{"id":"W2527835641","doi":"10.22360/springsim.2016.hpc.007","title":"Accelerating linear solvers for reservoir simulation on GPU workstations","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Speedup; Computer science; Parallel computing; Multigrid method; Krylov subspace; Xeon; Workstation; Linear system; Graphics processing unit; Computational science; Linear algebra; Graphics; Xeon Phi; Algorithm; Iterative method; Computer graphics (images); Mathematics; Operating system; Partial differential equation","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":[0.0001086392,0.00008756527,0.00009146056,0.00004867221,0.0000650951,0.00001189,0.0000704159,0.00003840202,0.0000477884],"category_scores_gemma":[0.0008832476,0.00006245666,0.00003743772,0.0001119464,0.00001373594,0.0001362516,0.00001054755,0.00004648484,0.00003255593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006277193,"about_ca_system_score_gemma":0.00000564101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.240296e-7,"about_ca_topic_score_gemma":3.699484e-7,"domain_scores_codex":[0.9994411,0.00001331138,0.0001921647,0.0001075597,0.0001085943,0.000137207],"domain_scores_gemma":[0.9953535,0.004389231,0.00002768582,0.0001175514,0.00007314255,0.00003893308],"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":[0.000006014814,0.00000773675,0.000006492973,0.00001703104,0.000008126573,1.041353e-7,0.00003883903,0.952424,0.0006079575,0.01047744,0.0001905253,0.0362157],"study_design_scores_gemma":[0.000215841,0.0000310455,0.00002644967,0.0000429409,0.000003221082,1.328608e-7,0.00002094831,0.925616,0.001693346,0.07048859,0.001762086,0.00009936431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005195939,0.000004581624,0.9916287,0.0002104655,0.0001678379,0.0002072596,0.000007793918,0.0003222811,0.002255128],"genre_scores_gemma":[0.3040173,0.000001938283,0.6955714,0.00005311895,0.00009263914,0.000033962,0.000002292203,0.00002420443,0.0002031806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2988214,"threshold_uncertainty_score":0.2546909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1161960094577626,"score_gpt":0.3837104010153688,"score_spread":0.2675143915576062,"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."}}