{"id":"W4389887946","doi":"10.1109/vds60365.2023.00008","title":"HPC<sup>2</sup> lusterScape: Increasing Transparency and Efficiency of Shared High-Performance Computing Clusters for Large-scale AI Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Supercomputer; Computer science; Transparency (behavior); Workload; Visualization; Distributed computing; Scale (ratio); Synchronization (alternating current); Resource (disambiguation); Data science; Operating system; Artificial intelligence; Computer network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001312665,0.0002774476,0.0004677188,0.0002413347,0.0003653252,0.0002127591,0.0009463337,0.000117382,0.000003624028],"category_scores_gemma":[0.00002737884,0.0002586216,0.0001060735,0.0009819437,0.0000504943,0.0006547164,0.0003268824,0.0001446994,0.00001042478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003228952,"about_ca_system_score_gemma":0.00007545564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001067279,"about_ca_topic_score_gemma":0.000005912877,"domain_scores_codex":[0.9973928,0.000117682,0.0006858893,0.0006837361,0.000385848,0.0007340508],"domain_scores_gemma":[0.9986122,0.0003726256,0.0001838084,0.000516599,0.0001651621,0.0001495622],"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":[0.00004735803,0.0001070353,0.00237403,0.0006978539,0.00004386425,0.000004014472,0.009969298,0.9721092,0.0001552543,0.006535493,0.00120968,0.006746913],"study_design_scores_gemma":[0.001283009,0.0001891346,0.001145295,0.0002547336,0.00001373901,0.00001792245,0.0003139108,0.9958047,0.0001306142,0.000403023,0.000137763,0.0003061763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4382675,0.00004302503,0.5603327,0.0001887197,0.0001609545,0.0003111877,0.00004044984,0.0003122636,0.0003431304],"genre_scores_gemma":[0.9747348,0.00001154865,0.02480907,0.0001589639,0.00007248921,0.00001286962,0.00006963436,0.00002098875,0.0001096376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5364673,"threshold_uncertainty_score":0.9999866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902937152497722,"score_gpt":0.2439976044416135,"score_spread":0.2249682329166363,"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."}}