{"id":"W3157272387","doi":"10.1109/hpca51647.2021.00065","title":"CHOPIN: Scalable Graphics Rendering in Multi-GPU Systems via Parallel Image Composition","year":2021,"lang":"en","type":"article","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Rendering (computer graphics); Scalability; Parallel computing; Graphics pipeline; General-purpose computing on graphics processing units; Parallel rendering; Frame rate; CUDA; Tiled rendering; Graphics; Graphics processing unit; Software rendering; Texture memory; Implementation; Exploit; Computer graphics (images); 3D computer graphics; Artificial intelligence; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003892746,0.0007753799,0.0005324741,0.0005332787,0.0005472949,0.0009645292,0.001484622,0.0004358757,0.00401665],"category_scores_gemma":[0.0008454533,0.0004048101,0.0004799787,0.0004817858,0.000652278,0.0009545562,0.00164745,0.001177624,0.000738524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006245762,"about_ca_system_score_gemma":0.0006909812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003186272,"about_ca_topic_score_gemma":0.003504251,"domain_scores_codex":[0.9996476,0.00004818188,0.00001158485,0.00004734486,0.0001929224,0.00005231665],"domain_scores_gemma":[0.9997295,0.00006669948,0.00002214123,0.00007963964,0.00005282254,0.00004917825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001427016,0.0003795427,0.002783989,0.0002695447,0.0001585309,0.0008068636,0.0007935994,0.2908182,0.1943619,0.03521885,0.02215051,0.4508314],"study_design_scores_gemma":[0.00009726751,0.0000976456,0.0003271895,0.00000893319,0.00001246072,0.0001108606,0.00003536833,0.9546556,0.03087904,0.004948703,0.008804089,0.0000227348],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04935259,0.0003429077,0.9241864,0.0001859416,0.0001360093,0.0001753799,0.00006816888,0.0164738,0.009078754],"genre_scores_gemma":[0.4340915,0.0002696943,0.5577626,0.0001718478,0.00005451828,0.0001393419,0.0002674673,0.001865182,0.00537782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00401665,"threshold_uncertainty_score":0.01343697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03272806255103762,"score_gpt":0.2965082285915037,"score_spread":0.2637801660404661,"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."}}