{"id":"W1964533240","doi":"10.1109/tmag.2011.2177814","title":"Power Aware Parallel 3-D Finite Element Mesh Refinement Performance Modeling and Analysis With CUDA/MPI on GPU and Multi-Core Architecture","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Parallel computing; CUDA; SPMD; Computational science; GPU cluster; Finite element method; Multi-core processor; SIMD; Software; Computation; Supercomputer; Algorithm; Operating system","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":[0.0002861785,0.0004549389,0.0004135786,0.0004345862,0.0003304522,0.0004572368,0.001048767,0.0005113678,0.001042328],"category_scores_gemma":[0.001353033,0.0002902115,0.0005116099,0.0005657891,0.0003417571,0.0006247977,0.0003390848,0.0004339726,0.0002484927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000638365,"about_ca_system_score_gemma":0.0006421997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008086276,"about_ca_topic_score_gemma":0.006217646,"domain_scores_codex":[0.9997254,0.00006277313,0.00001254296,0.00003084583,0.0001408727,0.00002761808],"domain_scores_gemma":[0.9994957,0.0001873979,0.00004959544,0.0001073391,0.0001452151,0.00001472216],"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":[0.00004745633,0.00005030941,0.001400638,0.00005605466,0.00002276203,0.00005066358,0.0000718597,0.9620864,0.01479049,0.003857529,0.0005025697,0.01706333],"study_design_scores_gemma":[0.000002327362,0.000006438414,0.0001867766,0.000001029208,0.000001958106,0.000006190387,0.000003137943,0.9968966,0.002358946,0.00027157,0.0002623246,0.000002767435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1844275,0.0002203491,0.8039399,0.0002030172,0.00003080762,0.00007939403,0.0001809658,0.002330751,0.008587355],"genre_scores_gemma":[0.8303366,0.000157331,0.1669094,0.0000347688,0.00001023344,0.0001254768,0.0001629411,0.0002742457,0.001988967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008086276,"threshold_uncertainty_score":0.01607841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297054414821429,"score_gpt":0.2509497496219653,"score_spread":0.227979205473751,"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."}}