Chunk 25.1

In this chunk, the assistant implemented and deployed a PI-controlled dispatch pacer to replace the previous burst-based P-controller. The `DispatchPacer` uses an exponential moving average (EMA) of the GPU inter-completion interval as a feed-forward rate, with a PI correction on the smoothed GPU queue depth error. A bootstrap phase dispatches the target number of items at a fixed spacing before the first GPU completion, then switches to timer-based pacing at the PI-computed interval. The deployment (`pacer1`) showed generally good behavior, but the user identified an edge case: when synthesis is compute-constrained, the pacer drives the dispatch interval below the GPU rate to fill the queue, which floods the system with concurrent synthesis jobs, causing CPU contention and degrading overall throughput. The user noted that the control should optimize for sustained system throughput rather than just queue depth, and must adapt to different proof types (which trigger pinned buffer re‑allocations) and to startup transients where pinned allocations skew initial measurements. To address this, the assistant extended the pacer with a **synthesis throughput cap**: the dispatch rate is clamped to not exceed the measured synthesis completion rate (with a small headroom factor), and the PI integral term is frozen when the cap is active to prevent windup. An atomic counter tracks synthesis completions (incremented by workers after pushing to the GPU queue), and the pacer computes an EMA of the inter-completion interval to derive the sustainable synthesis rate. The cap is gated by a warmup threshold to avoid being overly conservative during startup. This creates a self-regulating loop: if synthesis slows due to contention, the measured rate drops, automatically reducing the dispatch ceiling and relieving CPU pressure. The overarching theme remains the iterative refinement of a complex control system for GPU pipeline scheduling. The team moved from a reactive semaphore to a damped P-controller, then to a PI‑controlled pacer with GPU rate feed‑forward, and finally added a synthesis throughput cap with anti‑windup to handle CPU‑bound scenarios. The pinned memory pool fix remains deployed and effective, but the dispatch logic now incorporates both GPU and synthesis rate measurements to maintain stable, high‑utilization scheduling across varying workloads and system bottlenecks.

The Synthesis Throughput Cap: Closing the Loop on GPU Pipeline Control 2222 words

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