{"id":"W2951543943","doi":"10.1145/3329785.3329922","title":"Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs","year":2019,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020; Banting and Best Diabetes Centre, University of Toronto; Deutsche Forschungsgemeinschaft; Nvidia","keywords":"Computer science; Hash function; Parallel computing; Heuristics; Execution time; Replicate; CUDA; Graphics; Dependency (UML); General-purpose computing on graphics processing units; Algorithm; Computer graphics (images); Programming language; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001232402,0.00004140715,0.00009689553,0.0001496044,0.00002625398,0.00003433703,0.0001317045,0.0000141232,0.00001454238],"category_scores_gemma":[0.000006080091,0.00003351687,0.00003386502,0.0003023029,0.000007617844,0.0001875931,0.00004409122,0.00003210954,0.00002217563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007766413,"about_ca_system_score_gemma":0.000007589552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005690353,"about_ca_topic_score_gemma":0.00000513552,"domain_scores_codex":[0.9995875,0.00001502015,0.00009323683,0.0001218615,0.0001204416,0.00006187481],"domain_scores_gemma":[0.9996458,0.00004294175,0.00004873013,0.0002208001,0.0000234649,0.00001831675],"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":[0.000007136381,0.00005654453,0.4388307,0.00008276277,0.0002321923,0.000001605532,0.0009724938,0.004267186,0.008119319,0.009262359,0.00006713254,0.5381006],"study_design_scores_gemma":[0.00009350247,0.00006059134,0.06318019,0.00002489946,0.0000178378,9.42191e-7,0.00001319949,0.9352254,0.001311537,0.00002278235,0.000006299952,0.00004278897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698646,0.0000150832,0.02745732,0.00007229717,0.00005021567,0.00003405115,1.325811e-7,0.00004897796,0.002457334],"genre_scores_gemma":[0.9977736,0.00000693971,0.001428479,0.00007258838,0.00000352323,7.22667e-7,4.400131e-7,0.000001211111,0.000712526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9309583,"threshold_uncertainty_score":0.1366778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009039321772734662,"score_gpt":0.2022655335372611,"score_spread":0.1932262117645265,"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."}}