{"id":"W4390821557","doi":"10.3390/a17010031","title":"GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Qassim University; University of Northern British Columbia","keywords":"Matrix multiplication; Computer science; Multiplication (music); Diagonal; Sparse matrix; Overhead (engineering); Parallel computing; Matrix (chemical analysis); Diagonal matrix; Locality; Algorithm; Set (abstract data type); Computer data storage; Computational science; Mathematics; Computer hardware","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":[0.0003215769,0.0005307425,0.00046873,0.0005844214,0.0004829704,0.0007055787,0.001022338,0.0005292796,0.004675598],"category_scores_gemma":[0.00171679,0.000284751,0.0003952135,0.00117524,0.0004391975,0.0007515742,0.001196262,0.0006855475,0.001520611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005043652,"about_ca_system_score_gemma":0.001185545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004320608,"about_ca_topic_score_gemma":0.00895718,"domain_scores_codex":[0.9997557,0.00004760644,0.00001471981,0.00002497713,0.0001285507,0.00002832913],"domain_scores_gemma":[0.9995394,0.0001267475,0.00004168559,0.0001078908,0.0001529726,0.00003130861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003251229,0.0001917296,0.002326961,0.000240021,0.00007928112,0.0002760803,0.0003173767,0.5141839,0.0210595,0.1320386,0.01681037,0.3121512],"study_design_scores_gemma":[0.00003083993,0.00002373782,0.0001029293,0.000006217566,0.000002620304,0.00003087819,0.00001825098,0.9838662,0.002080126,0.01106548,0.00276787,0.000004931424],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02861649,0.0002994692,0.9612824,0.0001903533,0.00007297472,0.00007950651,0.0001636568,0.001539766,0.007755402],"genre_scores_gemma":[0.2163966,0.0002230181,0.7771351,0.00007214277,0.00003843621,0.0002050965,0.0004941048,0.0002479245,0.005187463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004675598,"threshold_uncertainty_score":0.01564145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040094391687363,"score_gpt":0.2955937275811731,"score_spread":0.2751927836642994,"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."}}