The Starved GPU: A Bottleneck Paradigm Shift in Phase 9 Benchmarking

Introduction

In the course of optimizing Groth16 proof generation for Filecoin's Proof-of-Replication (PoRep) on NVIDIA GPUs, a team working on the cuzk SNARK proving engine reached a critical inflection point. Message 2492 captures the moment when the team first confronts evidence that their PCIe transfer optimization (Phase 9) has been so successful that the GPU has become starved for work — a complete inversion of the previous bottleneck regime where GPU kernel execution dominated the critical path. This message, though brief, represents a paradigm shift in how the optimization problem must be understood and attacked going forward.

The Message in Full

The assistant writes:

42.9s/proof with c=15 j=10 — that's worse than the small run. The theory is right: GPU is so fast now that it's starving for work. The prove times (29-45s) suggest synthesis herding — bursts of partitions arrive together then gaps. Let me get the waterfall timeline data and also run with higher concurrency to see if we can feed the GPU better.

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Let me find the timeline/waterfall utility first. ``` /home/theuser/curio/extern/cuzk/target/release/cuzk-bench metrics --help 2>&1 Get Prometheus metrics from the daemon

>

Usage: cuzk-bench metrics

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Options: -h, --help Print help ```

This message is a response to the user's observation in <msg id=2490> that the team should look at timing data and waterfalls, and the user's theory in <msg id=2491> that "the theory would be complete starvation on synths coming in fast enough." The assistant is confirming that theory with data and taking the next investigative steps.

The Context: Phase 9's Success Creates a New Problem

To understand the significance of this message, one must appreciate what came before it. The team had been working through a series of optimization phases targeting the Groth16 proof generation pipeline for Filecoin's 32 GiB sector size. Phase 8 had achieved a baseline of ~37.4s/proof using a dual-worker GPU interlock. Phase 9 then implemented a PCIe transfer optimization that pre-staged polynomial uploads using cudaHostRegister with async DMA, double-buffered deferred batch sync in Pippenger MSM, and early deallocation of GPU buffers.

The Phase 9 results in isolation were dramatic: the ntt_msm_h_ms metric dropped from 2430ms to 690ms (a 71.6% reduction), and gpu_total_ms fell from 3746ms to 1450ms (a 61.3% reduction). Single-worker throughput improved by 14.2% to 32.1s/proof. The GPU had become dramatically more efficient.

However, when the assistant scaled up the benchmark to 15 concurrent syntheses and 10 proofs (c=15, j=10) as the user requested in <msg id=2468>, the throughput regressed to 42.9s/proof — worse than the Phase 8 baseline. This counterintuitive result is the central puzzle that message 2492 grapples with.

The Reasoning: Diagnosing GPU Starvation

The assistant's reasoning in this message reveals a sophisticated understanding of pipeline dynamics. The key insight is that the per-partition GPU time has dropped so low (~1.5s) that the GPU finishes processing each partition faster than the CPU-side synthesis workers can produce new ones. This creates a phenomenon the assistant calls "synthesis herding" — bursts of partitions arrive at the GPU together (when multiple synthesis workers finish around the same time), followed by idle gaps while the CPU workers catch up.

The prove times ranging from 29 to 45 seconds support this interpretation. If the GPU were the bottleneck, one would expect consistent, tightly clustered prove times. The wide variance suggests that the pipeline is not in steady state — sometimes the GPU has work queued up, sometimes it waits. This is the classic signature of a producer-consumer system where the consumer (GPU) has outpaced the producer (CPU synthesis).

The assistant's decision to pursue waterfall timeline data is methodologically sound. Aggregate throughput numbers like 42.9s/proof can be misleading — they don't reveal why the pipeline is slow. A waterfall chart showing per-partition timing across GPU workers, CPU synthesis, and queue wait times would reveal exactly where the idle gaps occur and how severe they are. This is the difference between knowing that something is wrong and knowing what is wrong.

Assumptions and Their Implications

The message contains several important assumptions that shape the subsequent investigation:

Assumption 1: Higher concurrency will feed the GPU better. The assistant plans to "run with higher concurrency to see if we can feed the GPU better." This assumes that the GPU starvation is caused by insufficient parallelism on the CPU side — that more synthesis workers will produce partitions faster and keep the GPU busy. This is a reasonable hypothesis, but it carries risk: higher concurrency means more CPU memory bandwidth contention (as the team would later discover in Chunk 0 of this segment). The 8-channel DDR5 memory bandwidth is a shared resource, and 10 synthesis workers already compete with CPU-side MSM operations.

Assumption 2: The waterfall utility exists as a subcommand of cuzk-bench. The assistant tries cuzk-bench metrics --help expecting to find a timeline/waterfall subcommand. The help output reveals only a simple metrics command with no subcommands. This is a moment of discovery — the tooling for detailed pipeline visualization may not exist yet, or may need to be accessed differently. The assistant adapts by reading the help output and presumably will look elsewhere.

Assumption 3: The theory of GPU starvation is correct. The assistant states "The theory is right" with confidence. While the data is consistent with this theory, it is not yet proven. The waterfall data would confirm or refute it. This assumption is reasonable as a working hypothesis but should be held lightly until confirmed.

Input Knowledge Required

To fully understand this message, one needs:

  1. Knowledge of the Phase 9 optimization: The PCIe transfer optimization that reduced GPU kernel time from ~3.7s to ~1.5s per partition. Without this context, the claim that the GPU is "starving" makes no sense — why would a faster GPU be a problem?
  2. Understanding of the pipeline architecture: The cuzk engine uses a producer-consumer model where CPU synthesis workers generate circuit partitions and GPU workers process them. The partition_workers setting (pw=10) controls how many synthesis threads run concurrently, while gpu_workers_per_device (gw=1) controls how many GPU worker threads share the device.
  3. The benchmark methodology: The -c flag controls concurrency (number of simultaneous proofs in flight), and -j controls the number of proofs to run. The team uses these parameters to stress-test the pipeline under different load conditions.
  4. The previous baseline results: Phase 8 achieved 37.4s/proof, and the small-scale Phase 9 run achieved 32.1s/proof. The regression to 42.9s/proof at higher concurrency is only meaningful against these baselines.

Output Knowledge Created

This message produces several important pieces of knowledge:

  1. The GPU starvation hypothesis is supported by preliminary data: The 42.9s/proof result at c=15, j=10, combined with the wide prove time variance (29-45s), is consistent with a producer-constrained pipeline.
  2. The bottleneck has shifted: Previously, the bottleneck was GPU kernel execution and PCIe transfer latency. Now it appears to be CPU synthesis throughput. This fundamentally changes which optimization levers are worth pulling.
  3. The tooling gap is identified: The cuzk-bench metrics command does not provide waterfall/timeline visualization. This may require building new tooling or accessing the data through a different mechanism (e.g., Prometheus metrics scraped and plotted externally).
  4. The next investigative direction is set: Run higher concurrency benchmarks and find a way to visualize per-partition timing to confirm the starvation hypothesis and quantify the idle gaps.

The Thinking Process

The assistant's thinking process, visible in the message structure, follows a clear arc:

  1. Observe the result: 42.9s/proof is worse than the small run. This is the raw data point.
  2. Interpret the result: The GPU is starving for work. This is the hypothesis.
  3. Explain the mechanism: Synthesis herding — bursts of partitions arrive together then gaps. This is the causal model.
  4. Plan the investigation: Get waterfall timeline data and run higher concurrency. This is the methodology.
  5. Execute the first step: Find the timeline/waterfall utility by checking the CLI help. This is the action. The progression from observation → hypothesis → causal model → methodology → action is a textbook example of systematic debugging. The assistant doesn't jump to conclusions or implement a fix prematurely. Instead, they seek more data to confirm the root cause before designing a solution.

Broader Significance

This message marks a turning point in the optimization project. The team has been chasing GPU efficiency through multiple phases (Phase 6: per-partition dispatch, Phase 7: engine-level pipeline, Phase 8: dual-worker interlock, Phase 9: PCIe optimization). Each phase made the GPU faster. But now the GPU is too fast relative to the CPU synthesis pipeline. The optimization problem has transformed from "how do we make the GPU faster?" to "how do we keep the GPU fed?"

This is a classic pattern in systems optimization: improving one component eventually exposes a different bottleneck elsewhere in the system. The skill lies in recognizing when this shift has occurred and adjusting the optimization strategy accordingly. Message 2492 captures the exact moment of that recognition.

The subsequent investigation (detailed in Chunk 0 of this segment) would reveal that the true bottleneck is CPU memory bandwidth contention — the 10 synthesis workers compete with CPU-side MSM operations for 8-channel DDR5 bandwidth, inflating CPU times by 2-12× at high concurrency. This deeper diagnosis would lead to the Phase 10 two-lock design, which attempts to overlap CPU memory management with GPU compute. But at the moment of message 2492, the team is still operating at the level of the starvation hypothesis, before the memory bandwidth root cause is identified.

Conclusion

Message 2492 is a brief but pivotal communication that demonstrates systematic diagnostic reasoning in a complex optimization context. The assistant correctly interprets a counterintuitive benchmark regression as evidence of a bottleneck paradigm shift, formulates a testable hypothesis (GPU starvation), and sets a clear investigative direction. The message also reveals a tooling gap that will need to be addressed. For anyone studying applied systems optimization, this message exemplifies the critical skill of recognizing when the bottleneck has moved and adjusting the investigative approach accordingly.