{"id":"W4233182001","doi":"10.32920/ryerson.14655531","title":"Cache filtering algorithm for least frequently used data with accurate memory simulation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Parallel computing; Cache; Cache algorithms; Cache-oblivious algorithm; CPU cache; Dram; Cache coloring; Algorithm; Interleaved memory; Computer hardware; Semiconductor memory; Memory management","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":[0.0005639948,0.0005060534,0.0007647539,0.0006249659,0.0005158748,0.0007723362,0.001468018,0.0006989784,0.002765404],"category_scores_gemma":[0.002288185,0.0003701243,0.0005051909,0.0007643236,0.0003541834,0.001274025,0.0004786927,0.0007274848,0.0005579893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007712179,"about_ca_system_score_gemma":0.001257769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008697946,"about_ca_topic_score_gemma":0.005770664,"domain_scores_codex":[0.9996087,0.00006975804,0.00002573705,0.00005339559,0.0001830702,0.00005944825],"domain_scores_gemma":[0.9989809,0.0003860988,0.00007159801,0.0002096762,0.0003052249,0.0000464867],"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":[0.0002332893,0.00008802905,0.002131579,0.00006489921,0.00005655128,0.00007943371,0.00009527883,0.8761341,0.01436529,0.01791705,0.002158588,0.08667597],"study_design_scores_gemma":[0.000007313265,0.000009601654,0.00005183402,0.000001496225,0.000003262514,0.00000531918,0.000002108701,0.9968791,0.001667351,0.000954652,0.0004155455,0.000002465767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02441429,0.0001198264,0.9729881,0.00007744857,0.00003667886,0.00003892822,0.00005333435,0.001002804,0.001268581],"genre_scores_gemma":[0.3816967,0.0001637644,0.6140605,0.00009666681,0.00003420715,0.0002112735,0.0003193557,0.0003203793,0.003097142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008697946,"threshold_uncertainty_score":0.01729465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09607504414667822,"score_gpt":0.3350370610210939,"score_spread":0.2389620168744157,"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."}}