{"id":"W2038666141","doi":"10.1109/hpca.2015.7056063","title":"GPGPU performance and power estimation using machine learning","year":2015,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":213,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Memory bandwidth; Graphics; Kernel (algebra); Bandwidth (computing); General-purpose computing on graphics processing units; Frequency scaling; Multi-core processor; Graphics processing unit; Range (aeronautics); Scaling; Computer hardware; Power (physics); Parallel computing; Computer graphics (images)","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":[0.000323402,0.0009586681,0.0005037515,0.0007596152,0.0002679088,0.0006658066,0.001070249,0.0005722845,0.0009311957],"category_scores_gemma":[0.003404628,0.0004595695,0.0004340684,0.0008071524,0.0004329349,0.00124443,0.0004029901,0.0009272091,0.0003839087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009420805,"about_ca_system_score_gemma":0.0006887771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01110431,"about_ca_topic_score_gemma":0.008498062,"domain_scores_codex":[0.9997216,0.00005328096,0.00001236956,0.00008280555,0.00009971847,0.00003021522],"domain_scores_gemma":[0.9991853,0.0004011224,0.0001132766,0.0001226806,0.0001574362,0.00002014883],"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":[0.00002802182,0.00004591774,0.002847473,0.00002156302,0.00002550453,0.00001965863,0.00001605415,0.9653975,0.00146157,0.0006595912,0.000629873,0.02884731],"study_design_scores_gemma":[0.000001339363,0.000005743782,0.0002824519,0.000001270616,0.000001539727,0.000004524356,0.000001496312,0.9984723,0.0006804152,0.0004520259,0.00009493004,0.00000195858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2790405,0.0005212894,0.7095167,0.0004323121,0.00004215923,0.00009445125,0.0003846332,0.005164738,0.004803135],"genre_scores_gemma":[0.9284483,0.0001972143,0.06906207,0.00008366227,0.00001999134,0.00008348039,0.0004241607,0.0002198193,0.001461302],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01110431,"threshold_uncertainty_score":0.02207935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02982307307628912,"score_gpt":0.2702077411338378,"score_spread":0.2403846680575487,"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."}}