The Thread Balancing Act: Diagnosing CPU-GPU Contention in SNARK Proof Synthesis

Introduction

In the pursuit of optimizing Filecoin's SNARK proving pipeline, few moments are as instructive as the one captured in message 1973 of this coding session. Here, the assistant confronts a hard truth revealed by two benchmark runs: isolating CPU threads for the GPU pool does not automatically improve throughput, because the synthesis phase of proof generation is voraciously parallel and suffers when starved of cores. This message represents a critical pivot point — a moment of diagnostic clarity where the assistant abandons a flawed hypothesis and designs a new experiment based on empirical evidence.

The message is deceptively brief: a few lines of analysis followed by a configuration file write. But beneath its surface lies a rich tapestry of reasoning about CPU-GPU tradeoffs, parallel workload characteristics, and the subtle art of resource partitioning in heterogeneous computing systems. Understanding this message requires tracing the thread-isolation experiments that preceded it, the benchmark data that informed it, and the architectural assumptions that it challenges.

The Message: Exact Text

The assistant wrote:

The results are nearly identical to the first isolated run. The core issue is clear: with only 96 rayon threads (instead of 192), synthesis takes 47-48s instead of 39s. The synthesis is highly parallel and benefits from all 192 logical cores.

Let me try a different approach: no limit on rayon (all 192 cores), only limit the GPU pool to 32. This way synthesis still gets all cores, and only b_g2_msm is limited — but b_g2_msm is only ~25s single-threaded, so even with 32 threads it should be fast enough:

[write] /tmp/cuzk-isolated3.toml Wrote file successfully.

The message then reports LSP errors in an unrelated file (a missing header in sppark/msm/pippenger.hpp), which is a routine diagnostic artifact from the editor tooling and not relevant to the reasoning at hand.

Context: The Thread Isolation Experiments

To understand why this message was written, we must step back to the experiments that preceded it. The session had been investigating a structural GPU idle gap in the cuzk proving engine — a period where the GPU sits idle waiting for synthesis to complete before it can begin proving. Earlier work ([msg 1955] through [msg 1972]) had established a baseline: with the default configuration (192 rayon threads, no GPU thread isolation), the pipeline achieved roughly 46.1 seconds per proof with GPU utilization around 70.9%.

The hypothesis driving the thread isolation experiments was that CPU-GPU contention was the culprit. The reasoning was intuitive: the GPU pool (groth16_pool) uses CPU threads to orchestrate GPU kernel launches and manage multi-scalar multiplication (MSM) operations. If these GPU-management threads compete with synthesis threads for CPU cores, both sides suffer. By partitioning the thread pool — dedicating some cores exclusively to GPU work and others exclusively to synthesis — the hope was that both sides would run more efficiently.

The first experiment ([msg 1955]) used 64 rayon threads + 32 GPU threads. The results were sobering: synthesis time increased from 39s to 46.3s, GPU utilization improved only marginally to 78.1%, and throughput was essentially unchanged at 45.4s/proof. The second experiment ([msg 1971]) tried 96 rayon threads + 32 GPU threads, yielding nearly identical results: 47-48s synthesis, similar GPU utilization.

The Diagnostic Insight

Message 1973 is the moment where the assistant synthesizes these results into a coherent diagnosis. The key observation is that synthesis time scales directly with available rayon threads: 192 threads yield ~39s synthesis, 96 threads yield ~47-48s, and 64 threads yield ~46s (the 64-thread case was slightly better than 96-thread due to less GPU contention, but still far worse than the 192-thread baseline).

This tells the assistant something crucial about the nature of the synthesis workload. The synthesis phase — which encompasses witness generation and sparse matrix-vector (SpMV) evaluation for all 10 PoRep C2 partitions — is not just parallelizable; it is super-linearly parallelizable up to the full thread count. Each additional core meaningfully reduces wall-clock time. There is no saturation point at 64 or 96 threads; the workload continues to benefit from all 192 logical cores available on the machine.

This is a non-obvious finding. Many parallel workloads exhibit diminishing returns as thread count increases, due to Amdahl's Law effects, memory bandwidth limitations, or synchronization overhead. The fact that synthesis scales cleanly to 192 threads suggests that the workload is embarrassingly parallel — each partition's synthesis is largely independent, with minimal shared state or synchronization points. The rayon work-stealing scheduler can keep all cores busy with useful work.

The New Hypothesis

Armed with this diagnosis, the assistant formulates a new hypothesis: if synthesis needs all 192 cores, then the thread isolation approach is fundamentally misguided. Instead of partitioning cores, the correct approach is to leave rayon unlimited and only limit the GPU pool threads. The reasoning is that b_g2_msm — the multi-scalar multiplication on the G2 curve, which is the most expensive GPU operation — takes approximately 25 seconds in single-threaded mode. Even with 32 threads dedicated to GPU management, this operation should complete well within the synthesis window.

This is a subtle but important shift in thinking. The assistant is distinguishing between two different kinds of CPU work:

Synthesis work (witness generation, SpMV): highly parallel, CPU-bound, benefits from all cores.
GPU orchestration work (MSM management, kernel launches): mostly about keeping the GPU fed, with relatively low CPU demand. The earlier experiments had treated both as competing for the same pool of cores. The new insight is that GPU orchestration threads are not heavy CPU consumers — they spend most of their time waiting for GPU kernels to complete. Limiting them to 32 threads while leaving 192 threads for synthesis should, in theory, give synthesis all the cores it needs while still providing adequate GPU management bandwidth.

Assumptions and Their Validity

This message rests on several assumptions, some explicit and some implicit:

Assumption 1: Synthesis scales linearly with thread count. The data supports this for the range tested (64→96→192 threads), but it's not proven that 192 is the optimal point. There could be diminishing returns beyond 192, or the workload might benefit from even more threads (e.g., 256). The assistant assumes the current trend continues.

Assumption 2: The GPU pool threads are not CPU-bound. The assistant asserts that b_g2_msm is "only ~25s single-threaded" and implies that 32 threads are sufficient. This assumes that the GPU operations are primarily GPU-bound, with CPU threads mostly waiting. This is likely correct for MSM operations, which are dominated by GPU computation, but may not hold for other GPU pipeline stages.

Assumption 3: The GPU pool limit of 32 is "enough." The assistant doesn't specify what 32 threads are doing during GPU operations. If the GPU pool uses threads for parallel CPU-side preprocessing (e.g., scalar preparation, point addition), then limiting to 32 could create a new bottleneck. The assistant implicitly assumes that 32 threads saturate the GPU's needs.

Assumption 4: The config file write will succeed and the daemon will accept the new configuration. This is a procedural assumption — the assistant expects the tooling to work correctly, which it generally does.

Mistakes and Incorrect Assumptions

The most significant potential mistake in this message is the assumption that limiting GPU pool threads to 32 while leaving rayon at 192 will eliminate contention. The earlier experiments showed that even with 96 rayon + 32 GPU, synthesis was slower than the baseline. The assistant attributes this entirely to the rayon reduction, but there could be other factors:

Cache and memory bandwidth contention. Even if threads are logically separated, they share physical CPU resources including L3 cache and memory bandwidth. The GPU pool threads, even if mostly waiting, still consume cache space and memory bandwidth when they do run. This could subtly degrade synthesis performance.
OS scheduler interference. The Linux scheduler doesn't respect logical thread pool boundaries. A GPU pool thread could be scheduled on the same physical core as a synthesis thread, causing resource contention at the microarchitectural level. The CUZK_GPU_THREADS environment variable controls the C++ thread pool size, but doesn't pin threads to specific cores.
Rayon's work-stealing behavior. Rayon's default thread pool uses all available cores. When the GPU pool is limited to 32 threads, the remaining 160 threads are available for rayon. But rayon's work-stealing scheduler may not optimally distribute synthesis work across all cores, especially if some cores are intermittently occupied by GPU pool threads. The assistant also doesn't consider the possibility that the GPU pool threads themselves are doing useful CPU work that benefits from more threads. The b_g2_msm operation might have a CPU-side component (e.g., bucket accumulation in Pippenger's algorithm) that scales with thread count. Limiting to 32 threads could slow down the GPU pipeline, increasing the GPU idle gap rather than reducing it.

Input Knowledge Required

To fully understand this message, a reader needs:

Knowledge of the cuzk proving pipeline architecture: The distinction between synthesis (CPU-bound, parallelizable across partitions) and GPU proving (GPU-bound, with CPU orchestration threads). The reader must understand that a single PoRep C2 proof involves 10 partitions, each requiring ~30-37s of synthesis, and that these partitions currently run in parallel via rayon.
Understanding of thread pool isolation: The concept of partitioning CPU cores into dedicated pools for different workloads, and the CUZK_GPU_THREADS / RAYON_NUM_THREADS environment variables that control this partitioning.
Familiarity with the benchmark methodology: The "isolated" benchmark runs used specific TOML configuration files and the cuzk-bench tool to measure proof times, queue wait times, and GPU utilization via timeline events.
Knowledge of MSM operations: The b_g2_msm is a multi-scalar multiplication on the BLS12-381 G2 curve, which is the dominant GPU operation in the proving pipeline. Understanding that it takes ~25s single-threaded (meaning with minimal CPU thread support) is crucial to the assistant's reasoning.
Context from earlier experiments: The two prior isolated runs (64+32 and 96+32 thread splits) and their results, which provide the empirical basis for the diagnosis.

Output Knowledge Created

This message creates several pieces of actionable knowledge:

Empirical confirmation that synthesis scales to 192 threads: The benchmark data proves that the synthesis phase is highly parallel and benefits from all available logical cores. This is a non-trivial finding that shapes all subsequent optimization work.
A refined hypothesis about contention: The assistant now understands that the contention problem is not symmetric — synthesis is the sensitive workload, while GPU orchestration is relatively tolerant of thread reduction. This reframes the optimization problem from "how to partition cores" to "how to give synthesis all cores while keeping GPU fed."
A new experimental configuration: The cuzk-isolated3.toml file represents a concrete test of the new hypothesis. Its results (which would appear in subsequent messages) will either validate or refute the refined model.
Documentation of a dead end: The thread isolation approach (partitioning cores between synthesis and GPU) is shown to be ineffective for this workload. This saves future optimization efforts from repeating the same experiment.

The Thinking Process

The reasoning visible in this message is a model of scientific debugging. The assistant follows a clear cycle:

Observe: Two benchmark runs produce nearly identical results, both worse than baseline in synthesis time.
Compare: The assistant mentally computes the delta — 47-48s vs 39s — and recognizes that the common factor in both runs is reduced rayon threads (64 and 96 vs the baseline 192).
Hypothesize: The synthesis phase is highly parallel and requires all 192 cores. The GPU pool threads are not the primary bottleneck.
Design experiment: Test the hypothesis by removing the rayon limit entirely while keeping only the GPU pool limit. This isolates the variable — if synthesis returns to ~39s, the diagnosis is confirmed.
Execute: Write the configuration file and prepare to launch the next benchmark. The assistant also demonstrates an important cognitive skill: the ability to distinguish between correlation and causation. Both isolated runs showed slower synthesis, but the assistant correctly identifies the common cause (reduced rayon threads) rather than blaming GPU contention. This requires understanding the workload's scaling characteristics and trusting the benchmark data over intuition.

Broader Significance

This message represents a turning point in the optimization journey. Earlier phases had focused on architectural changes — the slotted pipeline, parallel synthesis, cross-sector batching. But here, the assistant is grappling with a fundamental systems problem: how to share finite CPU resources between two phases of a pipeline that have different scaling characteristics.

The insight that synthesis is embarrassingly parallel to 192 threads while GPU orchestration is lightweight has implications beyond this specific experiment. It suggests that the correct optimization strategy is not thread partitioning but temporal partitioning — let synthesis use all cores, complete quickly, then hand off to the GPU. This aligns with the "thundering herd" model described in the segment summary, where all 10 partitions finish synthesis simultaneously and submit to the GPU as a batch.

The message also highlights the importance of empirical measurement in systems optimization. The assistant could have continued refining the thread isolation approach based on intuition, but instead let the benchmark data guide the next experiment. This willingness to abandon a hypothesis in the face of contradictory evidence is the hallmark of effective engineering.

Conclusion

Message 1973 is a compact but dense piece of technical reasoning. In a few sentences, the assistant diagnoses a failed experiment, identifies the root cause, formulates a new hypothesis, and designs a test. The message captures the essence of the optimization challenge in the cuzk proving pipeline: the tension between CPU-bound synthesis and GPU-bound proving, and the difficulty of partitioning finite hardware resources between them.

The true value of this message lies not in its length but in its clarity of thought. It demonstrates how benchmark data, when properly interpreted, can correct mistaken assumptions and guide the optimization process toward productive directions. Whether the new experiment succeeds or fails, the diagnostic work in this message advances the team's understanding of their system — and that understanding is the foundation of all subsequent optimization.