{"id":"W1815597787","doi":"10.1109/mm.2015.71","title":"Achieving Exascale Capabilities through Heterogeneous Computing","year":2015,"lang":"en","type":"article","venue":"IEEE Micro","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Exascale computing; Computer science; Supercomputer; Symmetric multiprocessor system; Computer architecture; Software; Efficient energy use; Parallel computing; Distributed computing; Embedded system; Operating system","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":[0.000338522,0.0001601766,0.00018926,0.00005905372,0.0001522459,0.0002023865,0.000799255,0.00006870783,0.000002714656],"category_scores_gemma":[0.00004052951,0.0001582441,0.00007148444,0.0002213589,0.00006466861,0.0002943659,0.0002313676,0.0001304842,0.00005470072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007434536,"about_ca_system_score_gemma":0.00006877632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007540869,"about_ca_topic_score_gemma":0.00000242571,"domain_scores_codex":[0.9987299,0.0001110547,0.0002612421,0.0003788739,0.0001934173,0.0003255132],"domain_scores_gemma":[0.9990702,0.00008579446,0.00009608483,0.0005178309,0.0001322739,0.00009782472],"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":[0.00005042563,0.0008076445,0.008648654,0.0002323674,0.0002070449,0.0002559814,0.07135431,0.6169869,0.04717097,0.01631966,0.1670884,0.07087766],"study_design_scores_gemma":[0.00135693,0.0004521163,0.0002706045,0.0002050153,0.00001867485,0.00061599,0.0002887275,0.5803214,0.3552059,0.0115577,0.04809067,0.001616354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1411332,0.0003968303,0.8542348,0.0002463297,0.000563111,0.00009879959,9.982425e-7,0.0008869292,0.002439043],"genre_scores_gemma":[0.623486,0.000007376301,0.3756408,0.0004783691,0.000121108,0.000002506259,0.000001321616,0.0000115326,0.0002510363],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4823528,"threshold_uncertainty_score":0.6453008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03807270450294099,"score_gpt":0.2769949186668803,"score_spread":0.2389222141639393,"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."}}