{"id":"W1586644070","doi":"10.1007/bfb0038676","title":"Loop storage optimization for dataflow machines","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Dataflow; Dataflow architecture; Computer science; Parallel computing; Computation; Exploit; FIFO (computing and electronics); Graph; Loop (graph theory); FIFO and LIFO accounting; Data flow diagram; Queue; Model of computation; Overhead (engineering); Algorithm; Theoretical computer science; Programming language; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000545503,0.0005601526,0.0008187349,0.0004714713,0.0005251802,0.0008013914,0.001225588,0.0004239618,0.006108779],"category_scores_gemma":[0.001865509,0.0003392654,0.0004352236,0.001001371,0.000470264,0.001653653,0.0008180189,0.000635108,0.0003856316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001020089,"about_ca_system_score_gemma":0.00108737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00249891,"about_ca_topic_score_gemma":0.003217808,"domain_scores_codex":[0.9997488,0.00004646736,0.00001343382,0.00003639218,0.00009446681,0.00006033929],"domain_scores_gemma":[0.9994441,0.0002942784,0.00003605311,0.00009836281,0.0001012961,0.00002586205],"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":[0.0003842226,0.0001023137,0.0003347134,0.0002064993,0.00002953511,0.00003353386,0.00007410724,0.7094139,0.01040851,0.05770556,0.00828249,0.2130245],"study_design_scores_gemma":[0.00001574355,0.00002670112,0.0000821626,0.000006751589,0.000005318075,0.000009767528,0.000008231864,0.9610401,0.004071079,0.03348072,0.001247944,0.000005435577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04416691,0.001053919,0.9420222,0.0004159354,0.0001121144,0.00005305134,0.0001930835,0.001734667,0.01024823],"genre_scores_gemma":[0.73254,0.0005957305,0.2511843,0.0001038818,0.0001141358,0.0001696712,0.0004203256,0.000811811,0.01406024],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006108779,"threshold_uncertainty_score":0.02043593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01599827221338266,"score_gpt":0.256564949369787,"score_spread":0.2405666771564044,"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."}}