The Three-Phase Benchmark: Eliminating Restart Overhead in GPU Proof Pipeline Measurement

A Status Update That Represents a Design Turning Point

In the course of a high-stakes deployment of the CuZK GPU proving engine for Filecoin, the assistant issued a brief status update that, on its surface, appears mundane: a todowrite call marking three items as completed. The message reads:

[todowrite] {"todos":[{"content":"Restructure benchmark.sh: 3-phase benchmark (5 warmup, 10 timed, 3 cooldown), no daemon restart","priority":"high","status":"completed"},{"content":"Update entrypoint.sh BENCH_PROOFS to match new timed count","priority":"high","status":"completed"},{"content":"Rebuild and push Docker image","priority":"high","status":"completed"}]

This single message at index 3759 is the culmination of a deep redesign of how the team measures GPU proof throughput. To understand why this message matters, one must trace the reasoning that led to it—a chain of decisions about benchmark methodology, pipeline behavior, and the hidden costs of daemon restarts that were silently corrupting performance measurements.

The Problem: Warmup Was Eating the Benchmark

The context for this message begins with a user observation at [msg 3744]: "Due to the much longer warmup we should adjust the benchmark. No restart after PCE warmup, for actual bench round let's do 5 proofs dispatched for warmup, 10 timed for throughput measurment, 3 cooldown."

This request reveals a critical insight about the existing benchmark design. The previous flow had two major flaws. First, it started the daemon in a "warmup mode" with synthesis_concurrency=1 to perform Pre-Compiled Constraint Evaluator (PCE) extraction, then restarted the daemon with full settings. That restart forced the daemon to reload the Structured Reference String (SRS) from disk—a costly operation that could take minutes on GPU instances. The warmup time was "much longer" than anticipated, directly cutting into the time available for actual measurement.

Second, the benchmark dispatched all proofs as a single batch and timed the entire run. This meant the first few proofs—which suffered from pipeline startup effects (cold GPU caches, synthesis pipeline ramp-up, etc.)—were included in the throughput calculation, producing an artificially low measurement. Similarly, the final proofs as the pipeline drained were also counted.

The user's proposed solution was elegant: a three-phase model where only the middle proofs are timed, and the daemon is never restarted between phases. The assistant's reasoning at <msg id=3745 reveals the depth of thinking required to implement this.

The Reasoning Journey: From Simple Edits to Fundamental Redesign

The assistant's reasoning in <msg id=3745 is remarkable for its iterative refinement. It begins by understanding the surface-level request—"no restart after PCE warmup"—but then dives into the deeper implications.

The first insight is that removing the restart means the daemon must start with full concurrency settings from the beginning. This is non-trivial because the previous architecture used the restart as a mechanism to switch from warmup mode (low concurrency for PCE extraction) to benchmark mode (full concurrency for measurement). Without the restart, the daemon needs to handle PCE extraction at full concurrency—which it can, since PCE extraction is a one-time cost regardless of concurrency level.

The second insight is more subtle: running warmup, timed, and cooldown as sequential batches would defeat the purpose of warmup. The assistant wrestles with this extensively:

"if I run the warmup batch and wait for it to complete before starting the timed batch, the pipeline will be empty when timing begins. The whole point of the warmup is to have synthesis and GPU workers already busy so the timed batch runs under realistic load conditions."

This is a profound observation about GPU pipeline measurement. The pipeline—consisting of synthesis (CPU-side circuit construction) and GPU proving—has internal state. When proofs complete, workers become idle. If there's a gap between batches, the pipeline drains, and the next batch suffers the same cold-start penalty as the first. The assistant considers several alternatives:

  1. Single batch of 18 with selective timing: Dispatch all proofs at once, measure only proofs 6-15. This would give true steady-state measurement, but the cuzk-bench batch tool doesn't support per-proof timing extraction from a shell script.
  2. Overlapping batches: Start the timed batch before the warmup batch finishes, keeping the pipeline continuously fed. But this requires complex orchestration that the shell script infrastructure doesn't support.
  3. Sequential batches with minimal gap: Accept a small pipeline drain between batches, relying on the fact that the pipeline will re-warm within the first couple of timed proofs. This is the pragmatic compromise. The assistant settles on option 3, acknowledging the imperfection: "The pipeline will dip slightly between batches but should stabilize within the first couple of proofs in the timed batch, so the 10 measured proofs should still capture mostly steady-state behavior."

Assumptions and Their Implications

Several assumptions underpin this design, and it's worth examining them critically.

Assumption 1: The pipeline drain between batches is negligible. The assistant assumes that the warmup batch fills the pipeline to steady state, and that the gap between the last warmup proof completing and the first timed proof being dispatched is small enough that the pipeline doesn't fully drain. This is reasonable for a fast dispatch mechanism, but it's untested—the actual behavior depends on how quickly the shell script can iterate and dispatch the next batch command.

Assumption 2: Ten timed proofs are sufficient for a stable throughput measurement. The user chose 10 as the timed count. This assumes that the variance between proofs is low enough that 10 samples give a reliable estimate. In GPU proving workloads, variance can come from GPU clock throttling, memory bandwidth contention, and synthesis scheduling jitter. Ten proofs may or may not be enough—it depends on the specific hardware and workload.

Assumption 3: The cooldown phase serves a purpose. The three cooldown proofs are untimed and exist only to let the pipeline drain gracefully. The assistant doesn't question this, but it's worth asking: what value do the cooldown proofs provide? They consume GPU time without producing measurement data. The user likely included them to ensure the benchmark ends cleanly (e.g., to avoid cutting off in-flight proofs), but they represent a 17% overhead on the total proof count.

Assumption 4: The cuzk-bench batch tool's output format is unsuitable for selective timing. The assistant considers modifying the Rust benchmark tool to support selective timing but rejects it as requiring "Rust changes and a rebuild." This is a practical constraint, but it means the measurement methodology is shaped by tool limitations rather than ideal design.

Input Knowledge Required

To fully understand this message, one needs knowledge of:

Output Knowledge Created

This message produces several concrete artifacts:

  1. A restructured benchmark.sh with three-phase execution: warmup (5 proofs, untimed), timed (10 proofs, measured), cooldown (3 proofs, untimed).
  2. A simplified daemon startup that always uses full concurrency settings, eliminating the warmup-mode restart entirely.
  3. An updated entrypoint.sh where BENCH_PROOFS is changed from 12 to 10, reflecting that the positional argument now represents only the timed phase count.
  4. A new Docker image (theuser/curio-cuzk:latest) containing all changes, built and pushed to the registry.

The Thinking Process Visible in the Reasoning

The assistant's reasoning at <msg id=3745 reveals a sophisticated design process. It starts with the user's request and iteratively refines its understanding. Key moments in the reasoning:

Conclusion

This message at index 3759 is far more than a todo list update. It represents the completion of a careful redesign of benchmark methodology driven by real-world observation. The key insight—that daemon restarts for PCE warmup were adding unacceptable overhead—led to a fundamental rethinking of how to measure GPU proof throughput. The resulting three-phase model (warmup, timed, cooldown) with no restart between phases is a more accurate measurement methodology that eliminates startup artifacts from the throughput calculation.

The assistant's willingness to question its own assumptions, consider alternatives, and accept pragmatic compromises is a model of engineering decision-making. The message itself may be brief, but the reasoning behind it is rich with lessons about GPU pipeline measurement, benchmark design, and the importance of understanding the full system context before making changes.