{"id":"W3107936220","doi":"10.1109/pdcat.2009.87","title":"Balanced Dense Polynomial Multiplication on Multi-Cores","year":2009,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; National Science Foundation","keywords":"Speedup; Multiplication (music); Matrix multiplication; Computer science; Parallel computing; Computation; Multiplication algorithm; Univariate; Fast Fourier transform; Bivariate analysis; Polynomial; Algorithm; Arithmetic; Mathematics; Multivariate statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005963208,0.0006393851,0.0006666651,0.0004072102,0.0005784668,0.0006218805,0.001112691,0.0002856339,0.005114739],"category_scores_gemma":[0.002311512,0.0002845014,0.0003697775,0.0009793615,0.0004828318,0.002091306,0.001452543,0.0007116165,0.001162855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003838591,"about_ca_system_score_gemma":0.000812784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061254,"about_ca_topic_score_gemma":0.002651941,"domain_scores_codex":[0.9993889,0.000123858,0.00005085118,0.00009087396,0.0002461296,0.00009925225],"domain_scores_gemma":[0.9990951,0.0002583825,0.00005800333,0.0002735256,0.0002634404,0.00005155353],"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":[0.001263494,0.0002734789,0.002521179,0.0005767928,0.00006622381,0.0006336231,0.0004385523,0.1782561,0.09320983,0.1411979,0.01834649,0.5632163],"study_design_scores_gemma":[0.0001251792,0.0002440694,0.0004972373,0.00003817349,0.00002247878,0.0001892063,0.00008777068,0.8715729,0.04791759,0.0661865,0.0130935,0.00002535991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08732589,0.0003288215,0.9005609,0.0002260228,0.0001049531,0.000074756,0.0001317171,0.00206055,0.009186398],"genre_scores_gemma":[0.5068273,0.0002080595,0.4863675,0.0001416909,0.00005443725,0.0002371959,0.000563886,0.0002801616,0.005319693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005114739,"threshold_uncertainty_score":0.01711047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084127788653407,"score_gpt":0.2799358269007453,"score_spread":0.2590945490142113,"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."}}