{"id":"W4416429943","doi":"10.1109/iiswc66894.2025.00030","title":"ALPHA-PIM: Analysis of Linear Algebraic Processing for High-Performance Graph Applications on a Real Processing-In-Memory System","year":2025,"lang":"","type":"article","venue":"","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Graph; Wait-for graph; Computation; Graph algorithms; Graph partition; Bottleneck; Data-flow analysis","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":[0.0006600529,0.0009807957,0.0003578444,0.0008572597,0.0004444608,0.0006813435,0.001215486,0.000476607,0.00407968],"category_scores_gemma":[0.003295102,0.0002515765,0.0005386159,0.0009660605,0.0004117136,0.001067271,0.0004661946,0.000838031,0.0007194781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110379,"about_ca_system_score_gemma":0.001435952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006563785,"about_ca_topic_score_gemma":0.006433129,"domain_scores_codex":[0.9995108,0.00009157795,0.00002041409,0.00008522713,0.0001961885,0.00009576131],"domain_scores_gemma":[0.9984244,0.0007549547,0.0001131087,0.0002041227,0.0004184025,0.00008506388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006293692,0.0004806829,0.01257593,0.0005507166,0.0001351795,0.0003631791,0.0001591334,0.8397931,0.02586888,0.0124308,0.01533631,0.09167671],"study_design_scores_gemma":[0.000007812522,0.00006205394,0.001046391,0.000003015873,0.000006750692,0.00001940779,0.00002032794,0.9941267,0.003286743,0.000877045,0.0005401578,0.000003592919],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7550786,0.0007097531,0.2124027,0.0006306585,0.0002065243,0.0002683429,0.001475947,0.01161142,0.01761603],"genre_scores_gemma":[0.9000033,0.0001931129,0.0950965,0.0001216606,0.00003413376,0.0001218254,0.001419394,0.0003650234,0.002644937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006563785,"threshold_uncertainty_score":0.01364785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009859340040570338,"score_gpt":0.2608832478369065,"score_spread":0.2510239077963362,"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."}}