{"id":"W2016888570","doi":"10.1109/tpds.2010.62","title":"hiCUDA: High-Level GPGPU Programming","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":219,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CUDA; Computer science; Porting; Compiler; General-purpose computing on graphics processing units; Parallel computing; Programmer; Graphics; Software portability; Code (set theory); Operating system; Programming language; Software","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009055604,0.0008282721,0.0006297514,0.0006198454,0.0005712945,0.001416582,0.002764957,0.0009369865,0.006661736],"category_scores_gemma":[0.002640106,0.0006671196,0.0007310023,0.0008831368,0.000796122,0.001236858,0.00144484,0.001979092,0.00355763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004566775,"about_ca_system_score_gemma":0.001298705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002544265,"about_ca_topic_score_gemma":0.00239151,"domain_scores_codex":[0.9993837,0.0001478301,0.00005203457,0.0001094581,0.0002114043,0.00009563542],"domain_scores_gemma":[0.9990138,0.0002955976,0.00008338876,0.0002560562,0.0002753844,0.0000758167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001043398,0.0003267907,0.004460464,0.001063499,0.0001957373,0.0008503592,0.001065605,0.1022851,0.04707252,0.08340506,0.2106248,0.5476068],"study_design_scores_gemma":[0.0003077438,0.0002533733,0.001942665,0.0001138241,0.00005710937,0.0005671963,0.00008547399,0.6834759,0.0790503,0.02939094,0.2046261,0.000129435],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.007709516,0.0002043727,0.9230155,0.0001060612,0.00007205953,0.0001405233,0.0005793137,0.06167743,0.006495206],"genre_scores_gemma":[0.09467957,0.000352485,0.8824304,0.0003539878,0.00006892813,0.0006515844,0.002749592,0.01066716,0.008046381],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.006661736,"threshold_uncertainty_score":0.02228576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02100504174156377,"score_gpt":0.2438634565018963,"score_spread":0.2228584147603326,"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."}}