{"id":"W2419201994","doi":"","title":"Accelerating complex data transfer for cluster computing","year":2016,"lang":"en","type":"article","venue":"IEEE International Conference on Cloud Computing Technology and Science","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Serialization; Computer science; Bottleneck; Data transmission; Distributed computing; Overhead (engineering); SPARK (programming language); Java; Transfer (computing); Parallel computing; Operating system; Embedded system; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006841277,0.0006102688,0.00048851,0.0006749507,0.0009634371,0.001294206,0.001518353,0.0005192972,0.00653864],"category_scores_gemma":[0.003978208,0.0003547895,0.0003529522,0.001429016,0.000694508,0.002221543,0.001840468,0.001075704,0.001585158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00118064,"about_ca_system_score_gemma":0.001113656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002517137,"about_ca_topic_score_gemma":0.002068299,"domain_scores_codex":[0.9993941,0.0001008106,0.00004011731,0.00009789335,0.0002840001,0.00008301598],"domain_scores_gemma":[0.9982133,0.0005603349,0.0001191603,0.000676481,0.0003422845,0.00008853737],"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":[0.001011273,0.000371116,0.004842486,0.0005437112,0.0001328286,0.0004572473,0.00069717,0.1628655,0.1344214,0.09592149,0.04155531,0.5571805],"study_design_scores_gemma":[0.0001267979,0.0002671506,0.001688162,0.0000355004,0.00005052477,0.0003690914,0.0001398194,0.8387179,0.06706881,0.04234761,0.04911997,0.00006873085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06100705,0.0007453545,0.9196,0.000627931,0.000266078,0.0002348056,0.0001424638,0.008488883,0.008887298],"genre_scores_gemma":[0.4203393,0.000584366,0.5678514,0.0001697344,0.0001209278,0.0003643442,0.0005082684,0.0006989077,0.009362771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00653864,"threshold_uncertainty_score":0.02187389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1337019915681453,"score_gpt":0.3426406099870526,"score_spread":0.2089386184189073,"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."}}