{"id":"W2318549902","doi":"10.1109/tdsc.2016.2548463","title":"Understanding Practical Tradeoffs in HPC Checkpoint-Scheduling Policies","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scheduling (production processes); Exploit; Fault tolerance; Distributed computing; Energy consumption; Software deployment; Supercomputer; Efficient energy use; Embedded system; Reliability engineering; Parallel computing; Operating system; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006744006,0.0002364427,0.0002536368,0.0003297533,0.0004239391,0.0002128961,0.0003253662,0.000108484,0.000007812805],"category_scores_gemma":[0.00002477754,0.0001804674,0.00009502034,0.0004902862,0.00007092886,0.0001380419,0.0000232934,0.0003539668,0.00001543885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001950521,"about_ca_system_score_gemma":0.00005083636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006579023,"about_ca_topic_score_gemma":0.00004142307,"domain_scores_codex":[0.9980127,0.0001385382,0.000394915,0.000580818,0.0003240102,0.0005489849],"domain_scores_gemma":[0.9986866,0.0007014718,0.00009963844,0.00033935,0.00003189191,0.0001410194],"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":[0.0001738556,0.00130653,0.0005488975,0.0002319521,0.000358682,0.0007643753,0.01786896,0.4842531,0.004354316,0.121833,0.0003543824,0.3679519],"study_design_scores_gemma":[0.001937533,0.0002655252,0.000181675,0.0006279713,0.00003215525,0.0003429127,0.001782785,0.9848971,0.004563477,0.003387968,0.00132149,0.0006594526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08768769,0.0000603262,0.9032457,0.007167958,0.0004108332,0.0001444708,0.00000124114,0.0002267736,0.001055034],"genre_scores_gemma":[0.9766009,0.00002759767,0.02277601,0.0003734276,0.00008011209,0.000003168942,1.167751e-7,0.00001770527,0.0001209411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8889132,"threshold_uncertainty_score":0.7359247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06695110729053493,"score_gpt":0.2785192576247053,"score_spread":0.2115681503341703,"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."}}