{"id":"W4293024962","doi":"10.1145/3489517.3530610","title":"Optimizing parallel PREM compilation over nested loop structures","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 59th ACM/IEEE Design Automation Conference","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Parallel computing; Loop tiling; Nested loop join; Benchmark (surveying); Kernel (algebra); Suite; Thread (computing); Loop (graph theory); Scheduling (production processes); Operating system; Compiler","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000613113,0.0007411414,0.0004270821,0.0003984848,0.0003486415,0.0005569975,0.0008137317,0.0002820895,0.001075237],"category_scores_gemma":[0.001877789,0.0003093162,0.0004425947,0.0004507829,0.0005515188,0.0006337832,0.0006089627,0.0004293169,0.0003119578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006094699,"about_ca_system_score_gemma":0.001147523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001852644,"about_ca_topic_score_gemma":0.003754905,"domain_scores_codex":[0.9995531,0.0001162057,0.00002323501,0.00009529582,0.00009564525,0.0001163741],"domain_scores_gemma":[0.9991217,0.0004083476,0.0001200768,0.0001884283,0.0001246623,0.00003672584],"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":[0.0004797651,0.000230138,0.004419034,0.0003091746,0.00005444884,0.0002952105,0.0001887295,0.8082675,0.05748026,0.01104707,0.002720629,0.1145081],"study_design_scores_gemma":[0.00003061298,0.0001642977,0.000429196,0.000009254009,0.00001626197,0.00003976172,0.00003707005,0.9735748,0.0200244,0.004535248,0.001131901,0.000007253559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4766907,0.0004187512,0.5056067,0.0001391712,0.00006044962,0.0001122426,0.0002247155,0.008084094,0.008663271],"genre_scores_gemma":[0.7803825,0.0001034525,0.2166436,0.0000666874,0.00001723801,0.0001038783,0.0003654315,0.0007395261,0.001577743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001852644,"threshold_uncertainty_score":0.004422069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04740336592497874,"score_gpt":0.2686451311555154,"score_spread":0.2212417652305367,"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."}}