{"id":"W2089611640","doi":"10.1109/cefc.2010.5481252","title":"Understanding the efficiency of parallel incomplete Cholesky preconditioners on the performance of ICCG solvers for multi-core and GPU systems","year":2010,"lang":"en","type":"article","venue":"","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cholesky decomposition; Incomplete Cholesky factorization; Conjugate gradient method; Solver; Computer science; Parallel computing; Minimum degree algorithm; Factorization; Algorithm; Programming language","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":[0.001547319,0.0006544181,0.0007219865,0.0005160307,0.0003526065,0.0008781948,0.0005344106,0.0006059388,0.001579966],"category_scores_gemma":[0.01159265,0.0003558965,0.000238209,0.0007494846,0.0007868475,0.002292088,0.0005701048,0.0006095402,0.0002743477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003938321,"about_ca_system_score_gemma":0.0008226011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00288127,"about_ca_topic_score_gemma":0.002498616,"domain_scores_codex":[0.9993399,0.0002107569,0.00003419228,0.00007828542,0.00024159,0.00009529856],"domain_scores_gemma":[0.9926105,0.005528483,0.0004663085,0.0006884846,0.0006328123,0.00007338507],"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":[0.0009745698,0.0001824677,0.007573068,0.0004066784,0.0000639636,0.0002060604,0.0003332263,0.8496274,0.04886249,0.0114301,0.001230384,0.07910956],"study_design_scores_gemma":[0.00002591952,0.0001277793,0.002496101,0.00001549976,0.00001866237,0.00003874052,0.00005698209,0.9613883,0.03226825,0.002977022,0.000573726,0.00001304385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8399022,0.001365421,0.1467211,0.0006158081,0.00003292977,0.00005614105,0.0001764641,0.0007753898,0.01035456],"genre_scores_gemma":[0.9588594,0.0007082187,0.03902273,0.00005353162,0.00002903162,0.00003664977,0.0001830725,0.0002243983,0.0008829723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00288127,"threshold_uncertainty_score":0.008183122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09656858121821488,"score_gpt":0.2720199604564914,"score_spread":0.1754513792382765,"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."}}