{"id":"W3215592064","doi":"","title":"Differentiating-based Vectorization for Sparse Kernels","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Parallel computing; Sparse matrix; Kernel (algebra); Computation; Speedup; Vectorization (mathematics); Solver; Code (set theory); Supercomputer; Algorithm; Computational science; Set (abstract data type); Mathematics; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001924153,0.0002685431,0.0003017678,0.0002088092,0.0001841686,0.000291954,0.001197476,0.0002651977,0.00001408988],"category_scores_gemma":[0.00008691203,0.000332155,0.0002400886,0.0004556603,0.00003544467,0.000201209,0.0008110378,0.0002630829,0.000004784979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001466147,"about_ca_system_score_gemma":0.000273908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000326617,"about_ca_topic_score_gemma":0.000009352993,"domain_scores_codex":[0.998293,0.0001367389,0.0002189373,0.0009898338,0.00008427507,0.0002771953],"domain_scores_gemma":[0.9981241,0.0001518446,0.0003136989,0.0009485272,0.0003584587,0.0001033686],"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":[0.000009836494,0.00009693692,0.001038354,0.00009462994,0.00003961504,0.00002305021,0.00009274757,0.9655445,0.0000375415,0.03226614,0.0003368222,0.0004198881],"study_design_scores_gemma":[0.0003758022,0.0000397461,0.0004215245,0.0001163825,0.00003591961,5.8535e-7,0.000008016995,0.9895133,0.001499672,0.007459463,0.0001742831,0.0003553233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03112381,0.00003825325,0.9666599,0.00007164512,0.0006266061,0.0003561155,0.000009694116,0.0007845519,0.0003294379],"genre_scores_gemma":[0.9217891,0.0000291813,0.07739738,0.0001101833,0.00007348587,0.000003703944,0.0001264729,0.00001966901,0.0004508194],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8906653,"threshold_uncertainty_score":0.999913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07531089294161115,"score_gpt":0.2042343542805592,"score_spread":0.128923461338948,"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."}}