{"id":"W2470677901","doi":"10.1016/j.camwa.2016.06.027","title":"Preconditioned GMRES solver on multiple-GPU architecture","year":2016,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Western Canada Research Grid; CMG Reservoir Simulation Foundation; University of Florida","keywords":"Generalized minimal residual method; Preconditioner; Solver; Parallel computing; Computer science; Multiplication (music); Computational science; CUDA; Iterative method; Algorithm; Mathematics","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":[0.0001462402,0.0002117124,0.0001959442,0.0001288227,0.0002309807,0.0001132831,0.0009780944,0.00005376987,0.00003013374],"category_scores_gemma":[0.00002366881,0.0001274156,0.00006902202,0.0002873433,0.000110713,0.0001857902,0.0001448766,0.0001051533,0.0003084194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003326904,"about_ca_system_score_gemma":0.00003925963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.765295e-7,"about_ca_topic_score_gemma":0.000001563034,"domain_scores_codex":[0.9987668,0.00003528542,0.000249435,0.0004328096,0.000244411,0.0002712276],"domain_scores_gemma":[0.9975013,0.001056262,0.0001626045,0.001067444,0.00007388497,0.0001385091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000103313,0.0004059641,0.00002881566,0.00006918393,0.00006239014,0.000004250731,0.0007931739,0.0005115175,0.0006597649,0.8556566,0.001609552,0.1401885],"study_design_scores_gemma":[0.002883394,0.0004755746,0.0005481694,0.0006263645,0.00005229886,0.0001956058,0.00006260892,0.07451938,0.0100737,0.8556078,0.05370804,0.001247033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001569344,0.00001358458,0.9936003,0.001695725,0.00007183787,0.0006578389,0.00001951589,0.0003837875,0.001988053],"genre_scores_gemma":[0.1957822,0.000008329131,0.8022341,0.0004571709,0.0001802044,0.0006098816,0.000009244922,0.00002995038,0.0006889254],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1942128,"threshold_uncertainty_score":0.5195855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014953126089578,"score_gpt":0.2238963465839853,"score_spread":0.2137468153230895,"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."}}