{"id":"W2100720213","doi":"10.1142/s0218213008003765","title":"OPTIMAL BASIC BLOCK INSTRUCTION SCHEDULING FOR MULTIPLE-ISSUE PROCESSORS USING CONSTRAINT PROGRAMMING","year":2008,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); University of Waterloo","funders":"International Business Machines Corporation; Intel Corporation","keywords":"Computer science; Instruction scheduling; Compiler; Scheduling (production processes); Suite; Parallel computing; Optimizing compiler; Constraint programming; Schedule; Algorithm; Mathematical optimization; Dynamic priority scheduling; Programming language; Two-level scheduling; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004757637,0.000171392,0.0002224934,0.0004027339,0.0002288869,0.0003598559,0.0006982529,0.00009022366,0.00004327224],"category_scores_gemma":[0.0008925609,0.0001710555,0.0001922281,0.0002970202,0.0001762194,0.001575927,0.00007064255,0.0002391328,0.00001033506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001791284,"about_ca_system_score_gemma":0.0004069254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001175666,"about_ca_topic_score_gemma":0.00000803412,"domain_scores_codex":[0.9978829,0.0000441685,0.0009774795,0.000258863,0.0005915426,0.0002450547],"domain_scores_gemma":[0.9970784,0.0002522557,0.00070664,0.0001309895,0.001709521,0.0001222148],"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":[0.0001239909,0.000111074,0.000532501,0.000009011669,0.00008953213,0.00004716405,0.001130033,0.403867,0.003863132,0.005401807,0.00000974351,0.584815],"study_design_scores_gemma":[0.0003145295,0.0002132228,0.0001692082,0.0001375197,0.00002103975,0.002460943,0.0009942205,0.9294207,0.06341325,0.001270628,0.001290062,0.0002946834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2428929,0.00002380241,0.7543805,0.0006299106,0.001779822,0.0002166821,0.000005324442,0.00004112303,0.00002996171],"genre_scores_gemma":[0.6319713,0.00002274019,0.3674454,0.00006380275,0.0004726339,0.00000546246,0.000002449418,0.000008483615,0.00000775969],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5845203,"threshold_uncertainty_score":0.6975442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09098479028904376,"score_gpt":0.3335965103387119,"score_spread":0.2426117200496681,"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."}}