{"id":"W4399836999","doi":"10.48550/arxiv.2406.12110","title":"CacheSquash: Making caches speculation-aware","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Business; Computer network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001159983,0.0008692477,0.0004742639,0.0005179563,0.0006976507,0.001154911,0.002079881,0.0007482558,0.003198141],"category_scores_gemma":[0.003930948,0.00063479,0.0005156797,0.0005690123,0.001303825,0.002470556,0.002122406,0.001549438,0.0006723727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007152536,"about_ca_system_score_gemma":0.00198734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002368638,"about_ca_topic_score_gemma":0.005867444,"domain_scores_codex":[0.9988844,0.0002368639,0.00007467411,0.0001694903,0.0004468643,0.0001877742],"domain_scores_gemma":[0.9974088,0.0006219149,0.0003352973,0.001109005,0.0004041351,0.0001208914],"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":[0.00289954,0.000530046,0.0163944,0.0007451622,0.0003847074,0.0006987812,0.0009762762,0.2547265,0.1853873,0.06391746,0.04583591,0.4275039],"study_design_scores_gemma":[0.0001994345,0.0005382231,0.001195705,0.00006627316,0.0001442938,0.0002789483,0.0001558132,0.8193477,0.114573,0.03778206,0.02562547,0.00009301635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2425219,0.003348069,0.7096541,0.002185683,0.0005559425,0.0002344328,0.000510164,0.02922297,0.01176681],"genre_scores_gemma":[0.841005,0.0004793612,0.1514224,0.0005235953,0.0001118463,0.0001257965,0.0003980515,0.0007119116,0.00522206],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003198141,"threshold_uncertainty_score":0.01069885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09236074501883755,"score_gpt":0.2165616808226572,"score_spread":0.1242009358038197,"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."}}