{"ID":2867160,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19027","arxiv_id":"2509.19027","title":"Glass-Box Analysis for Computer Systems: Transparency Index, Shapley Attribution, and Markov Models of Branch Prediction","abstract":"We formalize glass-box analysis for computer systems and introduce three principled tools. First, the Glass-Box Transparency Index (GTI) quantifies the fraction of performance variance explainable by internal features and comes equipped with bounds, invariances, cross-validated estimation, and bootstrap confidence intervals. Second, Explainable Throughput Decomposition (ETD) uses Shapley values to provide an efficiency-preserving attribution of throughput, together with non-asymptotic Monte Carlo error guarantees and convexity (Jensen) gap bounds. Third, we develop an exact Markov analytic framework for branch predictors, including a closed-form misprediction rate for a two-bit saturating counter under a two-state Markov branch process and its i.i.d. corollary. Additionally, we establish an identifiability theorem for recovering event rates from aggregated hardware counters and provide stability bounds under noise.","short_abstract":"We formalize glass-box analysis for computer systems and introduce three principled tools. First, the Glass-Box Transparency Index (GTI) quantifies the fraction of performance variance explainable by internal features and comes equipped with bounds, invariances, cross-validated estimation, and bootstrap confidence inte...","url_abs":"https://arxiv.org/abs/2509.19027","url_pdf":"https://arxiv.org/pdf/2509.19027v1","authors":"[\"Faruk Alpay\",\"Hamdi Alakkad\"]","published":"2025-09-23T14:01:20Z","proceeding":"cs.PF","tasks":"[\"cs.PF\",\"cs.AR\"]","methods":"[]","has_code":false}