{"id":"W2148487037","doi":"10.1109/pccc.2009.5403809","title":"GPU support for batch oriented workloads","year":2009,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bloom filter; Computer science; Workload; Data structure; Set (abstract data type); Parallel computing; Graphics; Filter (signal processing); General-purpose computing on graphics processing units; Probabilistic logic; Instruction set; Operating system; Algorithm; Artificial intelligence; Programming language","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":[0.0009397849,0.0007753168,0.0008483766,0.0006078868,0.0009372351,0.001882217,0.002903849,0.0008188144,0.005333139],"category_scores_gemma":[0.006431853,0.0005200719,0.0004294782,0.00132885,0.0005428303,0.002078205,0.001438595,0.001097508,0.001270585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091131,"about_ca_system_score_gemma":0.001622112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007167558,"about_ca_topic_score_gemma":0.007276401,"domain_scores_codex":[0.9989694,0.000243401,0.00008367318,0.0001593006,0.0003372373,0.0002069413],"domain_scores_gemma":[0.9958853,0.001324769,0.0001471849,0.001607504,0.0007872629,0.0002479267],"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":[0.005446228,0.00108159,0.03180475,0.0006319736,0.0003886144,0.00207315,0.001516208,0.2866448,0.1734762,0.05806637,0.06819101,0.3706792],"study_design_scores_gemma":[0.0002134994,0.0002802679,0.002528579,0.00002916003,0.00005799418,0.0002629343,0.0001632386,0.9128851,0.04868207,0.01231006,0.02252878,0.00005833689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4527718,0.0007415169,0.4772662,0.0008075898,0.0002516128,0.0003069378,0.0008620388,0.03913603,0.02785634],"genre_scores_gemma":[0.8899937,0.0002056611,0.1024848,0.0002305862,0.00005570356,0.0001506461,0.001053646,0.001137161,0.004688125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007167558,"threshold_uncertainty_score":0.0178411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512463765667292,"score_gpt":0.2440351039392928,"score_spread":0.2289104662826199,"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."}}