{"id":"W2743824295","doi":"10.1007/s00500-017-2764-7","title":"Parallel ILU preconditioners in GPU computation","year":2017,"lang":"en","type":"article","venue":"Soft Computing","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; CMG Reservoir Simulation Foundation; University of Calgary; Nvidia","keywords":"Preconditioner; Computer science; Parallel computing; Speedup; Block (permutation group theory); Matrix (chemical analysis); Linear system; Sparse matrix; Computational science; Algorithm; Iterative method; Mathematics; Chemistry; Computational chemistry; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005404098,0.0001079201,0.0001411145,0.00008631564,0.0005186093,0.0004052938,0.0008778396,0.00004581171,0.000007863778],"category_scores_gemma":[0.00008807813,0.0001128179,0.00004648678,0.00008890466,0.00005609522,0.0005827,0.0003288345,0.0001498411,0.00007129593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002967435,"about_ca_system_score_gemma":0.00003348224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002832367,"about_ca_topic_score_gemma":0.000006714845,"domain_scores_codex":[0.9989947,0.00007171722,0.0002229285,0.0003112074,0.0001414212,0.0002579977],"domain_scores_gemma":[0.9991445,0.0001404527,0.0002004893,0.0004210901,0.00003672318,0.00005672062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001090307,0.0001345208,0.008748101,0.00006350716,0.00002569328,0.00009389452,0.003034346,0.08187975,0.0001301522,0.2919885,0.0007290206,0.6131616],"study_design_scores_gemma":[0.0005488246,0.00002375606,0.0377972,0.00005977165,0.000001626862,0.00001543089,0.00003319319,0.9032069,0.00009246414,0.05771502,0.0003074355,0.0001983606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06270496,0.00003042674,0.9303766,0.0006627261,0.0006070372,0.0001142178,7.883498e-7,0.0001564831,0.005346755],"genre_scores_gemma":[0.9144912,0.000001242373,0.08515567,0.000123558,0.000121316,0.000002178577,0.000003014101,0.000005878639,0.00009597984],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8517862,"threshold_uncertainty_score":0.4600582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0196105905296612,"score_gpt":0.2833568347330785,"score_spread":0.2637462442034173,"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."}}