{"id":"W2098161986","doi":"10.1109/fccm.2006.65","title":"Sparse Matrix-Vector Multiplication for Finite Element Method Matrices on FPGAs","year":2006,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Parallel computing; Stratix; Field-programmable gate array; Pipeline (software); Sparse matrix; FLOPS; Scalability; Vector processor; Matrix multiplication; Computational science; Computer hardware","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":[0.0004233727,0.0001271889,0.000125343,0.0001468219,0.0001320748,0.000139818,0.0004828767,0.00005181477,0.0000116398],"category_scores_gemma":[0.00003678077,0.0001101663,0.00007159539,0.0002797898,0.000007758714,0.0001483584,0.00008001198,0.00004950369,0.00003797781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004225182,"about_ca_system_score_gemma":0.00002216338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007225397,"about_ca_topic_score_gemma":0.000005394057,"domain_scores_codex":[0.9989157,0.00005625021,0.0002712594,0.0003707063,0.0001725716,0.000213504],"domain_scores_gemma":[0.9989436,0.0003606954,0.0001366574,0.000410722,0.0001101696,0.00003815747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002271397,0.0002643994,0.0001559147,0.00002882085,0.00001460202,0.000001132836,0.00008082854,0.4035875,0.0006815302,0.5146365,0.0440595,0.03646659],"study_design_scores_gemma":[0.0002804828,0.0001052938,0.0002751664,0.000008531639,0.000004075902,0.000001162411,0.000002136713,0.952871,0.01850541,0.006697074,0.02109539,0.0001543141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001374535,0.00004629143,0.9939376,0.001157217,0.00008874266,0.0004242274,0.00000357066,0.0008568136,0.003348159],"genre_scores_gemma":[0.0579727,0.00001010954,0.9395619,0.0002973799,0.00009594961,0.00009346638,0.000015222,0.000009303209,0.001943966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5492834,"threshold_uncertainty_score":0.4492452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02380533283076406,"score_gpt":0.3181352119325712,"score_spread":0.2943298791018071,"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."}}