{"id":"W4243058626","doi":"10.1109/micro.2016.7783731","title":"OSCAR: Orchestrating STT-RAM cache traffic for heterogeneous CPU-GPU architectures","year":2016,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Advanced Scientific Computing Research; U.S. Department of Energy; Office of Science; Advanced Micro Devices; National Science Foundation","keywords":"Computer science; Cache; Cache pollution; Parallel computing; Network packet; Bottleneck; Embedded system; Cache coloring; CPU cache; Cache algorithms; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005071287,0.0008326132,0.0004520848,0.0004673231,0.0004627559,0.0009606183,0.001737632,0.0004070722,0.002248075],"category_scores_gemma":[0.001145615,0.0002483541,0.0003244059,0.0004121381,0.0003713153,0.0009432507,0.0009121838,0.0005861804,0.0005591876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008232794,"about_ca_system_score_gemma":0.001249948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002582298,"about_ca_topic_score_gemma":0.006678576,"domain_scores_codex":[0.9996175,0.00006766251,0.00002504055,0.00008477074,0.0001048089,0.0001001804],"domain_scores_gemma":[0.9994316,0.0001252072,0.00005689057,0.0001271634,0.0001398434,0.0001192727],"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":[0.002067708,0.001097213,0.01532081,0.0004509264,0.0002518382,0.0009580391,0.0006381962,0.396878,0.2553882,0.02029615,0.02721101,0.279442],"study_design_scores_gemma":[0.00005703592,0.0002046432,0.0005580548,0.000008432727,0.00002787003,0.0000631986,0.00006270195,0.9641051,0.02738653,0.00189959,0.005604541,0.00002223905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4811225,0.001248799,0.4719576,0.0003634718,0.0003236624,0.0004113561,0.0003632568,0.0252943,0.01891504],"genre_scores_gemma":[0.8748404,0.000205664,0.1191338,0.0002285748,0.00005462401,0.0001857026,0.0004543984,0.000668123,0.004228733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002582298,"threshold_uncertainty_score":0.007520556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02391785545232628,"score_gpt":0.270102247510568,"score_spread":0.2461843920582418,"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."}}