Chunk 26.1

In this chunk, the assistant continued iterating on the dispatch pacer and PI controller tuning. After deploying the re-bootstrap fix (synthcap3), the user reported that when hitting the memory ceiling, the integral went deeply negative and caused the pipeline to fully drain before resuming synthesis. The assistant analyzed the issue as integral saturation and overly aggressive backoff, then implemented a series of PI tuning changes: normalizing error by target, lowering ki from 0.02 to 0.001, raising the integral cap asymmetrically (positive 100, negative -20), and tightening the rate_mult clamp. These changes were deployed as pitune1 through pitune4. The assistant also fixed re-bootstrap spam by adding a check that the pipeline is truly empty (total_dispatched <= gpu_completions) before re-entering bootstrap, preventing repeated re-bootstrap cycles while synthesis items were still in flight. Additionally, the user requested a hard cap on parallel synthesis to avoid CPU/DDR5 contention, so the assistant added a `max_parallel_synthesis` config field (default 18) that caps `synth_worker_count` in the pipeline path. After deploying pitune4 with both the PI tuning and the synthesis concurrency cap, the user confirmed it seemed to work well and asked to commit. The assistant then shifted focus to the broader deployment infrastructure: building the latest cuzk using the main Dockerfile (not the rebuild one) and setting up a default memory budget based on available system memory with a ~10 GB safety margin for vast.ai environments. The assistant began exploring the main Dockerfile and memory configuration to implement these changes, marking a transition from pacer tuning to production deployment setup.

The Long Tail of Tuning: From Integral Saturation to Production Deployment in a GPU Dispatch Pacer 2255 words

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