{"id":"W2163816448","doi":"10.1109/aps.2009.5171722","title":"Acceleration of large-scale FDTD simulations on high performance GPU clusters","year":2009,"lang":"en","type":"article","venue":"Digest - IEEE Antennas and Propagation Society. International Symposium","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acceleware (Canada)","funders":"Nvidia","keywords":"Acceleration; Computational science; Graphics processing unit; GPU cluster; Computer science; Scalability; Finite-difference time-domain method; Cluster (spacecraft); CUDA; Parallel computing; Software; Computer cluster; Graphics; General-purpose computing on graphics processing units; Scale (ratio); Computer graphics (images); Physics; Operating system; Optics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001631471,0.0001667367,0.0001737108,0.00005736808,0.0001242686,0.00005762815,0.0001168573,0.0001016888,0.00006751919],"category_scores_gemma":[0.00001420636,0.0001598437,0.00008081176,0.0002049698,0.00003789115,0.0002976478,0.000009736791,0.0001533631,0.000007466952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006171255,"about_ca_system_score_gemma":0.00001147135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004440167,"about_ca_topic_score_gemma":0.000001489281,"domain_scores_codex":[0.9989107,0.00003231303,0.0003624195,0.0002054431,0.0002995321,0.0001896016],"domain_scores_gemma":[0.9994316,0.00009796715,0.00009569946,0.0001267546,0.0001845652,0.0000634614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001722987,0.0004395968,0.0054518,0.0001157756,0.0001787785,0.000001277093,0.003615622,0.3260355,0.6305727,0.003125108,0.001356693,0.02893487],"study_design_scores_gemma":[0.0008816198,0.0004037065,0.03429018,0.00007022548,0.00002309,0.000003778122,0.00008038303,0.9068131,0.05509589,0.0002295944,0.001848211,0.0002601834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692722,0.00005045751,0.02506616,0.001570827,0.0005567588,0.0002536534,0.00003208816,0.0001319574,0.003065913],"genre_scores_gemma":[0.9963164,0.0003945888,0.002051966,0.0006431635,0.0001883434,0.000009938645,0.0001005371,0.00001809946,0.0002770034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5807776,"threshold_uncertainty_score":0.6518236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053142562586342,"score_gpt":0.2616349175563215,"score_spread":0.2511034919304581,"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."}}