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arXiv · 2610.21387

Top-Down Microarchitecture Analysis Without Causal Modelling Is Dangerous!

Chenxi Wang·Lei Wang·Wanling Gao·Guoxin Kang·Fanda Fan·Jianfeng Zhan
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Abstract

Top-Down Microarchitecture Analysis (TMA) is the standard for identifying CPU bottlenecks. However, TMA implicitly models performance via a single causal path: Workload(dataset, algorithm) => CPU(CPI) => System(Performance) (microarchitectural efficiency path). It overlooks two hidden causal paths: Workload(dataset, algorithm) => Instructions(Retired) => System(Performance) (algorithmic efficiency path) and Workload(dataset, algorithm) => Instructions(Retired) => CPU(CPI) => System(Performance) (microarchitecture-algorithm efficiency path). This causal blindness often misleads architects into futile hardware optimizations. To address this, we propose Causal Top-Down Microarchitecture Analysis (CTMA). Utilizing the object causal model, CTMA explicitly models these three causal paths among the workload, instructions, CPU, and system. We validate CTMA across Intel, AMD, and ARM platforms. Our experiments reveal counterintuitive insights: (1) CPI Profiling Blindness: Algorithmic instruction bloat can double elapsed time while the CPI profile remains frozen. (2) Over-Speculation Congestion: Throttling the Frontend paradoxically improves performance in pointer-chasing algorithms. (3) ILP Window Collapse: Extreme data topologies shift bottlenecks from Backend ROB capacity to Frontend fill-rate starvation. (4) Op-Cache SMT Overlap: Ultra-small footprints yield SMT speedups on Intel but cause severe thrashing on AMD. (5) "Ghost State" Artifact: Stale pipeline mitigations on Intel Xeon Gold 5120T artificially boost performance by 22.75% on SPEC CPU2017 and 42.2% on SPEC CPU2026. Based on this, we propose a dynamic speculation throttling mechanism.

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Chenxi Wang, Lei Wang, Wanling Gao, Guoxin Kang, Fanda Fan, Jianfeng Zhan. 2026-10-15. Top-Down Microarchitecture Analysis Without Causal Modelling Is Dangerous!. https://arxiv.org/abs/2610.21387

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