The Moment of Truth: Testing Parallel Synthesis in the cuzk Proving Engine

Introduction

In the long arc of optimizing a high-performance SNARK proving engine, there comes a moment when months of analysis, instrumentation, and implementation converge into a single action: killing the old process and starting the new one. Message [msg 1867] captures exactly that moment. It is deceptively brief — just two shell commands — but it represents the culmination of a deep diagnostic journey into the GPU idle gap that had been plaguing the cuzk proving engine's throughput.

The message reads in its entirety:

[assistant] Now let's test. First, kill the old daemon, then start with synthesis_concurrency=2: [bash] pkill -9 -f cuzk-daemon 2>/dev/null; sleep 2

This is the pivot point between implementation and validation. The assistant has just finished refactoring the engine's synthesis task loop to use a tokio::sync::Semaphore, enabling multiple proofs to be synthesized concurrently. Now it must discover whether the hypothesis — that parallel synthesis would eliminate the structural GPU idle gap — holds up under real hardware.

The GPU Idle Gap: A Structural Bottleneck

To understand why this message matters, one must understand what came before it. The cuzk proving engine, part of the Filecoin PoRep (Proof-of-Replication) pipeline, generates Groth16 proofs through a two-stage process: CPU-bound synthesis (building the Rank-1 Constraint System representation of the circuit) followed by GPU-bound proving (multi-scalar multiplication and number-theoretic transform operations). In the standard pipeline, these stages were orchestrated by a single synthesis task running in a tokio::spawn loop, processing proofs strictly sequentially.

The waterfall timeline instrumentation — itself a recent addition — had revealed the problem with brutal clarity. Each proof cycle showed synthesis consuming approximately 38–41 seconds of CPU time, while GPU proving required only 26–28 seconds. Because synthesis was strictly sequential, the GPU would finish proof N and then sit idle for 12–14 seconds waiting for synthesis of proof N+1 to complete. The GPU utilization was a mere 70.9%, with the remaining 29% representing pure structural idle time — silicon waiting on software.

This was not a hardware limitation. The machine boasted 754 GiB of RAM and 96 CPU cores, with ample headroom for concurrent work. The sequential synthesis was an architectural choice baked into the engine's design: a single async task that pulled requests from a scheduler, called process_batch().await, and blocked until synthesis completed before moving to the next request. The synthesis_lookahead channel with capacity 1 provided a small buffer but could not overcome the fundamental serialization.

The Implementation: Parallel Synthesis via Semaphore

The fix, implemented in the messages immediately preceding [msg 1867], was conceptually clean. Rather than awaiting process_batch directly in the synthesis loop, the assistant refactored the code to spawn each synthesis as a separate tokio task, gated by a tokio::sync::Semaphore with permits equal to the new synthesis_concurrency configuration parameter. The synthesis loop would acquire a semaphore permit, spawn the synthesis task, and immediately return to pull the next request from the scheduler. The semaphore ensured that at most N syntheses ran concurrently, preventing memory exhaustion.

The config parameter was added to PipelineConfig with a default value of 1 (preserving the original sequential behavior), and the example configuration was updated to document the new option. The build succeeded cleanly — the compiler produced no errors, only the usual warnings from the upstream bellperson dependency.

The Message: A Deliberate Transition

Message [msg 1867] is the bridge between that implementation and the empirical validation that follows. Its brevity is deceptive. The assistant writes "Now let's test" with the casual confidence of a developer who has done this many times, but the stakes are real. The parallel synthesis change touches the core orchestration logic of the proving engine. A bug could cause silent data corruption, deadlocks, or memory exhaustion. The pkill -9 is not mere housekeeping — it ensures that no stale daemon process from the previous benchmark run lingers, which could cause port conflicts, file descriptor leaks, or confusing multi-process interactions. The sleep 2 after the kill provides a safety margin for the OS to release resources.

The choice of SIGKILL (-9) rather than a gentler signal like SIGTERM is telling. In a production deployment, one would prefer graceful shutdown — flushing buffers, completing in-flight proofs, and writing checkpoint state. But this is a development and benchmarking context. The old daemon was started with nohup and its stdout/stderr redirected to log files. A clean shutdown protocol would add complexity without benefit when the goal is rapid iteration. The assistant is optimizing for developer velocity, not production hygiene.

The command also reveals an assumption: that the new daemon binary is already built and ready at ./target/release/cuzk-daemon. The build in [msg 1865] and [msg 1866] confirmed compilation success, but the assistant does not rebuild here. This assumes the binary is still present and correct — a reasonable assumption given the build completed just moments ago, but one that could fail if the build was interrupted or the filesystem state changed.

The Hypothesis Being Tested

The assistant is testing a specific hypothesis: that setting synthesis_concurrency=2 will allow two proofs to be synthesized concurrently, reducing the inter-proof interval from ~39 seconds (one synthesis) to ~20 seconds (two overlapping syntheses). Since the GPU time per proof is ~27 seconds, a new proof would arrive every 20 seconds — faster than the GPU can consume them — and the GPU would never idle. The predicted outcome was GPU utilization approaching 100% and throughput improving by approximately the ratio of the GPU idle gap: from ~45 seconds per proof to ~33 seconds per proof, a ~27% improvement.

This hypothesis rested on several assumptions:

  1. Sufficient memory: Each synthesis holds approximately 136 GiB of intermediate data (10 partitions). Two concurrent syntheses would require ~272 GiB, plus PCE (26 GiB) and SRS (44 GiB), totaling ~342 GiB. The machine's 754 GiB provided ample headroom, but memory bandwidth and NUMA effects could still cause contention.
  2. CPU core availability: Two concurrent syntheses would need to share the 96 CPU cores. If the GPU proving step also required significant CPU work (particularly the b_g2_msm operation), contention could inflate both synthesis and GPU times.
  3. No hidden serialization: The semaphore approach assumed that the synthesis tasks were truly independent — that they did not contend on shared locks, global allocators, or I/O resources like SRS file access.
  4. The GPU channel provides sufficient backpressure: The bounded channel between synthesis and GPU tasks would naturally queue completed proofs, preventing the GPU from being overwhelmed.

What Followed: The Reality Check

The benchmarks that follow [msg 1867] — in subsequent messages — reveal a more nuanced outcome. With synthesis_concurrency=2 and client concurrency -j 3, GPU utilization jumped to 99.3%, effectively eliminating the idle gap. The hypothesis about GPU saturation was confirmed.

However, the throughput improvement was modest: from ~45.3 seconds per proof to ~42.2 seconds per proof, a mere 5–7% gain rather than the predicted ~27%. The root cause was CPU resource contention. Running two full 10-partition syntheses simultaneously competed with the GPU prover's CPU-intensive b_g2_msm step. Both synthesis and GPU times inflated, eating most of the theoretical gain. Over-aggressive client concurrency (-j 4) led to catastrophic contention at 60.2 seconds per proof, while insufficient depth (-j 2) left the pipeline starved.

This is a textbook case of Amdahl's Law: parallelizing one stage of a pipeline merely shifts the bottleneck to the next constrained resource. The GPU idle gap was closed, but only by exposing a CPU contention bottleneck that had previously been hidden. The system's 96 cores are a shared resource, and dedicating more of them to synthesis means fewer are available for the GPU's CPU-bound work.

The Deeper Lesson: Instrumentation as a Diagnostic Tool

The waterfall timeline instrumentation that enabled this diagnosis proved critical not just for identifying the original GPU idle gap, but for understanding why the fix fell short of expectations. Without wall-clock timestamps for each synthesis and GPU step, the CPU contention would have been invisible — the assistant might have concluded that parallel synthesis simply didn't work, or that the implementation had a bug. Instead, the instrumentation revealed that both synthesis and GPU times increased under concurrency, pointing directly to shared resource contention.

This illustrates a broader principle in systems optimization: you cannot fix what you cannot measure, and you cannot understand a fix's failure without detailed measurement. The assistant's investment in instrumentation — adding TIMELINE events at synthesis start/end, GPU pickup/start/end, and channel send operations — paid dividends immediately in diagnosing the parallel synthesis results.

Conclusion

Message [msg 1867] is a single line in a conversation spanning hundreds of messages, but it captures the essence of disciplined systems engineering: form a hypothesis, implement a change, and test it rigorously. The assistant's casual "Now let's test" belies the depth of analysis that preceded it — the waterfall instrumentation, the memory accounting, the config refactoring, the semaphore implementation. And the results that follow, while not the unqualified success the hypothesis predicted, are arguably more valuable: they reveal the true nature of the bottleneck and point toward the next set of optimizations (reducing absolute CPU time of synthesis via specialized MatVec and pre-sorted SRS, rather than adding more parallelism).

In the end, the message is a testament to the iterative nature of performance engineering. Each optimization reveals the next bottleneck. The GPU idle gap is closed, but only to reveal CPU contention. That contention will be addressed in turn, and yet another bottleneck will emerge. The process never ends — but each cycle leaves the system faster and better understood than before.