{"id":"W2807600494","doi":"10.48550/arxiv.1307.6488","title":"From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Perimeter Institute","funders":"","keywords":"Computer science; Parallel computing; Tree traversal; CUDA; Code (set theory); Cache; Code generation; GPU cluster; Block (permutation group theory); Theoretical computer science; Programming language; Computational science; Operating system; Mathematics; Key (lock)","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":[0.002542125,0.001087884,0.0008127255,0.001042611,0.000793006,0.001837339,0.002798738,0.001227409,0.00539115],"category_scores_gemma":[0.006341589,0.0008710469,0.001410616,0.0009267583,0.001158422,0.001887493,0.002244324,0.002154493,0.002299925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297996,"about_ca_system_score_gemma":0.002980394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003547403,"about_ca_topic_score_gemma":0.004217444,"domain_scores_codex":[0.9986726,0.0003699314,0.0000765113,0.0001522889,0.0005892221,0.0001394666],"domain_scores_gemma":[0.9981524,0.0005527323,0.00011889,0.0006282693,0.0004521152,0.00009551505],"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":[0.0001761476,0.0002405515,0.002707115,0.0002466008,0.0001191862,0.0002642705,0.0003566766,0.6781284,0.01340596,0.1503126,0.01761457,0.136428],"study_design_scores_gemma":[0.00002532071,0.00002431532,0.0001251258,0.00001818152,0.00001032521,0.00002613843,0.0000193745,0.9684119,0.003577307,0.0221731,0.005575688,0.00001307966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00766974,0.0001288626,0.9764909,0.0002092433,0.00005422988,0.0001197643,0.0001619636,0.01072071,0.004444781],"genre_scores_gemma":[0.1026136,0.0001447885,0.8893519,0.0001455089,0.00003865407,0.0003556874,0.0005855878,0.004818959,0.00194529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00539115,"threshold_uncertainty_score":0.01803517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1083505757905866,"score_gpt":0.2477782851626662,"score_spread":0.1394277093720796,"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."}}