{"id":"W2883965613","doi":"10.1145/3203217.3203243","title":"Taming irregular applications via advanced dynamic parallelism on GPUs","year":2018,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Kernel (algebra); Bottleneck; Overhead (engineering); Parallelism (grammar); Parallel computing; Workload; CUDA; Data parallelism; Computer architecture; Embedded system; Operating 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":[0.0002774592,0.0005118233,0.0004381216,0.0003366381,0.0003799881,0.0006754536,0.0008854302,0.0003221836,0.001496904],"category_scores_gemma":[0.001446906,0.0002456382,0.0003183165,0.0004392239,0.0007565255,0.0008871475,0.001439006,0.0008725459,0.0005020875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003619586,"about_ca_system_score_gemma":0.0004731595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007618938,"about_ca_topic_score_gemma":0.001041197,"domain_scores_codex":[0.9996954,0.00005704235,0.00001389811,0.00004507475,0.0001210164,0.00006761297],"domain_scores_gemma":[0.9993278,0.0002038296,0.00006759571,0.0002698159,0.00007524381,0.00005574974],"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":[0.0007757418,0.000275384,0.004660976,0.0003490971,0.0001116075,0.0007997722,0.0006623145,0.4221614,0.2781168,0.06213558,0.007273249,0.2226781],"study_design_scores_gemma":[0.00006200391,0.0001984239,0.001067209,0.00002492469,0.00002871913,0.0002083528,0.0000749927,0.9058072,0.05477478,0.02444204,0.01328128,0.00002999486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4740274,0.001705153,0.5009085,0.000525879,0.0002446188,0.0000744623,0.0000976317,0.00500993,0.01740641],"genre_scores_gemma":[0.9267353,0.0004395695,0.06989545,0.0001595237,0.00004713318,0.00004610439,0.0001049084,0.0004069432,0.002164972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001496904,"threshold_uncertainty_score":0.005007684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007174581648149097,"score_gpt":0.2629762581007464,"score_spread":0.2558016764525973,"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."}}