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principle-explain-the-number

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Explain the Number

解释这个数字

  • Ask "why not double?" Name the resource or code path that bounds the result, such as a core, a lock, the disk, the network, or the load generator itself. Get it from a profile or from system counters taken during a run, then map it to source. A guess from reading the code is not a limiter. If you cannot say why the number is not twice as good, you do not know what you measured.
  • List what else the number could be measuring, and rule out each one with evidence. The usual suspects are errors, skipped or cached work, code that never ran, an untuned side (one left on default settings), noise, and a piece too small to matter end to end.
  • Keep the evidence with the number. Put the run count, the spread, and the limiter in the notes or a linked artifact.
For a performance number, use the benchmark-checklist skill for the full procedure. For an eval result, ask the same of the trials: did every run do the task, does the gap hold across trials and models, and does the scenario matter.
You skipped this when the evidence behind a number has no run count, no spread, or no named limiter, or when the time saved is larger than the time the changed piece took.
Distinct from Prove It Works, which checks that an output is real.
  • 问“为什么不能翻倍？” 指出限制结果上限的资源或代码路径，例如一个 core、一把 lock、disk、network，或 load generator 本身。通过 profile 或运行期间采集的系统计数器确定限制因素，再将其映射到源代码。仅凭阅读代码得出的猜测并不是限制因素。如果你无法说明为什么这个数字不能达到两倍优化，你就不知道自己究竟测量了什么。
  • 列出这个数字还可能在测量什么，并逐一用证据排除。 常见嫌疑对象包括错误、被跳过或命中缓存的工作、从未运行的代码、未经调优的一侧（某一侧仍保留默认设置）、噪声，以及某个小到不足以影响端到端结果的部分。
  • 将证据与数字保存在一起。 把运行次数、波动范围和限制因素记录在备注或关联的 artifact 中。
对于性能数字，使用 benchmark-checklist skill 完成完整流程。对于 eval 结果，也要对各次试验提出同样的问题：每次运行是否都完成了任务，差异在各次试验和不同模型之间是否始终成立，以及场景是否重要。
如果一个数字背后的证据缺少运行次数、波动范围或明确的限制因素，或者节省的时间超过了被改动部分自身所花的时间，就说明你跳过了这一步。
这不同于 Prove It Works，后者用于检查输出是否真实。