{"id":"W2764223492","doi":"10.1109/access.2017.2760251","title":"A DAQM-Based Load Balancing Scheme for High Performance Computing Platforms","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Scheme (mathematics); Load balancing (electrical power); Distributed computing; Queue; Convergence (economics); Queueing theory; Supercomputer; Utility maximization; Queue management system; Stability (learning theory); Parallel computing; Computer network","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":[0.0006973605,0.0004544639,0.000503248,0.0003342362,0.0007905629,0.0007640313,0.001909237,0.0005182503,0.001499729],"category_scores_gemma":[0.001009958,0.0001463259,0.0002788074,0.0004590186,0.0004029903,0.0008294907,0.0009448513,0.0007943294,0.0003381615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008980266,"about_ca_system_score_gemma":0.001176244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002073587,"about_ca_topic_score_gemma":0.002359554,"domain_scores_codex":[0.9995102,0.0001152079,0.00002907886,0.0001015093,0.0001857609,0.00005827233],"domain_scores_gemma":[0.9995937,0.00006898172,0.00005812373,0.00005081515,0.0001673651,0.00006096899],"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":[0.0004325701,0.0004515249,0.001278328,0.0003136411,0.00008825199,0.0002237656,0.0003657874,0.4836327,0.09498852,0.09460854,0.00838475,0.3152316],"study_design_scores_gemma":[0.00002264773,0.0000839926,0.000121291,0.00000400613,0.000007618826,0.00002756107,0.00001443201,0.9904132,0.002673974,0.004025108,0.002595229,0.00001092254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01759416,0.0002788079,0.9788536,0.0001626101,0.000130481,0.00009865541,0.00002815793,0.0002946439,0.002558954],"genre_scores_gemma":[0.8277659,0.0002645646,0.1677662,0.0001648977,0.0001694627,0.0001634747,0.00008193604,0.00004379192,0.003579701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002073587,"threshold_uncertainty_score":0.006515741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04384616690460819,"score_gpt":0.3126917432039431,"score_spread":0.2688455762993349,"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."}}