{"id":"W2048052601","doi":"10.1155/2014/321081","title":"GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform","year":2014,"lang":"en","type":"article","venue":"International Journal of Antennas and Propagation","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Compute Canada","keywords":"Computer science; Parallel computing; CUDA; Speedup; Graphics processing unit; Central processing unit; Xeon Phi; Finite-difference time-domain method; Computational science; Code (set theory); Xeon; GPU cluster; Computer hardware; Physics; Optics; Set (abstract data type)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001738467,0.000464127,0.0004269614,0.0004266011,0.0004675809,0.0005062469,0.0008452044,0.0005044867,0.002255542],"category_scores_gemma":[0.0005609056,0.0002190622,0.0004352188,0.0006117183,0.0002602556,0.0004642542,0.0005244306,0.0004758051,0.0007405956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005181666,"about_ca_system_score_gemma":0.0006334043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00744924,"about_ca_topic_score_gemma":0.005634039,"domain_scores_codex":[0.9998451,0.00002227199,0.00000547222,0.0000271021,0.00007862495,0.00002125504],"domain_scores_gemma":[0.9997968,0.0000434847,0.00001124423,0.00004238932,0.00008787602,0.00001823823],"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":[0.000245408,0.0001096524,0.002282633,0.0001779934,0.00006257459,0.0009028128,0.0002466725,0.7550789,0.08318752,0.0178599,0.01484371,0.1250023],"study_design_scores_gemma":[0.00001966814,0.00001103285,0.0001992715,0.000003905667,0.000004111609,0.00004925898,0.00001304567,0.9896221,0.005157052,0.001172449,0.003740895,0.00000725721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06669299,0.0002790431,0.9117031,0.0002423855,0.0001805201,0.00006237834,0.0003612997,0.005029165,0.01544922],"genre_scores_gemma":[0.4521609,0.0002424484,0.5381221,0.00006829996,0.00003875533,0.0001189747,0.0007196327,0.0005440723,0.007984744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00744924,"threshold_uncertainty_score":0.01481175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01970265576165711,"score_gpt":0.2711081646156452,"score_spread":0.2514055088539881,"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."}}