{"id":"W4402634902","doi":"10.1007/978-3-031-72567-8_4","title":"Integrating Multi-FPGA Acceleration to OpenMP Distributed Computing","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Field-programmable gate array; Parallel computing; Acceleration; Computational science; Distributed computing; Embedded system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000213657,0.0005123197,0.0003327505,0.0003874266,0.0003392319,0.001084613,0.00111638,0.0004469059,0.009267243],"category_scores_gemma":[0.0006718968,0.0002661886,0.0002077377,0.0005933277,0.0001849145,0.0009169176,0.0007384514,0.0008187848,0.002045156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000472258,"about_ca_system_score_gemma":0.000366831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001113536,"about_ca_topic_score_gemma":0.002286406,"domain_scores_codex":[0.9997784,0.00003371185,0.000009207854,0.00003212965,0.0001001757,0.00004632869],"domain_scores_gemma":[0.9997106,0.00006042832,0.00001332507,0.0000821109,0.0001012847,0.00003222195],"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":[0.0007124038,0.0002840767,0.00190458,0.0002825914,0.00008154289,0.000366395,0.0001486838,0.08104791,0.07644177,0.03844881,0.02268297,0.7775983],"study_design_scores_gemma":[0.000145304,0.0005240932,0.001883022,0.00008788459,0.000077818,0.0005381083,0.0001118463,0.7796718,0.09217837,0.03186422,0.09286804,0.00004948022],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09239813,0.003343744,0.8082222,0.0007831283,0.001190104,0.00008891553,0.0001468394,0.01213708,0.08168986],"genre_scores_gemma":[0.6250725,0.0009834942,0.3304335,0.0002046299,0.0002247127,0.00006204571,0.0002764941,0.0007251191,0.04201751],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009267243,"threshold_uncertainty_score":0.03100199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02950202829754467,"score_gpt":0.2952120802992664,"score_spread":0.2657100520017217,"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."}}