{"id":"W2161884282","doi":"10.1109/icpp.2004.42","title":"Low-cost register-pressure prediction for scalar replacement using pseudo-schedules","year":2004,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Register allocation; Computer science; Speedup; Parallel computing; Software pipelining; Schedule; Software; Scalar (mathematics); Register (sociolinguistics); Control flow graph; Algorithm; Suite; Mathematics; Theoretical computer science; Operating system","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":[0.0003321865,0.0001277025,0.0001283883,0.00009172053,0.000215519,0.0001666785,0.0004477073,0.00007913696,0.000004613467],"category_scores_gemma":[0.00004194531,0.0001186118,0.00006871087,0.0001902766,0.00002615651,0.0003532779,0.0001472056,0.00007022135,0.000004537237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000065611,"about_ca_system_score_gemma":0.00006895809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002526376,"about_ca_topic_score_gemma":0.000002530381,"domain_scores_codex":[0.9988877,0.0000269732,0.0002577768,0.000403805,0.000191581,0.0002321262],"domain_scores_gemma":[0.9991125,0.00002686378,0.0001094434,0.0005421573,0.000138979,0.00007008389],"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":[0.00004000145,0.0002109903,0.0004957854,0.00008706123,0.00004821021,0.000002416829,0.0002770513,0.8570243,0.001069556,0.1310858,0.002996067,0.006662744],"study_design_scores_gemma":[0.0006410644,0.0000989538,0.0001160143,0.00008138112,0.00001244371,0.00001284236,0.0000100348,0.9662612,0.02354023,0.00567971,0.003378036,0.0001680606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002811686,0.0000444159,0.994198,0.0005239513,0.0002414686,0.0004901286,0.000004548263,0.0008481531,0.0008376164],"genre_scores_gemma":[0.1880146,0.00001141375,0.8112445,0.0003115751,0.00007826495,0.00003532399,0.000007378141,0.00001004735,0.0002869294],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1852029,"threshold_uncertainty_score":0.4836851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02809111442326008,"score_gpt":0.2828068451508475,"score_spread":0.2547157307275875,"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."}}