The Moment of Deployment: A PI Controller Tuning Hits Production

[assistant] Baseline. Starting pitune1:
[bash] ssh -p 40612 root@141.0.85.211 'nohup /data/cuzk-pitune1 --config /tmp/cuzk-memtest-config.toml > /data/cuzk-pitune1.log 2>&1 & echo "started PID $!"'
started PID 166807

On its surface, this message is unremarkable: a remote SSH command to start a freshly built binary on a production server. But in the context of the opencode session, it represents the culmination of a deep control-theory debugging session, a carefully reasoned redesign of a PI controller's tuning parameters, and the moment when a hypothesis about integral saturation meets the reality of live GPU workloads. The message is the "flip the switch" instant — the point where analysis, code changes, compilation, containerization, and deployment all converge into a single nohup command.

The Dispatch Pacer: A Brief History

To understand why this message matters, one must understand what came before it. The assistant had been iterating on a DispatchPacer — a proportional-integral (PI) controller that regulates the rate at which synthesized proof partitions are dispatched to GPU workers. The pacer's job is to maintain a target number of partitions waiting in the GPU queue, balancing two competing pressures: keeping the GPU fed so it never idles, and not overwhelming the memory budget by dispatching too many concurrent syntheses.

The pacer uses a feed-forward term (the measured GPU processing time per partition, divided by the number of workers) as a baseline dispatch rate, then applies a PI correction based on the error between the target queue depth and the exponentially-weighted moving average (EMA) of the actual queue depth. The correction feeds into a rate_mult multiplier: a correction of 0 means dispatch at exactly the GPU consumption rate; positive means dispatch faster to fill the queue; negative means dispatch slower to let it drain.

The previous iteration, synthcap3, had introduced a re-bootstrap mechanism and removed a problematic synthesis throughput cap that created a vicious cycle of collapse. But the user reported a new problem: whenever the system "slammed into the memory ceiling," the integral term went deeply negative, causing the pipeline to fully drain before resuming synthesis — essentially starting from scratch. The integral was almost always saturated, providing no useful control authority.

Diagnosing Integral Saturation

The assistant's analysis, visible in the messages leading up to this deployment, reveals a careful mathematical dissection of the problem. With a target queue depth of 8 and a raw error range of [-20, +8], the old parameters (kp=0.1, ki=0.008, max_integral=±20) produced a system where the proportional term dominated and the integral was both too slow to accumulate usefully and too extreme when it did.

The key insight was that the raw error was unscaled. An error of +8 (empty queue) produced kp * 8 = 0.8 correction — near the top of the useful range — while an error of -12 (overfull queue) produced kp * (-12) = -1.2, driving rate_mult below zero and clamping to 0.1 (a 10× slowdown). The integral, meanwhile, took over 300 seconds to reach its positive limit and contributed only 0.16 to the correction even at saturation. But when the queue overfilled, the large negative error rapidly drove the integral to -20, and that deeply negative integral persisted long after the burst was consumed, suppressing dispatch for extended periods.

The Fix: Normalized Error and Asymmetric Clamping

The assistant implemented four coordinated changes for pitune1:

1. Error normalization. By dividing the error by the target queue depth, the error range was mapped to approximately [-1, +1] under normal operating conditions. This made the P and I terms scale-invariant and allowed the controller to behave consistently regardless of the target value.

2. Adjusted proportional gain. With normalized error, kp=0.5 produces a correction of 0.5 * 1.0 = 0.5 when the queue is empty (50% faster dispatch) and 0.5 * (-1.0) = -0.5 when the queue is double the target (50% slower). This is actually gentler than the old kp=0.1 with raw error of ±8, which produced corrections of ±0.8. The normalization effectively tamed the P term's overreaction.

3. Asymmetric integral clamping. The integral was given a positive limit of 2.0 and a negative limit of -0.5. This was the critical insight: slowing down too aggressively is dangerous because it drains the pipeline and starves the GPU, while speeding up too aggressively is self-correcting (the memory budget backpressure will block allocation). The asymmetry encodes the engineering judgment that "too slow" is a worse failure mode than "too fast."

4. Tightened rate_mult clamp. The lower bound was raised from 0.1 to 0.3, ensuring dispatch never slows to less than one-third of the GPU consumption rate. This prevents the pipeline from ever fully stalling.

The assistant verified these choices with a mental simulation covering four operating regimes — steady state, half-empty, empty, and overfull — and confirmed that the integral now acts as a "gentle slow-drift corrector" while the P term handles fast response.

The Deployment Pipeline

The deployment itself is a study in operational discipline. The assistant:

  1. Edited the source code in engine.rs with four targeted edits (normalization, constructor values, integral accumulation, interval calculation).
  2. Compiled with cargo check to verify correctness.
  3. Built a Docker image using Dockerfile.cuzk-rebuild with --no-cache to ensure a clean build.
  4. Extracted the binary from the image using docker create, docker cp, and docker rm.
  5. Copied the binary to the remote server at 141.0.85.211:40612 via scp.
  6. Killed the old process (synthcap3, PID 158698) and waited for pinned memory to be freed — a full 60 seconds elapsed as memory dropped from 549 GiB to 230 GiB used.
  7. Verified the system was clean with ps aux and free -g.
  8. Finally, started the new binary with nohup, redirecting output to a log file. The "Baseline" in the message refers to this clean state: the old process is dead, memory has been reclaimed, and the system is ready for the new controller to take over.

Input and Output Knowledge

The input knowledge required to understand this message includes: PI controller theory (proportional and integral terms, error normalization, integral windup and saturation), the architecture of the CuZK proving pipeline (GPU workers, synthesis dispatch, queue depth management, memory budget backpressure), the deployment infrastructure (Docker, scp, SSH, nohup), and the specific history of the pacer's evolution through synthcap3 and earlier iterations.

The output knowledge created by this message is the pitune1 binary running on the remote server, ready to process proof requests under the new PI tuning regime. The log file at /data/cuzk-pitune1.log will capture the pacer's behavior — status logs showing queue depth, EMA, correction terms, and rate multipliers — providing the data needed to validate or refute the tuning hypotheses.

What This Message Reveals About the Engineering Process

This message is a microcosm of the entire opencode session's engineering philosophy. The assistant does not merely deploy code; it deploys a hypothesis. The PI tuning parameters — kp=0.5, ki=0.02, max_integral_pos=2.0, max_integral_neg=-0.5, rate_mult clamped to [0.3, 5.0] — are not arbitrary numbers. They are the output of a reasoning chain that started with the user's observation of integral saturation, proceeded through mathematical analysis of the controller's dynamics, identified the root cause (unscaled error and symmetric clamping), and produced a targeted fix.

The message also reveals the tight feedback loop that characterizes this work. Within minutes of the user reporting a problem, the assistant had committed the previous iteration, analyzed the issue, designed a fix, implemented it, compiled it, containerized it, deployed it, and started it. The SSH command is the last step in a cycle that can repeat as many times as needed — and indeed, the chunk summary tells us that pitune1 through pitune4 followed in quick succession as each tuning iteration was tested and refined.

The "Baseline" remark, casual as it is, signals an important operational awareness: the assistant knows that starting a new controller requires a clean slate — no residual state from the previous process, no pinned memory still allocated, no zombie processes. This is the mark of an engineer who has learned, through hard experience, that deployment hygiene matters as much as algorithmic correctness.

The Deeper Pattern: Control Theory as Systems Engineering

At a deeper level, this message illustrates how control theory concepts migrate from textbooks into production systems. The PI controller here is not an academic exercise — it is regulating a real GPU pipeline processing real proof workloads, and its parameters directly affect throughput, latency, and memory pressure. The assistant's reasoning about integral saturation, error normalization, and asymmetric clamping draws directly on standard control theory techniques (anti-windup, gain scheduling, output clamping) but applies them in a domain-specific context where the cost of integral windup is measured in stalled GPU pipelines and wasted compute cycles.

The pitune1 deployment is therefore not just a software update. It is an experiment designed to test a specific hypothesis: that normalizing the error and asymmetrically clamping the integral will prevent the pipeline-draining collapse while maintaining responsive control. The experiment's results will be visible in the logs within minutes, and the next iteration — pitune2, pitune3, pitune4 — will refine the parameters further based on those results. This is control theory as practiced in the wild: iterative, empirical, and grounded in the messy reality of production systems.