{"id":"W3111941241","doi":"10.48550/arxiv.2012.06281","title":"Trash Talk: Accelerating Garbage Collection on Integrated GPUs is Worthless","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Garbage collection; Computer science; Garbage; Programmer; Embedded system; Variety (cybernetics); Mobile device; Task (project management); Memory bandwidth; Parallel computing; Computer architecture; Operating system; Programming language; Systems engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001896514,0.0003815485,0.0003790678,0.0003404928,0.0003202409,0.0003489488,0.001619705,0.0004548341,0.00003897656],"category_scores_gemma":[0.00005273708,0.0004431394,0.0002069076,0.001344542,0.00005401833,0.000250711,0.001091183,0.001236813,0.00004521403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002784115,"about_ca_system_score_gemma":0.000246133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001427995,"about_ca_topic_score_gemma":0.00002224646,"domain_scores_codex":[0.9977702,0.0001975751,0.0002726786,0.001316234,0.0001300262,0.0003132933],"domain_scores_gemma":[0.9983439,0.00009256031,0.0003337685,0.0008458734,0.00021432,0.0001695109],"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":[0.0001031855,0.0002773898,0.001259827,0.0001198895,0.0002016005,0.0005908947,0.001588313,0.9039146,0.0001435367,0.06762548,0.01991968,0.004255648],"study_design_scores_gemma":[0.0003017613,0.0001078421,0.0002468122,0.0001558969,0.0000231435,0.000003764129,0.0000380708,0.9880834,0.001794113,0.008046504,0.0007271067,0.0004715904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05672715,0.00001665714,0.9325453,0.000371473,0.0004689967,0.0003373172,0.0000105193,0.00145242,0.008070141],"genre_scores_gemma":[0.9782199,0.00011006,0.01897359,0.0006495826,0.000072031,0.0000019797,0.0000295096,0.00002457786,0.001918827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9214927,"threshold_uncertainty_score":0.9998021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09747982097487073,"score_gpt":0.2090469111279385,"score_spread":0.1115670901530677,"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."}}