Glass-Box Analysis for Computer Systems: Transparency Index, Shapley Attribution, and Markov Models of Branch Prediction
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Alpay, Faruk Alakkad, Hamdi |
| author_facet | Alpay, Faruk Alakkad, Hamdi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Glass-Box Analysis for Computer Systems: Transparency Index, Shapley Attribution, and Markov Models of Branch Prediction Alpay, Faruk Alakkad, Hamdi Performance Hardware Architecture 68M20, 60J10 C.1.1; C.4 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. |
| title | Glass-Box Analysis for Computer Systems: Transparency Index, Shapley Attribution, and Markov Models of Branch Prediction |
| topic | Performance Hardware Architecture 68M20, 60J10 C.1.1; C.4 |
| url | https://arxiv.org/abs/2509.19027 |