The Waterfall That Revealed a Bottleneck Shift: Diagnosing Parallel Synthesis in the cuzk Proving Engine

Introduction

In high-performance proving systems for zero-knowledge proofs, the difference between a well-tuned pipeline and a merely functional one often comes down to understanding where the time goes. This article examines a single diagnostic message from an opencode coding session — message index 1874 — in which an AI assistant runs a waterfall timeline visualization tool to inspect the behavior of a newly implemented parallel synthesis pipeline in the cuzk SNARK proving engine. The message is deceptively simple: a bash command executing a Python script against a log file. But the context surrounding it reveals a pivotal moment in a multi-round optimization effort, where a promising architectural change (parallel synthesis) collided with the messy reality of CPU resource contention.

Context: The Optimization Arc

To understand why message 1874 was written, we must trace the arc of the optimization effort that preceded it. The cuzk proving engine generates Groth16 proofs for Filecoin's Proof-of-Replication (PoRep) protocol. The proving pipeline has two main stages: CPU-bound synthesis (where circuit constraints are evaluated to produce witness vectors) and GPU-bound proving (where multi-scalar multiplication and number-theoretic transform operations produce the final proof). Earlier benchmarking in the session had revealed a structural problem: synthesis took approximately 39 seconds per proof, while GPU proving took only 27 seconds. Because synthesis was strictly sequential — each proof's synthesis began only after the previous proof's synthesis completed — the GPU was forced to idle for 12–14 seconds between proofs. GPU utilization languished at 70.9%.

The assistant diagnosed this GPU idle gap using a custom "waterfall" instrumentation that recorded wall-clock timestamps for synthesis start, synthesis end, GPU pickup, and GPU completion events. The waterfall visualization made the problem unmistakable: a repeating pattern of SSSSSSSSSGGGGGGGG where each proof's synthesis block (S) and GPU block (G) were serialized, with the GPU waiting for the next synthesis to finish. The fix seemed straightforward: run multiple synthesis tasks concurrently so the GPU always has work waiting.

The assistant implemented parallel synthesis by refactoring the engine's synthesis task loop to use a tokio::sync::Semaphore, controlled by a new synthesis_concurrency configuration parameter. The first benchmark with synthesis_concurrency=2 and client concurrency -j 3 (three in-flight proof requests) produced a disappointing result: 44.7 seconds per proof versus 45.3 seconds per proof for the sequential baseline — a mere 1.3% improvement, well within noise.

The Subject Message: A Diagnostic Pivot

Message 1874 is the assistant's response to this underwhelming result. After the first parallel synthesis benchmark produced only marginal improvement, the assistant ran a second, longer benchmark with 8 proofs (instead of 5) to gather more data. Message 1874 is the command that renders the waterfall visualization from this second benchmark's timeline log:

python3 /tmp/cuzk-waterfall.py /tmp/cuzk-parallel.log

The output shows a timeline event table beginning at timestamp 337.83 seconds into the daemon's lifetime, with job cd1331a1 starting synthesis at that point. The output is truncated in the conversation, but the key data is present: synthesis start, synthesis end (40.3 seconds later), channel send, and GPU pickup events.

This message is purely diagnostic. It produces no code changes, no configuration updates, no architectural decisions. It is an act of measurement — the assistant reaching for its primary diagnostic tool to understand why a theoretically sound optimization failed to deliver.

Why This Message Was Written

The assistant wrote this message because the first parallel synthesis benchmark raised more questions than it answered. The throughput was essentially unchanged, but the reasons were unclear. Several hypotheses competed:

  1. Synthesis time inflation: Perhaps running two syntheses concurrently caused each to slow down due to CPU or memory contention, negating the parallelism benefit.
  2. Client-side starvation: Perhaps the benchmark tool's concurrency level (-j 3) was insufficient to keep the pipeline fed, so the GPU still waited between proofs.
  3. GPU time inflation: Perhaps the GPU proving step itself slowed down because its CPU-bound sub-steps (notably b_g2_msm) competed with synthesis for CPU cores.
  4. Scheduling artifacts: Perhaps the semaphore or channel configuration created unexpected ordering delays. The assistant needed the waterfall data to distinguish between these hypotheses. The first benchmark's waterfall (from message 1872) had shown GPU utilization improving from 70.9% to 86.5%, but with concerning signs: synthesis times for concurrently running proofs were inflated (one took 54 seconds versus 38 seconds when run alone), and GPU prove time had increased from 27 seconds to 32 seconds on average. The second, longer benchmark was designed to produce a cleaner dataset with more proofs to average over, reducing noise.

Input Knowledge Required

To understand this message, one needs knowledge of several layers of the system:

  1. The cuzk proving pipeline architecture: Understanding that proof generation has two stages — CPU synthesis and GPU proving — that run concurrently in a producer-consumer pattern. Synthesis produces circuit witness data; GPU proving consumes it to produce the final Groth16 proof.
  2. The waterfall instrumentation: The assistant had earlier implemented a custom tracing system that records TIMELINE events with nanosecond-precision timestamps to a log file. The cuzk-waterfall.py script parses these events and renders a human-readable timeline table and a visual Gantt-like chart showing the overlap between synthesis and GPU phases for each proof.
  3. The parallel synthesis implementation: The assistant had just refactored the engine to use a tokio::sync::Semaphore allowing multiple process_batch() calls to run concurrently. The synthesis_concurrency parameter controls how many permits the semaphore grants.
  4. The benchmark methodology: The cuzk-bench batch command sends proof requests to the daemon with a specified concurrency (-j flag). The daemon processes them through its pipeline and reports per-proof timing. The -c flag controls total proof count.
  5. The hardware context: The system has 96 CPU cores and 754 GiB of RAM. The GPU is an unspecified model that takes ~27 seconds per proof under ideal conditions. The machine has ample memory for two concurrent syntheses (~272 GiB for synthesis data plus ~70 GiB for PCE and SRS structures).

The Thinking Process Visible in the Message

While the message itself is just a command execution, the surrounding context reveals the assistant's reasoning. In message 1873 (immediately preceding), the assistant analyzed the first waterfall and identified three problems:

  1. Synthesis time inflation: "When 2 syntheses run concurrently, each takes longer due to CPU/memory contention. P1 alone = 38s, but P2 running concurrently = 54s (42% slower)."
  2. GPU time increase: "GPU time also increased: avg 32s vs 27s before. The b_g2_msm CPU step (runs during GPU phase) is competing with synthesis for CPU."
  3. A persistent gap: "One big 25s gap between P3 GPU end and P5 GPU start — caused by P5's synthesis taking so long due to contention." The assistant then decided to "run a cleaner test with more proofs" — this is the benchmark whose waterfall appears in message 1874. The thinking was: the first benchmark had only 5 proofs, which might not be enough to see steady-state behavior. With 8 proofs, the pipeline would reach a stable regime and the waterfall would reveal whether the contention pattern was systematic or an artifact of the short run.

What the Waterfall Revealed

The waterfall output in message 1874 shows the timeline beginning at 337.83 seconds. This is late in the daemon's lifetime — the first proof started around 67 seconds earlier (from the first benchmark). The job cd1331a1 shows synthesis taking 40.3 seconds, which is close to the baseline single-synthesis time of ~39 seconds. This suggests that by this point in the run, the contention pattern had stabilized and synthesis was no longer being inflated as severely as in the first benchmark.

However, the full analysis (which continues in messages 1875–1882) reveals that the parallel synthesis pipeline, while successfully eliminating GPU idle time (achieving 99.3% GPU utilization in some configurations), produced only modest throughput improvement. The root cause was a bottleneck shift: by saturating the GPU, the assistant had merely exposed the CPU as the new bottleneck. The system's 96 cores are a shared resource that cannot be fully dedicated to synthesis without impacting the GPU prover's CPU-bound work.

Assumptions and Their Consequences

The parallel synthesis implementation rested on several assumptions that proved partially incorrect:

  1. Assumption: CPU cores are abundant enough for concurrent synthesis. The system has 96 cores, and a single synthesis uses rayon's default thread pool to parallelize across all available cores. The assumption was that two syntheses could each use ~48 cores with acceptable slowdown. In practice, the slowdown was 42% for the second synthesis, and the GPU's b_g2_msm step (which also uses rayon) suffered even more severely.
  2. Assumption: The GPU's CPU-bound work is negligible. The b_g2_msm step runs on the CPU during the GPU proving phase and uses multi-threaded Pippenger for multi-scalar multiplication. This step takes approximately 25 seconds of CPU time per proof. When two syntheses are also consuming all CPU cores, this step slows dramatically, inflating GPU prove time from 27 seconds to 37+ seconds.
  3. Assumption: Client concurrency can be tuned independently. The assistant tried three client concurrency levels: -j 2 (pipeline starved, 42.2s/proof), -j 3 (sweet spot, 43.0s/proof), and -j 4 (catastrophic contention, 60.2s/proof). The interaction between client concurrency and synthesis concurrency proved complex and sensitive.

Output Knowledge Created

This message produced critical diagnostic data that shaped the subsequent direction of the optimization effort:

  1. Quantified the bottleneck shift: The waterfall confirmed that parallel synthesis eliminated GPU idle time but shifted the bottleneck to CPU contention. This reframed the problem from "how do we keep the GPU busy?" to "how do we reduce absolute CPU time or decouple CPU workloads?"
  2. Established the waterfall as a diagnostic tool: The waterfall visualization proved essential for understanding pipeline dynamics. It revealed not just aggregate statistics but the temporal structure of contention — when syntheses overlapped, how long each phase took, and where gaps appeared.
  3. Identified the need for deeper optimizations: The modest 5–7% throughput improvement from parallel synthesis, despite 99.3% GPU utilization, made it clear that further gains required reducing the absolute CPU time of synthesis (e.g., via specialized MatVec kernels, pre-sorted SRS, or other Phase 5 Wave 2/3 optimizations) rather than adding more parallelism to the existing architecture.

Conclusion

Message 1874 is a single diagnostic command in a long optimization conversation, but it represents a critical juncture: the moment when a promising optimization (parallel synthesis) was confronted with empirical reality. The waterfall visualization it produced revealed that the system had hit a law of diminishing returns — saturating the GPU merely exposed the CPU as the new bottleneck. The message embodies a key engineering principle: before optimizing further, you must measure accurately. The assistant's disciplined use of instrumentation to understand why an optimization failed, rather than simply trying different parameters, is the hallmark of a systematic approach to performance engineering. The waterfall didn't just show what happened — it showed why it happened, and in doing so, pointed the way toward the next generation of optimizations.