The Persistent GPU Idle Gap: Analyzing Thread Isolation in a Groth16 Proving Pipeline

Introduction

In the course of optimizing the cuzk proving engine for Filecoin's Proof-of-Replication (PoRep) protocol, an assistant found itself confronting a stubborn performance problem: the GPU was spending significant time idle between proof jobs, waiting for synthesis work to complete. Message 1979 of the coding session captures a pivotal moment in this investigation — the analysis of a third experimental configuration designed to test whether thread isolation between CPU synthesis and GPU work could eliminate these idle gaps. The message is deceptively simple: a Python script that parses timeline events from a daemon log and prints formatted statistics. But this analysis represents the culmination of a systematic experimental campaign, and its results would reshape the assistant's understanding of where the bottleneck truly lies.

Context: The GPU Idle Gap Problem

The cuzk proving engine processes Filecoin PoRep proofs through a pipeline with two major phases: synthesis (CPU-bound work that constructs the circuit and evaluates constraints) and GPU proving (which performs the heavy multiscalar multiplication and number-theoretic transform operations). Earlier benchmarks had revealed a structural problem: the GPU was idle for roughly 12 seconds between consecutive proofs, achieving only ~71% utilization. This "GPU idle gap" represented a direct throughput tax — every second the GPU sat idle was a second of wasted compute capacity.

The assistant's initial hypothesis was that the idle gap stemmed from CPU resource contention. The synthesis phase uses rayon for parallel execution, and the GPU phase spawns threads for its C++ groth16 pool. If both phases were competing for the same logical cores, the assistant reasoned, then synthesis might be starved of CPU resources, slowing down the pipeline and leaving the GPU waiting for work. The obvious fix was thread isolation: dedicate a fixed number of threads to synthesis and a separate pool to GPU work, preventing them from interfering with each other.

The Experimental Campaign

The assistant had already run two isolation experiments before arriving at message 1979. The first configuration (cuzk-isolated.toml) allocated 64 threads to rayon (synthesis) and 32 threads to the GPU pool, on a machine with 192 logical cores (96 physical cores with hyperthreading). The results were disappointing: synthesis time actually increased from 39 seconds to 46 seconds, because reducing rayon threads from 192 to 64 starved the highly parallel synthesis work. Throughput was virtually unchanged at 45.4 seconds per proof versus the 46.1 second baseline.

The second configuration (cuzk-isolated2.toml) tried a gentler approach: 96 rayon threads (all physical cores) and 32 GPU threads. The results were nearly identical — synthesis still took 47–48 seconds, and GPU idle gaps persisted around 15 seconds between proofs.

At this point, the assistant pivoted to a third hypothesis (expressed in message 1973): "Let me try a different approach: no limit on rayon (all 192 cores), only limit the GPU pool to 32." The reasoning was that if GPU threads were the problem, limiting them to 32 should reduce contention without slowing synthesis, since the GPU's b_g2_msm operation is largely single-threaded and doesn't benefit from massive parallelism anyway.

Message 1979: The Analysis

Message 1979 is the analysis of this third experiment. The assistant invokes a Python script inline via python3 -c that parses the TIMELINE events from the daemon's log file (/tmp/cuzk-isolated3-run.log). The script is a refined version of one used earlier (message 1964), suggesting the assistant had iterated on the analysis tooling alongside the experiments.

The script works by:

  1. Parsing TIMELINE events: Each line in the log with a TIMELINE prefix contains comma-separated fields: a timestamp in milliseconds, an event name (e.g., SYNTH_START, SYNTH_END, GPU_START, GPU_END), a job UUID, and optional detail parameters.
  2. Grouping events by job ID: The script builds a dictionary mapping each job UUID to its collection of events, extracting timestamps for each event type.
  3. Computing per-job metrics: For each job, it calculates synthesis duration (SYNTH_END minus SYNTH_START), GPU duration (GPU_END minus GPU_START), and the idle gap between the previous job's GPU completion and the current job's GPU start.
  4. Aggregating statistics: It computes total wall time, total GPU active time, total idle gaps, GPU utilization percentage, and average synthesis/GPU/idle times. The output reveals a telling pattern:
Job Timeline Analysis (synth=192, gpu=32, concurrency=2):
     Job  Synth Start  Synth End  Synth ms  GPU Start  GPU End  GPU ms  Idle Gap
-----------------------------------------------------------------------------------------------
P     1        82678     121608     38930     121612   150866   29254         0
P     2        82657     130607     47950     150881   182863   31982        15
P     3       155012     199428     44416     199432   226633   27201     16569
P     4       188364     ...

Several features stand out. First, the synthesis times are now closer to the baseline: P1 achieves 38.9 seconds (versus 39s baseline), though P2 is slower at 47.9 seconds and P3 at 44.4 seconds. This confirms that giving synthesis access to all 192 cores does restore performance compared to the 64-thread and 96-thread experiments.

Second, the GPU idle gap between P2 and P3 is 16.6 seconds — essentially unchanged from the baseline and previous experiments. This is the critical finding. Even with unlimited synthesis threads and a capped GPU pool, the GPU still sits idle for over 16 seconds between proofs.

The Deeper Insight: What the Data Reveals

The persistence of the GPU idle gap across all three isolation experiments is a significant finding. It falsifies the hypothesis that CPU resource contention is the primary cause of GPU idle time. If contention were the culprit, then giving synthesis unlimited cores (experiment 3) should have reduced or eliminated the gap. Instead, the gap remained stubbornly at ~16 seconds.

The data points to a different structural problem. Looking at the timestamps, we can see that P1 and P2 start synthesis at nearly the same time (82678ms and 82657ms respectively — the 21ms difference is negligible). They finish synthesis at very different times: P1 at 121608ms (38.9s) and P2 at 130607ms (47.9s). This 9-second spread in synthesis completion times is notable, and it suggests that the two concurrent synthesis jobs are not perfectly balanced — one takes significantly longer than the other.

But the real clue is in the GPU timeline. P1 starts GPU work at 121612ms (immediately after its synthesis ends) and finishes at 150866ms (29.3s of GPU time). P2 starts GPU work at 150881ms — just 15ms after P1's GPU ends — and finishes at 182863ms (32.0s). Then there's a 16.6-second gap before P3's GPU starts at 199432ms.

Where does this gap come from? P3's synthesis starts at 155012ms, which is before P2's GPU even finishes. P3's synthesis ends at 199428ms. The GPU starts at 199432ms — just 4ms after synthesis ends. So the gap isn't caused by synthesis being slow; P3's synthesis finishes while the GPU is still busy with P2, and the GPU picks up P3's work immediately after P2 finishes.

Wait — this actually contradicts the "idle gap" framing. If P3's synthesis finishes before P2's GPU work completes, then the GPU should be able to start P3 immediately after P2. But the gap is 16.6 seconds. This suggests that the GPU isn't picking up P3's work right away, or that there's some other structural delay.

Actually, looking more carefully: P3's synthesis starts at 155012ms and ends at 199428ms (44.4s). P2's GPU ends at 182863ms. So P3's synthesis is still running when P2's GPU finishes. The GPU has nothing to do until P3's synthesis completes at 199428ms. That's a gap of 199428 - 182863 = 16,565ms — exactly the 16.6s idle gap reported.

So the gap is simply that P3's synthesis takes 44.4 seconds, and the GPU finishes P2 in 32.0 seconds, leaving 12.4 seconds where the GPU is idle waiting for P3's synthesis to finish. But wait, 44.4 - 32.0 = 12.4, not 16.6. The extra 4.2 seconds comes from the fact that P3's synthesis starts later than P2's GPU ends... no, P3's synthesis starts at 155012ms, which is before P2's GPU starts at 150881ms. Let me recalculate.

P2 GPU end: 182863ms P3 GPU start: 199432ms Gap: 199432 - 182863 = 16569ms ✓

P3 synthesis end: 199428ms P3 GPU start: 199432ms (4ms later — negligible)

So the gap is entirely explained by P3's synthesis not being ready when P2's GPU finishes. P3's synthesis takes 44.4s total, but it started at 155012ms, which is only 27865ms before P2's GPU end at 182863ms. So P3 still has 44416 - 27865 = 16551ms of synthesis left when P2's GPU finishes. That's the gap.

The root cause: synthesis is slower than GPU proving for the overlapping portion. With two concurrent synthesis jobs and one GPU processing them sequentially, the GPU can outrun the synthesis pipeline. The system needs more synthesis parallelism to keep the GPU fed.

Assumptions and Their Falsification

This message reveals several assumptions that the assistant held, and that the data systematically falsified:

Assumption 1: Thread contention causes GPU idle gaps. The assistant assumed that CPU threads used by GPU work were starving synthesis threads, slowing down the pipeline. The data shows that even with unlimited synthesis threads (experiment 3), the idle gap persists. This assumption is falsified.

Assumption 2: Limiting GPU threads to 32 would not affect GPU performance. The assistant reasoned that b_g2_msm is "only ~25s single-threaded" and thus 32 threads would be sufficient. The data shows GPU times of 27–32 seconds, which is in line with baseline, so this assumption holds — but it didn't help solve the problem.

Assumption 3: The bottleneck is in the CPU-GPU handoff. The assistant was looking for a contention or scheduling issue at the boundary between synthesis and GPU work. The data reveals that the bottleneck is simpler: synthesis is just slower than GPU proving at the current parallelism level.

Input Knowledge Required

To fully understand this message, one needs:

  1. Knowledge of the cuzk proving pipeline architecture: Understanding that proofs go through a CPU synthesis phase followed by a GPU proving phase, and that these phases are pipelined across multiple proofs.
  2. Understanding of the TIMELINE event format: The log format with comma-separated timestamp, event type, job ID, and details.
  3. Knowledge of rayon and thread pool configuration: Understanding that rayon_threads controls the global rayon thread pool size, which affects synthesis parallelism, and that gpu_threads controls the C++ groth16 pool.
  4. Context from previous experiments: The reader needs to know that this is the third in a series of isolation experiments, each testing a different thread allocation strategy.
  5. Understanding of the baseline metrics: The assistant compares against a baseline of ~39s synthesis, ~27s GPU, ~12s idle gap, ~71% GPU utilization, and ~46s/proof throughput.

Output Knowledge Created

This message produces several concrete outputs:

  1. Quantified performance metrics for the third isolation config: Synthesis averages ~43.8s, GPU averages ~29.5s, idle gap is ~16.6s, GPU utilization is ~64%.
  2. Evidence that thread isolation does not solve the GPU idle gap: Across all three experiments, the gap persisted at 10–16 seconds regardless of thread allocation.
  3. A refined understanding of the bottleneck: The gap is caused by synthesis being slower than GPU proving at the current parallelism level, not by thread contention.
  4. A reusable analysis tool: The Python script for parsing TIMELINE events and computing per-job metrics, which could be applied to future experiments.

The Thinking Process

The assistant's thinking in this message is visible in the structure of the analysis. Rather than eyeballing log files, the assistant writes a precise parsing script that extracts structured data from the unstructured log. This reveals a systematic, engineering-minded approach: define metrics, collect data, compute statistics, draw conclusions.

The script itself shows careful design. It handles edge cases (missing events via .get() with defaults), computes derived metrics (idle gaps, utilization percentages), and presents results in a formatted table. The assistant is thinking like a performance engineer, not just a developer.

The choice to run this analysis immediately after the benchmark (message 1978) shows that the assistant is operating in a tight feedback loop: run experiment, analyze results, form new hypothesis, repeat. Message 1979 is the "analyze results" step for the third experiment.

Conclusion

Message 1979 represents a crucial moment of empirical reality-check in the optimization campaign. The assistant had formed a plausible hypothesis — that CPU thread contention was causing GPU idle gaps — and had designed a series of experiments to test it. The data from the third experiment definitively showed that even with unlimited synthesis threads, the GPU idle gap persisted. This forced a reconceptualization of the problem: the bottleneck wasn't in thread scheduling or contention, but in the fundamental throughput mismatch between CPU synthesis and GPU proving.

The message is a testament to the value of systematic experimentation and quantitative analysis in performance engineering. Each experiment eliminated one hypothesis and narrowed the search space. The assistant didn't guess at the answer; it let the data speak. And the data, captured in that simple formatted table, told a clear story: the GPU was idle because synthesis couldn't keep up, not because threads were fighting each other. This insight would prove essential for the next phase of the optimization work, which would shift focus from thread management to pipeline architecture.