Wiring Direct GPU Processing Time into the Dispatch Pacer

In the course of a high-stakes optimization session for a GPU-accelerated zero-knowledge proof system, a single edit message — <msg id=3563> — represents the culmination of a deep debugging spiral and the pivot to a fundamentally more correct measurement strategy. The message itself is deceptively brief: [edit] /tmp/czk/extern/cuzk/cuzk-core/src/engine.rs followed by Edit applied successfully. But this edit, which wires a new gpu_processing_total_ns atomic counter and the num_gpu_workers parameter into the DispatchPacer::new() call, is the keystone of an architectural shift in how the system measures GPU throughput for its dispatch pacing controller.

The Problem That Drove This Edit

The story begins with a subtle but critical flaw in the dispatch pacer's GPU rate measurement. The pacer, a PI (proportional-integral) controller responsible for regulating how quickly work items are dispatched to the GPU, relied on an exponential moving average (EMA) of the inter-completion interval — the time between successive GPU job completions. This approach had been repeatedly patched with workarounds: skipping the first completion to avoid pipeline-fill contamination, and only updating the GPU rate when the waiting queue was non-empty to avoid idle-time contamination.

The user identified the fatal flaw in <msg id=3541>: "tracking gpu processing time isn't that simple because there are two lightly pipelined gpu workers, not just one." With two interleaved GPU workers, the queue can be empty while both workers are actively processing. Worker A pops an item and starts; Worker B pops an item and starts. The queue is now empty (waiting = 0), but both workers are busy. When Worker A finishes, the waiting > 0 guard skips the measurement entirely — even though the GPU was fully utilized. The proxy was fundamentally broken.

The Reasoning Behind the New Approach

The assistant's reasoning in <msg id=3542> is a masterclass in iterative problem-solving. The initial instinct was to track the number of workers currently busy using an atomic counter, but this ran into a timing problem: by the time the pacer processes a completion notification, the active counter has already been decremented. The assistant then arrived at a cleaner insight: instead of inferring GPU busyness from queue depth or completion intervals, measure the actual processing duration directly from the GPU workers themselves. Each worker already times its own work — that duration is available in the code as gpu_result.gpu_duration. The key formula is:

effective_dispatch_interval = avg_gpu_processing_time / num_gpu_workers

With two workers each taking one second, the effective consumption rate is two completions per second, so the pacer should dispatch every 0.5 seconds. This formula is immune to both pipeline-fill contamination (the first few items) and idle-time contamination (when the pipeline drains), because the processing time of a single partition is a characteristic of the GPU hardware, not of queue dynamics.

What This Edit Actually Does

Message <msg id=3563> is the edit that completes the wiring of this new measurement architecture. Looking at the sequence of edits that precede it:

Input Knowledge Required

To understand this message, one needs to know the structure of the CUZK proving engine: that it uses a dispatcher task that wakes on timer ticks and GPU completion notifications, that it has multiple GPU workers per device (typically two) that process partitions in a pipelined fashion, and that the DispatchPacer struct maintains an EMA-based feed-forward term alongside a PI feedback correction on the waiting queue depth. One also needs to understand the atomic synchronization pattern: gpu_completion_count is an Arc<AtomicU64> incremented by GPU workers on completion and read by the pacer to compute deltas. The new gpu_processing_total_ns follows the same pattern but accumulates nanoseconds instead of counting events.

Output Knowledge Created

This edit creates a correctly wired measurement pipeline. The GPU workers now atomically add their processing durations to a shared accumulator. The pacer reads the delta on each update cycle, computes an average per-partition processing time, and derives the feed-forward dispatch interval by dividing by the number of workers. This eliminates the need for the waiting > 0 guard, the first-completion skip, and all the other hacks that had accumulated. The measurement is now a direct physical quantity — how long does the GPU take to process one partition — rather than an inferred proxy contaminated by pipeline dynamics.

Assumptions and Potential Pitfalls

The approach assumes that GPU processing time per partition is relatively stable and that the EMA will converge to a meaningful average. It also assumes that dividing by num_gpu_workers is the correct way to derive the dispatch interval — this is true when workers are independent and the GPU can sustain concurrent processing, but it may need adjustment if workers share resources and processing time increases under contention (the assistant acknowledged this in <msg id=3542>, noting that "actual processing time increases slightly under contention — maybe 1.2 seconds per item when both are active instead of 1 second alone"). The EMA naturally captures this effect, so the formula self-corrects.

Significance in the Broader Context

This edit is not the end of the story — the assistant will go on in <msg id=3564> to update all four pacer.update() call sites to pass the new atomic, and later iterations will reveal that the synthesis throughput cap creates a vicious cycle collapse that requires removing the cap entirely and adding re-bootstrap detection. But <msg id=3563> represents the moment when the GPU rate measurement was put on solid theoretical footing, replacing a sequence of fragile heuristics with a direct measurement grounded in the physics of the computation.