{"id":"W2899399853","doi":"10.1109/cluster.2018.00029","title":"Scalable Shared-Memory Parallelization of the Block Recursive Inversion Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Scalability; Parallel computing; Computer science; Block (permutation group theory); Shared memory; Inversion (geology); Inverse; Distributed memory; Parallel algorithm; Reduction (mathematics); Computational complexity theory; Execution time; Algorithm; Block size; Mathematics; Key (lock)","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":[0.0004095772,0.0004815958,0.0005271293,0.0002958851,0.0003390441,0.0005878474,0.001296433,0.0003409578,0.003212834],"category_scores_gemma":[0.001476668,0.0002321928,0.0004445081,0.0004180133,0.0003412241,0.0008666141,0.000924043,0.0005972662,0.0006981954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003688921,"about_ca_system_score_gemma":0.00127542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005307667,"about_ca_topic_score_gemma":0.006909846,"domain_scores_codex":[0.9995887,0.00007365915,0.00002025429,0.00006275143,0.000176116,0.00007858875],"domain_scores_gemma":[0.9994434,0.0001476756,0.00003391744,0.000159686,0.0001856628,0.00002972912],"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":[0.0007681585,0.0002600566,0.002757744,0.0002416589,0.0001656512,0.0006246184,0.0004237478,0.4719688,0.1040125,0.03076685,0.01005233,0.377958],"study_design_scores_gemma":[0.00005549059,0.00005744314,0.0003394876,0.000004992673,0.00001323955,0.00005952125,0.0000238382,0.9765513,0.01649151,0.003557513,0.002835349,0.00001029194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1087152,0.0002666552,0.8722574,0.0001869604,0.00007782855,0.000110831,0.0001668727,0.005781566,0.01243675],"genre_scores_gemma":[0.6133167,0.00009942359,0.3806698,0.00005745652,0.00003436564,0.0001558911,0.000449707,0.0003365936,0.004879987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005307667,"threshold_uncertainty_score":0.01074803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01229722274615713,"score_gpt":0.2393286985257406,"score_spread":0.2270314757795835,"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."}}