{"id":"W3020586295","doi":"10.1109/tpds.2020.2990429","title":"aeSpTV: An Adaptive and Efficient Framework for Sparse Tensor-Vector Product Kernel on a High-Performance Computing Platform","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Supercomputer; Parallel computing; Leverage (statistics); FLOPS; Thread (computing); Kernel (algebra); Tensor (intrinsic definition); Sparse matrix; Tensor product; Computational science; Theoretical computer science; Artificial intelligence; Mathematics","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":[0.0007328769,0.0009984538,0.0007360356,0.0007011777,0.0006244906,0.001041061,0.002334688,0.0006304513,0.003605538],"category_scores_gemma":[0.002152733,0.0004791981,0.0009166647,0.0009141819,0.0007771897,0.001987999,0.001655373,0.001535054,0.001657268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008056206,"about_ca_system_score_gemma":0.001622964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005561103,"about_ca_topic_score_gemma":0.006492257,"domain_scores_codex":[0.9991946,0.0001578208,0.00005559731,0.0001190304,0.0003674756,0.0001054673],"domain_scores_gemma":[0.9992962,0.0001163726,0.00006123365,0.0002025468,0.0002408456,0.0000827269],"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":[0.0006690195,0.0003074526,0.003630158,0.0004266962,0.0002305747,0.0004343808,0.0004466167,0.3386648,0.0749848,0.07558862,0.03834943,0.4662674],"study_design_scores_gemma":[0.0000350857,0.00006037157,0.0002182717,0.000007733312,0.000009857587,0.00005246359,0.00002432041,0.9733,0.01029914,0.008059419,0.007911038,0.00002226944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008048769,0.000124351,0.9830657,0.00008243735,0.00004516005,0.0000609561,0.0001054655,0.007299102,0.001168032],"genre_scores_gemma":[0.2059927,0.000285299,0.7848377,0.0001451482,0.0000689702,0.0003188429,0.00127561,0.002465859,0.004609863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005561103,"threshold_uncertainty_score":0.01206177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07216470640477066,"score_gpt":0.2944106034335115,"score_spread":0.2222458970287408,"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."}}