With the advent of LLM tools (eg: Claude, Codex), small optimizations are a gateway drug to contributing to the ecosystem. Many people, myself included, use a MacBook as a daily driver. However, there are many mechanisms in these machines and macOS itself that might make posted numbers not really trustworthy, even with --rigorous. I'm thinking about thermal pressure / throttling, coalescing, P/E cores, different power sources, low/high power mode, memory pressure, virtualization, noisy daemons in the background, etc. Of course, there's also pyperformance. Perhaps there's real value in extending system {tune,show} to this platform.
My understanding is that there's no macOS API for CPU affinity, but we can still check stuff (eg: pmset), provide actionable advice. There's taskpolicy and pthread_set_qos_class_self_np. The model is per worker, not global.
Perhaps the real issue here is improving our harness on macOS.
With the advent of LLM tools (eg: Claude, Codex), small optimizations are a gateway drug to contributing to the ecosystem. Many people, myself included, use a MacBook as a daily driver. However, there are many mechanisms in these machines and macOS itself that might make posted numbers not really trustworthy, even with
--rigorous. I'm thinking about thermal pressure / throttling, coalescing, P/E cores, different power sources, low/high power mode, memory pressure, virtualization, noisy daemons in the background, etc. Of course, there's also pyperformance. Perhaps there's real value in extendingsystem {tune,show}to this platform.My understanding is that there's no macOS API for CPU affinity, but we can still check stuff (eg: pmset), provide actionable advice. There's taskpolicy and pthread_set_qos_class_self_np. The model is per worker, not global.
Perhaps the real issue here is improving our harness on macOS.