{"id":"W3137917418","doi":"10.1145/1735971.1736036","title":"Addressing shared resource contention in multicore processors via scheduling","year":2010,"lang":"en","type":"article","venue":"ACM SIGPLAN Notices","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scheduling (production processes); Distributed computing; Workload; Thread (computing); Shared memory; Multi-core processor; Gang scheduling; Cache; Parallel computing; Fair-share scheduling; Quality of service; Two-level scheduling; Operating system; 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":[],"consensus_categories":[],"category_scores_codex":[0.00042673,0.0001382585,0.0001583429,0.0001941663,0.0001503815,0.0003078371,0.001243657,0.0001139737,0.00001025475],"category_scores_gemma":[0.0006797953,0.0001323365,0.00003462631,0.0003571717,0.00004179953,0.0005948588,0.0002908172,0.0003427312,0.0000166431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000146572,"about_ca_system_score_gemma":0.00003229492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006651406,"about_ca_topic_score_gemma":0.00008889824,"domain_scores_codex":[0.9987949,0.00005958202,0.0002787905,0.0003852417,0.0002125267,0.0002689537],"domain_scores_gemma":[0.9988658,0.000232813,0.0001831355,0.0005570026,0.00009076084,0.00007043697],"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":[0.0002295894,0.0012827,0.1944523,0.0007777875,0.0001118262,0.0003083952,0.02609301,0.2117653,0.1190986,0.01344885,0.002518911,0.4299128],"study_design_scores_gemma":[0.0004391472,0.00003611333,0.009221734,0.0001680484,0.000004707197,0.000009729604,0.00006368412,0.9819892,0.006106503,0.001107876,0.0005834851,0.0002697948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5046285,0.0001087395,0.4918439,0.0008331998,0.0002675928,0.0002400761,0.000001732242,0.0008148978,0.001261295],"genre_scores_gemma":[0.6960797,0.000001498311,0.3036597,0.0001475963,0.00004727692,0.000008600587,0.000008801224,0.000007948876,0.00003877637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7702239,"threshold_uncertainty_score":0.5396525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05763234658827183,"score_gpt":0.3106199538624083,"score_spread":0.2529876072741365,"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."}}