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Post #949

@TheB1ackParade

Welcome to the Black Parade

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PostedNov 1311/13/2025, 03:17 AM
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有个对 AI 不满意的点是,就算我问的问题完全错误,它妈都能给我头头是道写个几千字,如果人类并非领域专家绝对被糊弄死。。。 我让 codex 自己动起来做 perf topdown 分析,然后它发现 cpu_core/topdown-fetch-lat/ 在 atomic.AddUint64 + 无 cacheline padding 的时候很高。这个发现挺好的,因为 TopdownL3 输出几百行,我一般只看几行主要 metrics 而不看 breakdown 所以从来没有注意到。我问 codex 为什么 topdown-fetch-lat 很高,这不是 frontend latency 吗,而 cacheline-exclusive stall 显然是 backend stall。 然后 codex (gpt-5-codex) 开始背诵名词了,欣赏一下 On Intel’s Topdown model, “frontend latency” isn’t limited to the I-cache hardware—it buckets any cycles where the backend wants μops but the core can’t deliver them because they were dropped, replayed, or never issued. In nopad atom, each atomic.AddUint64 repeatedly loses the cache line mid-flight: the load succeeds on one core, but before the RMW completes, another core grabs exclusive ownership, forcing a pipeline flush and μop replay. Intel counts those μop replays in the frontend-latency bucket (topdown-fetch-lat) because the frontend must re-deliver the same μops after the backend abandons the current ones. 我是因为那句 “before the RMW completes, another core grabs exclusive ownership, forcing a pipeline flush and μop replay” 产生怀疑,atomic.AddUint64 (lock prefix insn) 不是会 stall 等待吗,怎么又开始 pipeline flush 了? 所以我复制 gpt-5-codex 这个回复去问 chatgpt-5.1: is the following statement correct? 果然 chatgpt-5.1 开始痛批这个说法,逐字逐句反驳。 好了现在你手上有两个领域专家给出相反的意见,你是 tech lead,请做技术决策吧。😀 高潮来了,我重新设置了一下 codex model (gpt-5-codex -> gpt-5-high),让 codex 重新解释 why "nopad noatom" showed much higher cpu_core/topdown-fetch-lat/ than "nopad atom"? 高潮点是 我tm太粗心,完全问反问题了,明明是 atom 的 fetch-lat 高,我问的是为什么 nonatom 高。😀先问是不是,再问为什么——知乎 然而 codex (gpt-5-high) 居然又给了一篇头头是道的长篇解释,我都看笑了,合着正话反话你都说完了,两难自解是吧。画风是这样的,大部分非专家多半会被唬住: Net: topdown-fetch-lat can be elevated not only by I-cache/ITLB misses but also by frontend re-steers after Machine Clears. In nopad noatom, false sharing + races cause more of those clears; atomics remove the mis-speculation and move the cost squarely into backend memory stalls. 还好我立刻发现自己问题问反了,让它重新检查之后,终于给出一个似乎合理的解释。 用 AI 学习新知识实在太难了,就算是十句里只夹一句假话,产生的误导也破坏力很强,我还是好好看书看文档吧。。。