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codex - 💡(How to fix) Fix Recommended Optimal Concurrent Thread Count [1 participants]

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openai/codex#17356•Fetched 2026-04-11 06:17:03
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Root Cause

But it didn't feel optimal. It seemed like my brain was thrashing because of the context switching, and eventually many of my threads went dormant as I focused in on only 2 or 3 at a time.

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What variant of Codex are you using?

App and CLI

What feature would you like to see?

TLDR: Display an estimated optimal concurrent AI thread count that is dynamically calculated based on real-time and historical AI turn time and human response time

I've been struggling recently with trying to optimize the number of active threads I have running.

I finally got my local dev environment setup to be completely parallelizable, and thus yesterday I went crazy with up to 6 threads at once.

But it didn't feel optimal. It seemed like my brain was thrashing because of the context switching, and eventually many of my threads went dormant as I focused in on only 2 or 3 at a time.

I was talking with Codex and thinking about how to optimize this. With some simple formulas, and understanding average AI turn time and average time I take to respond to an AI after a turn, it seems like we could start coming up with a dynamic number of optimal threads to run concurrently.

So my ideas is this: please start tracking these metrics, and expose them, and even provide an optimal thread recommendation based on that info.

I think this will become even more important once Codex becomes an always-running agent similar to OpenClaw. I'm hoping it will be able to essentially act as a project manager, scanning GitHub for the next most appropriate issue, and then spinning up the threads.

But the problem is that I think we need some kind of thread throttling in that scenario. And the metric I described above I think would be excellent to determine the ideal throttling.

And what's nice is that I could start measuring my own and my AIs' performance. Because as I attempt to vertically scale each thread by raising the abstraction level of the tasks, as models improve in capability and speed, and as my own brain improves in responding to AIs and context-switching, it would be nice to see my throughput of work actually increase.

Sorry this is long-winded, but I think it would be awesome.

Additional information

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extent analysis

TL;DR

Implement a dynamic thread count calculator based on real-time and historical AI turn time and human response time to optimize concurrent AI threads.

Guidance

  • Identify the key metrics to track, such as average AI turn time and average human response time, to inform the dynamic thread count calculation.
  • Develop a formula to calculate the optimal thread count based on these metrics, considering factors like context switching and thread dormancy.
  • Consider implementing thread throttling to prevent overloading and optimize resource utilization.
  • Explore ways to expose and visualize these metrics to facilitate monitoring and adjustment of the thread count.

Example

No specific code example can be provided without further technical details, but a potential approach could involve using a simple formula like optimal_threads = (average_human_response_time / average_ai_turn_time) * scaling_factor, where scaling_factor is a tunable parameter.

Notes

The implementation of this feature would require careful consideration of various factors, including the specific use case, system resources, and user behavior. Additionally, the formula for calculating the optimal thread count may need to be refined and adjusted based on real-world data and user feedback.

Recommendation

Apply a workaround by manually tracking and adjusting the thread count based on observed performance and user experience, until a more automated solution can be implemented. This will allow for some optimization of concurrent AI threads while a more robust solution is developed.

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#autograd error#model save/load#optimization#mixed precision#environment setup

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