Reading the Waterfall: How TIMELINE Data Exposed the Structural GPU Idle Gap in a Groth16 Proving Pipeline

Introduction

In the middle of an intensive optimization campaign for a Filecoin PoRep Groth16 proof generation pipeline, a single message captures the precise moment when data contradicts expectation. Message [msg 1963] shows an AI assistant staring at benchmark results that refuse to budge, then turning to the project's TIMELINE instrumentation for answers. The thread isolation optimization—carefully designed to prevent CPU contention between synthesis threads and GPU worker threads—has delivered a meager 1.5% improvement (45.4s per proof vs. 46.1s baseline). The assistant's response is not frustration but curiosity: why didn't it work? The answer, buried in the TIMELINE events, will fundamentally reshape the pipeline architecture.

This article examines that message in detail: what led to it, what it reveals, the assumptions it challenges, and the knowledge it generates. It is a case study in data-driven debugging—where the absence of expected improvement is itself the most valuable signal.

The Thread Isolation Hypothesis

To understand the message, one must understand what came before. The cuzk proving engine is a complex pipeline that synthesizes Groth16 proofs for Filecoin's Proof of Replication (PoRep). It uses a Rust-based synthesis stage running on CPU via the Rayon parallel work-stealing thread pool, followed by a GPU stage that performs multi-scalar multiplication (MSM) and number-theoretic transform (NTT) operations. The GPU stage itself uses a C++ thread pool (groth16_pool) for preprocessing and batched operations like b_g2_msm.

The assistant's hypothesis was straightforward: when both synthesis and GPU work run concurrently, the C++ GPU thread pool competes with Rayon synthesis threads for CPU cores. On a 96-core machine with hyperthreading (192 logical processors), the default configuration gave all threads to Rayon, leaving the GPU pool to fight for scraps. The fix was to limit the GPU thread pool to 32 threads and Rayon to 64 threads, reserving the remaining cores for the operating system and reducing contention.

The assistant implemented this by modifying the C++ code to use lazy initialization for groth16_pool (see [msg 1934]–[msg 1946]), replacing a static constructor with std::call_once so that the CUZK_GPU_THREADS environment variable could be set from Rust's main() before the pool was created. This was necessary because C++ static constructors run at library load time, before Rust's main() executes. The fix was elegant, the reasoning sound, and the expected outcome was a measurable throughput improvement.

The Benchmark That Refused to Cooperate

Message [msg 1962] shows the benchmark run that produced the data. The daemon was started with the isolated configuration (synthesis_concurrency=2, rayon_threads=64, gpu_threads=32) and five proofs were submitted with concurrency=2:

[1/5] COMPLETED — 108.9s (prove=33763 ms, queue=27010 ms)
[2/5] COMPLETED — 74.4s (prove=26732 ms, queue=256 ms)
[3/5] COMPLETED — 75.3s (prove=26821 ms, queue=2655 ms)
[4/5] COMPLETED — 76.2s (prove=26415 ms, queue=659 ms)
[5/5] COMPLETED — 77.2s (prove=26207 ms, ...

The first proof is an outlier at 108.9s with 27s of queue wait—a cold-start penalty from loading the Precomputed Constraint Evaluation (PCE) cache. The remaining four proofs settle into a steady state around 75–77s total, with prove times (the actual GPU proving phase) consistently around 26s. The assistant calculates the steady-state throughput at 45.4s per proof, compared to the baseline of 46.1s.

This is the moment captured in [msg 1963]. The assistant's reaction—"Interesting! The results are very close to baseline"—is understated but significant. An optimization that should have freed up CPU resources has delivered essentially zero benefit. The first proof's 27s queue wait suggests PCE caching is a factor, but that's a cold-start issue, not a throughput issue. The real question is: why doesn't thread isolation help steady-state throughput?

Diving into the Waterfall

The assistant's next move is decisive: "Let me also collect the waterfall data. Let me look at the timeline events to understand what's happening." The TIMELINE instrumentation is a custom event-logging system built into the cuzk engine that records key pipeline transitions with microsecond timestamps. The assistant greps the log file for TIMELINE events:

TIMELINE,64837,SYNTH_START,8368628e-b072-4a10-bf5b-a466d429b2ca,kind=porep-c2
TIMELINE,65108,SYNTH_START,218214c8-dfec-4f41-bbc9-06405b621d51,kind=porep-c2
TIMELINE,112308,SYNTH_END,218214c8-dfec-4f41-bbc9-06405b621d51,synth_ms=46972
TIMELINE,112308,CHAN_SEND,218214c8-dfec-4f41-bbc9-06405b621d51,
TIMELINE,112311,SYNTH_END,8368628e-b072-4a10-bf5b-a466d429b2ca,synth_ms=47238
TIMELINE,112311,CHAN_SEND,8368628e-b072-4a10-bf5b-a466d429b2ca,
TIMELINE,112314,GPU_PICKUP,218214c8-dfec-4f41-bbc9-06405b621d...

Each event is a comma-separated record: a timestamp in milliseconds since some epoch, an event type, a UUID identifying the job, and optional details. The format is designed for machine parsing and waterfall visualization.

The data tells a stark story. Two synthesis jobs start at timestamps 64837 and 65108—just 271ms apart, confirming they launch nearly simultaneously under concurrency=2. Then, 47 seconds later, both finish within 3ms of each other: job 218214c8 at timestamp 112308 (synth_ms=46972) and job 8368628e at timestamp 112311 (synth_ms=47238). Both immediately transition to CHAN_SEND, and the GPU picks up the first job at timestamp 112314—just 3ms after the channel send.

This is the "thundering herd" pattern in full display. All synthesis work completes at virtually the same instant, creating a batch that floods the GPU channel. The GPU, which has been idle for 47 seconds, suddenly receives all jobs at once.

What the Data Reveals

The TIMELINE data exposes two critical facts that the assistant's mental model had not yet incorporated:

First, synthesis takes ~47 seconds, not ~4 seconds. The assistant had been operating under the assumption that individual partitions were small, independent work units that could be dispatched to the GPU as they completed. The synth_ms values of 46972 and 47238 show that the full synthesis phase—including witness generation and SpMV evaluation for all 10 partitions of a PoRep C2 proof—takes nearly 47 seconds. This is not a collection of fast, independent jobs; it is a monolithic parallel computation where all partitions run concurrently via Rayon and finish together.

Second, the GPU is structurally idle during synthesis. The gap between SYNTH_START (64837) and GPU_PICKUP (112314) is 47,477ms—over 47 seconds of GPU idle time per proof. The GPU only works during the ~26s "prove" phase measured by the benchmark. This means GPU utilization is roughly 26/(47+26) = 35% in steady state, assuming no overlap between synthesis and GPU work. The thread isolation optimization could not possibly address this because the bottleneck is not CPU contention—it is the sequential handoff from CPU synthesis to GPU proving.

The assistant does not explicitly state these conclusions in [msg 1963]—the message is the act of gathering data, not the analysis of it. But the TIMELINE output is the raw material from which these insights will be extracted. The message is a pivot point: the assistant stops optimizing parameters and starts understanding the pipeline's fundamental structure.

Assumptions and Blind Spots

The message reveals several assumptions that were guiding the optimization effort:

  1. Partitions are independent ~4s work units. This assumption drove the entire thread isolation strategy. If partitions were fast and independent, the GPU could start working on completed partitions while others were still synthesizing, and thread isolation would help by preventing contention. The TIMELINE data disproves this: all partitions finish together because they run in parallel, not sequentially.
  2. CPU contention is the primary bottleneck. The assistant believed that synthesis threads and GPU threads were fighting for CPU cores, and that separating them would improve throughput. The benchmark results show this is not the case—at least not at the current level of concurrency.
  3. The queue wait on the first proof is a PCE caching issue. This is likely correct, but it is a cold-start problem that disappears after the first proof. It does not explain the steady-state throughput. The most significant blind spot is the assumption about partition independence. This assumption was so deeply embedded that the assistant had designed the thread isolation optimization around it. The TIMELINE data does not directly contradict it—it simply shows that synthesis takes 47 seconds. The realization that those 47 seconds encompass 10 partitions running in parallel, and that the partitions are not independent work units but tightly coupled through Rayon's work-stealing, will come later in the conversation when the user corrects the assistant's understanding.

Knowledge Created

Message [msg 1963] generates several pieces of actionable knowledge:

The Path Forward

Message [msg 1963] does not contain the solution to the GPU idle problem. It does not propose per-partition dispatch or cross-sector pipelining. It is simply the moment of recognition that the current optimization strategy has hit a wall, and that a deeper understanding of the pipeline is required.

The TIMELINE data gathered in this message will directly inform the Phase 7 design that follows. Once the user corrects the assistant's understanding of partition timing—revealing that each partition takes ~32-37s and they all run in parallel via Rayon—the pieces will fall into place. The solution is to break the "10 circuits as a batch" abstraction, treat each partition as an independent work unit, and dispatch completed partitions to the GPU one-by-one. This eliminates the thundering herd, reduces memory pressure from 10 partitions to 1-2, and enables cross-sector pipelining where Sector B's synthesis begins while Sector A's partitions are still being GPU-proved.

But none of that is visible in [msg 1963]. What is visible is a developer doing the hardest and most important work in performance engineering: looking at data that contradicts expectations, resisting the urge to explain it away, and digging deeper until the true structure of the problem reveals itself. The TIMELINE events in this message are not just log lines—they are the first glimmers of a new understanding that will reshape the entire proving pipeline.