{"id":"W1773424846","doi":"10.48550/arxiv.1005.1695","title":"CrystalGPU: Transparent and Efficient Utilization of GPU Power","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Parallel computing; General-purpose computing on graphics processing units; CUDA; Power (physics); Computational science; Computer graphics (images); Graphics; Physics","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":[0.0002007067,0.0001964129,0.000251184,0.0002350707,0.00008452777,0.00005436234,0.0008096347,0.0002503772,0.00001180292],"category_scores_gemma":[0.00001847906,0.0002207292,0.00009438931,0.0003197947,0.0001114085,0.00008470847,0.0006967885,0.0003374964,0.000002105935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003580541,"about_ca_system_score_gemma":0.00008879928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003219087,"about_ca_topic_score_gemma":0.000005371732,"domain_scores_codex":[0.9988114,0.00007960539,0.0002015946,0.0006475426,0.00008663225,0.0001732696],"domain_scores_gemma":[0.9987503,0.00004814348,0.0002369975,0.0006936581,0.000176856,0.00009409331],"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":[0.00001377116,0.0001280669,0.000597091,0.00007790355,0.00002796102,0.00001977602,0.000517978,0.8825759,0.0001628383,0.1150721,0.00008638654,0.0007202451],"study_design_scores_gemma":[0.0002139723,0.00004520766,0.001333816,0.00007623953,0.00002092315,0.000002437397,0.00001346222,0.9904067,0.001085996,0.006364644,0.0001971759,0.000239451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1839771,0.00006971694,0.8140407,0.0000326155,0.0002146657,0.0001685245,0.000006534952,0.0002385703,0.00125159],"genre_scores_gemma":[0.985947,0.0001427002,0.0137336,0.00002274155,0.000009881371,4.049923e-7,0.000007963421,0.000009369727,0.0001263731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8019699,"threshold_uncertainty_score":0.9001076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07587969116579776,"score_gpt":0.2126763665102704,"score_spread":0.1367966753444727,"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."}}