{"id":"W2513915076","doi":"10.1109/fccm.2016.33","title":"Accelerating Apache Spark Big Data Analysis with FPGAs","year":2016,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Software portability; Computer science; SPARK (programming language); Big data; Java; Field-programmable gate array; Programming paradigm; Embedded system; Operating system; Parallel computing; Programming language","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":[0.0008645123,0.0007974025,0.0004629494,0.0007832797,0.0004887387,0.001166535,0.001097485,0.0002876801,0.01265942],"category_scores_gemma":[0.002264979,0.0003538584,0.0005335878,0.0008953162,0.0002850971,0.0008411102,0.0007996621,0.0006108831,0.004557639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005981116,"about_ca_system_score_gemma":0.0009892861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00369236,"about_ca_topic_score_gemma":0.004682065,"domain_scores_codex":[0.999215,0.0001106743,0.00004564201,0.0001406683,0.0003874328,0.0001005309],"domain_scores_gemma":[0.9987615,0.0003125727,0.00004626184,0.0002803789,0.0004748174,0.0001244473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003215881,0.0003035259,0.01025834,0.0008058032,0.0003532117,0.0009768071,0.000290778,0.1228102,0.05279393,0.02132936,0.267937,0.5189251],"study_design_scores_gemma":[0.0005330186,0.000386403,0.005547439,0.0001289585,0.0001049443,0.0003850234,0.0001818317,0.7182944,0.09100994,0.01778008,0.1655263,0.0001216349],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1215272,0.001976658,0.6606345,0.001995125,0.002173714,0.0004471864,0.004984342,0.1135098,0.09275164],"genre_scores_gemma":[0.5681958,0.001120492,0.4021902,0.0004197909,0.0002927353,0.0002727622,0.006886081,0.002802917,0.01781921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01265942,"threshold_uncertainty_score":0.04234999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08768122493673237,"score_gpt":0.2535300519665726,"score_spread":0.1658488270298403,"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."}}