The Deployment That Broke the Cycle: Shipping a PI-Controlled GPU Dispatch Pacer

ssh -p 40612 root@141.0.85.211 'nohup /data/cuzk-pacer1 --config /tmp/cuzk-memtest-config.toml > /data/cuzk-pacer1.log 2>&1 & echo "PID: $!"'
PID: 136407

At first glance, this message looks like a routine deployment command — SSH into a remote server, launch a binary with nohup, redirect logs, and print the process ID. But in the context of the conversation, this single command represents the culmination of an intensive iterative refinement cycle. It is the moment when a new PI-controlled dispatch pacer, designed to stabilize GPU pipeline scheduling in a zero-knowledge proof system, goes live on a production machine. The message is deceptively simple, but the reasoning that led to it is anything but.

The Context: A Pipeline in Turmoil

To understand why this message was written, we must trace the thread of failures that preceded it. The team had been battling GPU underutilization in the cuzk proving engine — a system that synthesizes zero-knowledge proofs and dispatches them to a GPU for accelerated proving. The core problem was that the dispatch mechanism, which decides when and how many synthesized partitions to send to the GPU, was unstable.

The first attempt was a simple semaphore-based throttle that limited total in-flight partitions. This failed because it constrained the absolute number of partitions in the pipeline rather than targeting a specific queue depth of partitions waiting for the GPU. The assistant then implemented a P-controller (proportional control), deployed as cuzk-pctrl1, which replaced the semaphore with a Notify-based two-phase loop: wait for a GPU completion event, then dispatch the full deficit in a burst to intentionally overshoot and converge on a steady state. This proved too aggressive, instantly filling all allocation slots. A dampened version (cuzk-pctrl2) capped the burst size at max(1, min(3, deficit * 0.75)), but the system remained unstable because the deep synthesis pipeline made the raw waiting count a noisy and delayed feedback signal.

The user then proposed a more sophisticated PI controller (proportional-integral control) operating on a smoothed signal like an Exponential Moving Average (EMA) of the waiting count or GPU consumption rate. The assistant implemented the DispatchPacer struct with PI control, GPU rate EMA feed-forward, a bootstrap phase for initial calibration, and a timer-based steady-state pacing loop. This was built and compiled as the pacer1 binary.

The Vicious Cycle: Why the Old Approach Failed

The critical insight that motivated this deployment came from analyzing the pinned memory pool logs of the pctrl2 deployment. The assistant discovered that 355 pinned buffers of ~2.4 GiB each had been allocated over the run, with new allocations still occurring 27 minutes after startup. The pool had 87–89 free buffers at checkout time, yet allocations were still happening — a sign that bursty dispatch was periodically spiking demand beyond the pool's existing capacity.

The user articulated the vicious cycle succinctly in [msg 3470]: late cudaHostAlloc calls cause GPU hiccups, which break the naive P-control pacing. The cycle works like this: bursty dispatch → pool exhaustion → cudaHostAlloc (which serializes through the GPU driver) → GPU timing jitter → pacing estimates become inaccurate → more bursty dispatch. The PI pacer with EMA smoothing was designed to break this cycle by keeping dispatch rate steady, which would keep concurrent synthesis count stable, which would keep the pool within its existing allocation — eliminating new cudaHostAlloc calls after warmup.

The Assumptions Behind the Deployment

This deployment rested on several key assumptions. First, that the PI controller with EMA feed-forward would produce a steadier dispatch rate than the dampened P-controller, even in the presence of GPU timing noise. Second, that the bootstrap phase — which dispatches a fixed number of items at 200ms spacing before the first GPU completion — would provide enough initial data for the EMA to calibrate without causing an instant burst. Third, that the timer-based pacing loop in steady state would converge on a stable dispatch interval that keeps the GPU queue depth near the target without overshooting. Fourth, that the pinned memory pool would stop growing once dispatch steadied, because the peak concurrent synthesis count would stabilize.

There was also an implicit assumption about the workload: that the proof types and partition sizes in the production environment would be consistent enough for the PI gains and EMA parameters (tuned in the code) to work without re-tuning. The assistant had not yet added the synthesis throughput cap with anti-windup — that would come later in the chunk — so the deployment assumed that the PI controller alone, with GPU rate feed-forward, would be sufficient to prevent CPU-side contention from destabilizing the system.

Input Knowledge Required

To understand this message, one needs to know several things. The cuzk system is a GPU-accelerated zero-knowledge proof engine that synthesizes proof partitions and dispatches them to a GPU. The pinned memory pool (PinnedPool) is a zero-copy mechanism that avoids expensive host-to-device transfers by keeping GPU-accessible buffers alive and reusing them across partitions. The --config flag points to a TOML configuration file (/tmp/cuzk-memtest-config.toml) that presumably sets parameters like the GPU queue depth target, PI gains, EMA alpha, and memory budget. The nohup invocation and log redirection indicate a production deployment where the process must survive terminal disconnection and log to a persistent file for later analysis. The remote server at 141.0.85.211 port 40612 is one of the GPU machines in the proving cluster.

Output Knowledge Created

This message produced a running process (PID 136407) on the remote machine, writing logs to /data/cuzk-pacer1.log. It also created a checkpoint in the conversation: the old pctrl2 process had been killed, and the new pacer was now the active deployment. The assistant would later analyze these logs to evaluate whether the PI controller achieved its goals. The deployment also implicitly created a comparison point — the team could now compare pacer1 behavior against the pctrl1 and pctrl2 logs to measure improvement in allocation rate, GPU utilization stability, and dispatch smoothness.

The Thinking Process Visible in the Reasoning

The assistant's reasoning in the preceding messages reveals a sophisticated understanding of control theory applied to systems engineering. The assistant recognized that the pinned pool allocation pattern (allocations still happening late in the run despite abundant free buffers) was a symptom of bursty dispatch, not a pool design flaw. The assistant traced the causal chain from dispatch burst → pool exhaustion → cudaHostAlloc → GPU stall → pacing failure, and correctly identified that fixing the dispatch stability would break the cycle. The assistant also showed awareness of the trade-off between pre-allocating a fixed pool (which would act as a hard concurrency limiter) and relying on the pacer to keep dispatch within existing pool capacity — ultimately choosing to deploy the pacer first and observe, with pre-allocation as a fallback if allocations persisted.

A Pivotal Moment in an Iterative Process

This message is not the end of the story — the user would later identify that the pacer still had issues when synthesis became compute-constrained, leading to the addition of a synthesis throughput cap with anti-windup. But it represents a pivotal moment: the transition from a reactive, burst-based control scheme to a predictive, smoothed control scheme. The deployment command is the point where theory meets practice, where the PI controller leaves the safety of compilation and enters the chaos of production GPU workloads. The PID 136407 is not just a process identifier — it is the latest iteration in a relentless pursuit of stable, high-utilization GPU scheduling.