{"id":"W4285503949","doi":"10.1109/ipdps53621.2022.00121","title":"HDagg: Hybrid Aggregation of Loop-carried Dependence Iterations in Sparse Matrix Computations","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Speedup; Sparse matrix; Computer science; Cholesky decomposition; Computation; Parallel computing; Solver; Algorithm; Locality; Matrix (chemical analysis); Tree (set theory); Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000635943,0.0007646796,0.0006617946,0.0007593449,0.0008809516,0.0006879432,0.001099351,0.0003244937,0.001829054],"category_scores_gemma":[0.001666374,0.0003601532,0.0006161606,0.001075154,0.0005227572,0.001158096,0.001698591,0.000743605,0.0005381671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006120746,"about_ca_system_score_gemma":0.001857218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006731331,"about_ca_topic_score_gemma":0.014451,"domain_scores_codex":[0.9993586,0.00009868399,0.00003878351,0.00008972443,0.0002974919,0.0001167419],"domain_scores_gemma":[0.9991161,0.000236894,0.00008026093,0.0002403388,0.0002333906,0.00009297255],"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":[0.000660973,0.0004112718,0.01102248,0.0002224919,0.0001516758,0.000269634,0.0005318305,0.3468081,0.03829909,0.01907187,0.01569728,0.5668533],"study_design_scores_gemma":[0.00005846585,0.0001466718,0.0008400198,0.000008658364,0.00002201663,0.00005797994,0.00006088415,0.9732473,0.01298684,0.006930158,0.005623965,0.00001714996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1099368,0.0004283287,0.8757942,0.0002415803,0.0001619232,0.0001326243,0.0002065097,0.007659682,0.005438359],"genre_scores_gemma":[0.4745183,0.0001756205,0.5190786,0.000209884,0.0000763263,0.000175832,0.0008860785,0.0006851539,0.004194323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006731331,"threshold_uncertainty_score":0.01338428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649664832997953,"score_gpt":0.2814351398593931,"score_spread":0.2649384915294136,"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."}}