{"id":"W4388031372","doi":"10.1145/3581784.3607103","title":"Embracing Irregular Parallelism in HPC with YGM","year":2023,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Laboratory Directed Research and Development; Lawrence Livermore National Laboratory; U.S. Department of Energy","keywords":"Computer science; Serialization; Asynchronous communication; Latency (audio); Implementation; Suite; Message passing; Parallel computing; Distributed computing; USable; Computer architecture; Computer network; Operating system; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002762712,0.00008977727,0.0001088149,0.0002293236,0.00006226592,0.00009805465,0.0004711348,0.00003803184,0.000006498826],"category_scores_gemma":[0.00001379074,0.00007154743,0.0000204904,0.001061814,0.00001700379,0.0002392716,0.0001694228,0.00008359442,0.00008257067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001692692,"about_ca_system_score_gemma":0.00003243783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004050683,"about_ca_topic_score_gemma":0.00001590447,"domain_scores_codex":[0.9991331,0.00003870398,0.0001434148,0.0002779982,0.0001671675,0.0002396252],"domain_scores_gemma":[0.9994914,0.00004586161,0.00003401721,0.0003522308,0.0000323065,0.0000442081],"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":[0.00002247485,0.0001075505,0.00322936,0.00003793159,0.00002455926,0.0002897911,0.002727327,0.44034,0.0003566419,0.4951481,0.02803238,0.02968394],"study_design_scores_gemma":[0.0003276127,0.00005788671,0.006239498,0.00004480088,9.718414e-7,0.00001431345,0.00003003878,0.9748566,0.001357412,0.01441242,0.002416409,0.0002420968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008343996,0.00002100024,0.9529803,0.001340109,0.00007264945,0.000100511,1.040265e-7,0.001978155,0.0351632],"genre_scores_gemma":[0.6831231,0.00001680756,0.3130364,0.0004670358,0.00003330317,0.00001903084,0.000003040354,0.00001082548,0.00329057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6747791,"threshold_uncertainty_score":0.291762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01445443690114672,"score_gpt":0.247370805678141,"score_spread":0.2329163687769942,"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."}}