{"id":"W2143998620","doi":"10.1109/cmpcon.1993.289657","title":"Supporting a dynamic SPMD in a multi-threaded architecture","year":2002,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"SPMD; Computer science; Thread (computing); Parallel computing; Locality; Flexibility (engineering); Computation; Architecture; Dynamic data; Computer architecture; Embedded system; 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":[0.0001859192,0.0001039827,0.0001210406,0.0001864416,0.00005001309,0.00007753658,0.000518873,0.00005232015,0.00005047222],"category_scores_gemma":[0.00004746672,0.00009224087,0.00003960761,0.0004249944,0.00001740485,0.0001283005,0.0001470667,0.0001536961,0.00004053242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002875436,"about_ca_system_score_gemma":0.0000092074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000027539,"about_ca_topic_score_gemma":0.00004653901,"domain_scores_codex":[0.9990206,0.0000470921,0.000232997,0.000294364,0.000116496,0.0002885041],"domain_scores_gemma":[0.9995014,0.00004088452,0.00006529196,0.0003177958,0.00002308065,0.00005151081],"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":[0.000009628882,0.001294519,0.01100413,0.00008592477,0.00003804018,0.0003535445,0.01849968,0.1603176,0.002333907,0.02314967,0.008185863,0.7747275],"study_design_scores_gemma":[0.0002296168,0.00002177376,0.0006870796,0.00001441607,4.897833e-7,0.00001978316,0.000008317835,0.9971607,0.0003458973,0.001006888,0.0003761321,0.0001289589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00390633,0.0000797491,0.9901541,0.000907367,0.00004212089,0.0001160087,2.100722e-7,0.0007556019,0.00403853],"genre_scores_gemma":[0.5161105,0.000006778459,0.4823433,0.0002914561,0.000003913262,0.00000567714,5.607149e-7,0.000004324896,0.001233389],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.836843,"threshold_uncertainty_score":0.3761474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676749141578427,"score_gpt":0.2956872243563161,"score_spread":0.2689197329405318,"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."}}