The Waterfall That Revealed the Gap: Diagnosing GPU Idle in the cuzk Proving Engine

At first glance, message [msg 1850] appears to be a routine execution step in a debugging session: the assistant runs a benchmark against the cuzk proving daemon. But this message is far more significant than it seems. It represents the culmination of a careful instrumentation effort—the first moment the newly built waterfall timeline is put to the test—and it produces the raw data that will drive the next major architectural decision in the optimization of a Filecoin PoRep Groth16 proof generation pipeline. This is the message where the hypothesis meets reality, and the numbers speak.

The stdout/stderr Epiphany

The message opens with a small but telling realization. The assistant had previously implemented a timeline_event function that writes structured timeline data using eprintln! (standard error), while the daemon's tracing subscriber uses tracing_subscriber::fmt() which defaults to standard output. The daemon was launched with output redirections: stdout to /tmp/cuzk-waterfall-stdout.log and stderr to /tmp/cuzk-waterfall.log. The assistant checks the stderr log and finds it empty—not because the instrumentation is broken, but because no proofs have been submitted yet.

This moment of debugging meta-cognition is characteristic of systems engineering. The assistant had to mentally trace the flow of output: tracing goes to stdout, eprintln! goes to stderr, the daemon redirects both to separate files, and the timeline file is empty simply because the daemon is sitting idle waiting for work. The instrumentation is correct; the system just hasn't been exercised. This realization, though minor, prevents a false conclusion that the timeline code is broken, and clears the way for the real experiment.

Running the Benchmark

With the daemon confirmed ready, the assistant launches the benchmark command:

cuzk-bench batch -t porep --c1 /data/32gbench/c1.json -j 2 -c 5

This command tells the bench tool to submit five proof requests (-c 5) with a client-side concurrency of two (-j 2). The -t porep flag selects the proof type (PoRep, the Filecoin Proof-of-Replication), and --c1 points to a pre-computed C1 output file—the intermediate state from the first phase of proof generation that serves as input to the C2 phase being benchmarked.

The benchmark runs and produces these results:

  [1/5] COMPLETED — 105.4s (prove=26669 ms, queue=36538 ms)
  [2/5] COMPLETED — 64.1s (prove=27133 ms, queue=267 ms)
  [3/5] COMPLETED — 81.5s (prove=26350 ms, queue=5280 ms)
  [4/5] COMPLETED — 79.2s (prove=25971 ms, queue=11625 ms)
  [5/5] COMPLETED — 80.6s (prove=27555 ms...

The output is truncated in the conversation data, but the pattern is already clear. Each proof reports two metrics: prove (the actual GPU proving time, consistently ~26-27 seconds) and queue (the time the request spent waiting before GPU proving began). The first proof shows a massive 105.4s total time with 36.5s of queue time—this is the cold-start penalty where the pipeline has no work queued ahead. Subsequent proofs settle into a pattern of ~64-81s total with queue times varying from 267ms to over 11 seconds.

What the Numbers Reveal

The queue times are the critical signal. The first proof's 36.5s queue time represents the synthesis phase—the CPU-intensive work of constructing the circuit and generating the proof's a/b/c vectors before the GPU can begin its multi-scalar multiplication (MSM) and number-theoretic transform (NTT) work. The second proof's queue time drops to just 267ms because the pipeline's synthesis lookahead mechanism has already started synthesizing proof #2 while proof #1 is still on the GPU. This is the intended behavior of the two-stage pipeline.

But the third proof's queue time of 5.28s and the fourth's 11.6s tell a different story. These growing queue times reveal that synthesis is falling behind. The pipeline can overlap synthesis of proof N+1 with GPU proving of proof N, but only to a limited depth. With synthesis_lookahead=1 (the channel capacity between synthesis and GPU), the pipeline can only have one proof queued ahead. Once that buffer is consumed, the GPU must wait for the next synthesis to complete.

The numbers confirm the hypothesis that motivated the waterfall instrumentation in the first place: synthesis time (~37-41s) consistently exceeds GPU time (~26-27s), creating a structural idle gap of roughly 12-14 seconds per proof cycle. The GPU finishes its work and sits idle while the CPU catches up.## The Context: Why This Message Matters

To understand the significance of this benchmark run, one must appreciate the journey that led here. The cuzk proving engine had been through six phases of optimization, each building on the previous. Phase 5 introduced the Pre-Compiled Constraint Evaluator (PCE) to accelerate the CPU-bound constraint evaluation. Phase 6 implemented a slotted pipeline architecture to reduce peak memory by processing partitions sequentially rather than all at once. But the end-to-end benchmarks at the close of Phase 6 revealed a puzzling result: the standard pipeline (slot_size=0) significantly outperformed the partitioned path in throughput (47.7s vs 72s per proof), yet GPU utilization hovered around only 57%.

The question hanging over the project was: why is the GPU not fully utilized? Was it a memory bandwidth bottleneck? A kernel launch overhead issue? Or something structural in the pipeline architecture itself? The team had hypotheses, but no hard data. The waterfall timeline instrumentation—implemented across messages [msg 1821] through [msg 1833]—was designed to answer this question definitively by capturing precise wall-clock timestamps for every stage of the pipeline: when synthesis starts, when it ends, when the synthesized job enters the channel, when the GPU picks it up, and when GPU proving completes.

Message [msg 1850] is the first execution of that instrumentation. It is the moment the hypothesis meets measurement.

Assumptions and Their Validation

Several assumptions underpin this message. The first is that the timeline_event instrumentation correctly captures the relevant events. The assistant assumes that eprintln! output reaches the stderr log file, which it does—the log is simply empty because no proofs have run yet. This assumption is validated by the subsequent message ([msg 1851]) which shows the timeline data pouring in after the benchmark executes.

The second assumption is that the benchmark parameters (-j 2 -c 5) are sufficient to reveal the pipeline's steady-state behavior. Five proofs with concurrency of two means the bench tool submits up to two requests in parallel, and the daemon processes them through its internal pipeline. This is the standard configuration used in previous benchmarks, providing continuity with prior results.

A third, more subtle assumption is that the daemon's stdout/stderr separation is correct. The assistant initially worries that the timeline events might be going to the wrong file, but a quick check of the running process confirms the daemon is alive and the log files are in the expected locations. The cat of the stdout log shows the daemon started successfully and loaded its configuration.

The Input Knowledge Required

To fully grasp this message, one needs to understand several layers of context:

  1. The cuzk proving engine architecture: A two-stage pipeline where CPU synthesis (circuit construction, constraint evaluation, witness generation) feeds into GPU proving (multi-scalar multiplication, number-theoretic transforms). These stages communicate through a bounded channel with configurable lookahead depth.
  2. The waterfall instrumentation: A set of eprintln! calls added to the engine's synthesis and GPU worker code paths, each emitting a structured CSV line with a monotonic timestamp (milliseconds since engine start), an event type (SYNTH_START, SYNTH_END, CHAN_SEND, GPU_PICKUP, GPU_START, GPU_END), a job UUID, and optional detail fields.
  3. The benchmark tool: cuzk-bench batch submits N proof requests with a given concurrency level, measuring wall-clock time per proof and decomposing it into GPU proving time and queue wait time.
  4. The hardware context: A machine with 754 GiB of RAM, an NVIDIA GPU, and the Filecoin parameter cache at /data/zk/params. The C1 input file at /data/32gbench/c1.json represents a 32 GiB sector's proof-of-replication intermediate state.
  5. The optimization history: Previous phases established that synthesis takes ~38s and GPU proving takes ~27s, creating a ~12s gap. The waterfall was built to confirm this and to test whether parallel synthesis could close the gap.

The Output Knowledge Created

This message produces the raw benchmark output that will be analyzed in the following messages ([msg 1851] through [msg 1855]). The key data points are:

The Thinking Process

The assistant's reasoning in this message is a model of disciplined debugging. It begins by identifying a potential problem (empty stderr log) and systematically ruling out explanations: the daemon is running (confirmed by pgrep), the log files exist, the stdout log shows the daemon started successfully. The conclusion—"it's empty because no proofs have been submitted yet"—is reached by process of elimination.

Then, rather than overthinking, the assistant executes the benchmark. The command is carefully constructed: it uses 2>&1 | tee to capture both stdout and stderr of the bench tool to a separate file for later analysis, preserving the daemon's log files cleanly. The output is displayed inline, giving immediate visibility into the results.

The assistant does not yet analyze the waterfall data in this message—that will happen in the next round. But the benchmark numbers alone already tell a compelling story. The first proof's 105.4s total time includes a 36.5s queue, which is essentially the synthesis time of the first proof (since there's nothing ahead of it in the pipeline). The second proof's queue drops to 267ms because it was synthesized while the first proof was on the GPU. But the third proof's queue climbs back to 5.28s, and the fourth to 11.6s, showing that the synthesis lookahead buffer is being exhausted and the GPU is starting to wait.

This is the smoking gun. The sequential synthesis task, processing one proof at a time, cannot keep the GPU fed. The pipeline's lookahead depth of 1 provides only one proof of buffer, which is consumed after the first overlap. After that, each subsequent proof must wait for its synthesis to complete before the GPU can begin, creating the growing queue times.

The Broader Significance

Message [msg 1850] sits at a pivot point in the optimization journey. The previous phases had focused on reducing per-proof costs: faster constraint evaluation (PCE), memory-efficient partition processing (slotted pipeline), and SRS preloading. These optimizations improved individual proof times but left the pipeline architecture unchanged. The waterfall instrumentation reveals that the next frontier is not faster components, but better concurrency—running multiple syntheses in parallel to keep the GPU saturated.

This insight will drive the implementation of parallel synthesis via tokio::sync::Semaphore in the following messages, and the subsequent discovery that parallel synthesis, while saturating the GPU at 99.3% utilization, merely shifts the bottleneck from GPU idle to CPU contention. The law of diminishing returns in pipeline optimization is laid bare: each optimization reveals the next bottleneck, and the system's 96 CPU cores become the new limiting factor.

In this sense, message [msg 1850] is the diagnostic that reframes the entire optimization problem. It is not the solution—it is the measurement that makes the solution possible.