{"id":"W2885000039","doi":"10.1109/isca.2018.00025","title":"Scheduling Page Table Walks for Irregular GPU Applications","year":2018,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Australian School of Taxation, University of New South Wales; Society for the Study of School Psychology","keywords":"Computer science; SIMD; Thread (computing); Bottleneck; Scheduling (production processes); Parallel computing; Speedup; Compiler; Multithreading; Operating system; Embedded 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":[0.0003434138,0.0004776617,0.0004000356,0.000391889,0.0006062927,0.000637177,0.0009007032,0.0003311384,0.002499135],"category_scores_gemma":[0.001789904,0.0002925804,0.0002410491,0.0007825792,0.0004816555,0.0007965325,0.0006346137,0.0004837423,0.0005218083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006130566,"about_ca_system_score_gemma":0.001240189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003032861,"about_ca_topic_score_gemma":0.008656568,"domain_scores_codex":[0.9997317,0.00005013397,0.0000237602,0.00004945199,0.00007231289,0.00007266719],"domain_scores_gemma":[0.9989846,0.0004099144,0.0001155081,0.0002045495,0.0001682093,0.0001172441],"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":[0.003131683,0.0007492971,0.02982928,0.0005782045,0.000138456,0.001297197,0.001057149,0.3917439,0.2246268,0.01288768,0.01844081,0.3155195],"study_design_scores_gemma":[0.00007799522,0.000323047,0.003270091,0.000008786864,0.00002277038,0.0001241502,0.0001864151,0.9454631,0.04142781,0.003556024,0.00551773,0.00002203658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9138274,0.0005563553,0.07678294,0.0002119672,0.00009355006,0.0001008902,0.0001437331,0.003815396,0.004467858],"genre_scores_gemma":[0.9422578,0.0001597262,0.05488565,0.00005575796,0.00001947272,0.00005207815,0.0003165302,0.0002708615,0.001982179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003032861,"threshold_uncertainty_score":0.008360445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195212086416581,"score_gpt":0.2786284851047329,"score_spread":0.2591072764630747,"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."}}