphics processing units; Multi-core processor; Reduction (mathematics); Computation; Von Neumann architecture; CUDA; Embedded system; Graphics; Operating system; Algorithm","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":[0.0001890276,0.00008150598,0.0001140277,0.0001525084,0.00002885742,0.00007265183,0.000541053,0.00003260135,0.00002708454],"category_scores_gemma":[0.00002038462,0.00007223115,0.00002686059,0.0002702597,0.00001092957,0.00005418599,0.0002136796,0.0001059405,0.00006659359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001721775,"about_ca_system_score_gemma":0.00003492483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004382071,"about_ca_topic_score_gemma":0.000005776428,"domain_scores_codex":[0.9992192,0.00004557187,0.0001715113,0.0002544631,0.0001196915,0.0001896064],"domain_scores_gemma":[0.9994687,0.00008906394,0.00004397017,0.0003326648,0.00002244053,0.00004311284],"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":[0.000008285752,0.0001718458,0.08749001,0.00005094961,0.00001541634,0.00005615721,0.001370229,0.2236504,0.0006364806,0.5121151,0.003329797,0.1711054],"study_design_scores_gemma":[0.0001643335,0.00003841689,0.006348919,0.00002349828,3.694925e-7,0.00001138953,0.000007972416,0.9879581,0.0003279638,0.004242301,0.0007284036,0.0001482917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0396107,0.00001476641,0.9165817,0.0003044379,0.0001099592,0.0001059369,1.246108e-7,0.0005202898,0.04275203],"genre_scores_gemma":[0.7683409,0.000001915612,0.2306301,0.0005339038,0.00001133337,8.001659e-7,8.789764e-7,0.000003602628,0.0004765095],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7643077,"threshold_uncertainty_score":0.2945501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655415346989578,"score_gpt":0.2604403385925569,"score_spread":0.2438861851226612,"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."}}