The Shifting Bottleneck: When Saturating the GPU Reveals CPU Contention in a Groth16 Proving Pipeline

Introduction

In the high-stakes world of Filecoin proof generation, every second of throughput matters. The SUPRASEAL_C2 Groth16 proving pipeline, responsible for generating Proofs of Replication (PoRep) for Filecoin storage proofs, had been laboring under a structural inefficiency: the GPU sat idle for 12–14 seconds per proof cycle while waiting for CPU synthesis to complete. The obvious fix—parallelizing the CPU synthesis work to keep the GPU fed—seemed straightforward. But as message [msg 1877] reveals, optimization is rarely a simple matter of adding more parallelism. When the assistant implemented parallel synthesis and achieved 99.3% GPU utilization, the bottleneck did not disappear; it merely shifted from the GPU to the CPU, exposing a deeper resource contention problem that demanded a more nuanced understanding of the system's shared computational resources.

Context: The Optimization Journey

The conversation leading up to [msg 1877] documents a systematic investigation into the cuzk proving engine's performance. The pipeline operates in two stages: CPU synthesis (transforming circuit constraints into proving artifacts) and GPU proving (executing the heavy cryptographic computation). Earlier benchmarks had revealed a stark waterfall timeline showing that synthesis took approximately 39 seconds per proof while GPU proving took only 27 seconds. Because synthesis was strictly sequential—each proof's synthesis began only after the previous proof's synthesis completed—the GPU would finish its work and then wait 12 seconds for the next proof's synthesis to finish. This structural GPU idle gap represented a 29% utilization loss.

The assistant diagnosed this problem in [msg 1854] with a text-based waterfall visualization and proposed parallel synthesis as the solution. The implementation, carried out across [msg 1856] through [msg 1866], refactored the engine's synthesis task loop to use a tokio::sync::Semaphore, allowing multiple proofs to be synthesized concurrently under the control of a new synthesis_concurrency configuration parameter. The change was clean, the build succeeded, and the daemon was deployed for testing.

Initial results were promising but puzzling. In [msg 1873], the waterfall showed GPU utilization jumping to 86.5%, but synthesis times had inflated dramatically—from 38 seconds for a solo synthesis to 54 seconds when two ran concurrently. GPU proving time also increased from 27 seconds to 32 seconds. The net throughput improvement was marginal: approximately 44.7 seconds per proof versus 45.3 seconds, well within noise.

A second benchmark in [msg 1875] with more proofs painted a more extreme picture. GPU utilization hit 99.3%—the idle gap was essentially eliminated. But GPU proving time had ballooned to 37.6 seconds (a 40% increase), and average synthesis time rose to 47.2 seconds. The net result was 43.0 seconds per proof, a mere 5% improvement over the baseline. Something deeper was wrong.

The Subject Message: Recognizing the True Bottleneck

[msg 1877] captures the moment of recognition. The assistant writes:

Actually, the real question is whether we can reduce CPU contention. The b_g2_msm step in the GPU prover uses the full rayon thread pool. Maybe we should limit rayon's threads during synthesis to leave headroom for GPU's CPU work. But that's a deeper change.

>

For now, let me also try the important comparison: concurrency=2 with only -j 2 (2 in-flight proofs) to see if 3 concurrent requests is too many:

The message then executes bash commands to start a new daemon instance with a modified configuration (synthesis_lookahead=1) and verifies that it is ready.

This brief message is deceptively dense. It contains three distinct layers of analysis: a diagnosis of the root cause (CPU contention), a proposed remedy (limiting rayon threads), and an immediate experimental step (reducing client concurrency). Each layer reveals something important about the assistant's reasoning process and the nature of the optimization problem.

The CPU Contention Problem Explained

To understand why parallel synthesis failed to deliver proportional throughput gains, one must understand the architecture of the cuzk proving engine. The GPU proving phase is not purely GPU-bound. It includes a step called b_g2_msm (multi-scalar multiplication on the G2 curve), which runs on the CPU using the Rayon parallel computation library. This step is multi-threaded and consumes significant CPU resources—approximately 25 seconds of wall-clock time per proof, spread across all available cores.

When the assistant enabled two concurrent syntheses, each synthesis also used Rayon to parallelize its work across all available CPU cores. On a 96-core machine, this meant that two syntheses plus the GPU's b_g2_msm step were all competing for the same thread pool. The result was a classic case of resource contention: every component ran slower because they were fighting for CPU time.

The synthesis time inflation (from 39 seconds to 47 seconds) and GPU time inflation (from 27 seconds to 38 seconds) were both symptoms of this contention. The GPU utilization metric was misleadingly positive—99.3% utilization sounds ideal, but it masked the fact that the GPU was spending more time waiting for its own CPU-bound subtasks to complete. The GPU was busy, but it was busy slower.

Assumptions and Their Consequences

The parallel synthesis implementation rested on an implicit assumption: that CPU synthesis and GPU proving could be cleanly overlapped because they use different hardware resources. This assumption was partially correct—the GPU itself is a separate device—but it failed to account for the CPU-bound subtasks within the GPU proving phase. The b_g2_msm step creates a shared dependency on the CPU that neither synthesis nor GPU proving can escape.

A second assumption was that the Rayon thread pool would naturally handle contention through work-stealing and dynamic scheduling. In practice, Rayon's default behavior is to spawn as many threads as there are logical cores and let the OS scheduler handle contention. This leads to oversubscription: when three CPU-intensive workloads each try to use all 96 cores, the OS must constantly context-switch between 288+ threads, degrading throughput for all of them.

The assistant's proposed fix—limiting Rayon's thread count during synthesis to leave headroom for the GPU's CPU work—addresses this directly. By capping synthesis threads to, say, 48 cores, the remaining 48 cores would be available for b_g2_msm, potentially allowing both workloads to complete faster than when they compete for all 96. This is a form of resource partitioning that acknowledges the shared nature of the CPU.

The Experimental Pivot

The second part of [msg 1877] shows the assistant pivoting to a practical experiment: testing with client concurrency -j 2 instead of -j 3. This is a pragmatic response to the observation that three in-flight proofs might be over-aggressive. With synthesis_concurrency=2 and only two concurrent client requests, the system would naturally have at most two proofs in the pipeline at any time, reducing the peak contention.

This experimental approach reveals the assistant's methodology: when a theoretical analysis hits complexity, fall back to empirical testing. The assistant does not commit to the rayon-thread-limiting approach (acknowledging it as "a deeper change") but instead tests a simpler configuration change that might yield immediate insight. This is characteristic of disciplined systems optimization—test the simplest hypothesis first, then escalate to more invasive changes only when necessary.

Input and Output Knowledge

To fully understand [msg 1877], the reader needs knowledge of: the cuzk proving engine's two-stage pipeline architecture (synthesis → GPU proving), the role of b_g2_msm as a CPU-bound subtask within GPU proving, the Rayon parallel computation library and its default thread-pool behavior, the tokio::sync::Semaphore mechanism used for concurrency control, and the benchmark methodology (client concurrency -j N, waterfall timeline instrumentation). The message also references specific configuration parameters (synthesis_concurrency, synthesis_lookahead) that were introduced in earlier messages.

The message creates new knowledge by: identifying CPU contention as the binding constraint after GPU saturation, proposing resource partitioning (limiting rayon threads) as a structural fix, and establishing the experimental protocol for testing client concurrency levels. It also implicitly documents a law of diminishing returns: parallel synthesis saturated the GPU but shifted the bottleneck to the CPU, yielding only 5% throughput improvement.

The Thinking Process

The assistant's reasoning in [msg 1877] follows a clear diagnostic pattern. First, it identifies the true root cause: "The b_g2_msm step in the GPU prover uses the full rayon thread pool." This is a precise, technical diagnosis grounded in the architecture. Second, it proposes a targeted fix: "limit rayon's threads during synthesis to leave headroom for GPU's CPU work." Third, it evaluates the cost of that fix: "But that's a deeper change." This cost-benefit assessment is crucial—the assistant recognizes that modifying Rayon's thread pool configuration could have wide-ranging effects and should not be undertaken without more evidence.

The pivot to testing -j 2 is a recognition that the current experiment might be using too much client concurrency. With synthesis_concurrency=2 and -j 3, the system can have up to three proofs in flight, meaning two syntheses running plus one GPU proving—all competing for CPU. Reducing to -j 2 would mean at most two proofs in flight, which could be either two syntheses or one synthesis plus one GPU prove. This simpler configuration might reduce contention enough to show a clearer picture.

Broader Implications

The lesson of [msg 1877] extends beyond this specific proving pipeline. It illustrates a general principle in systems optimization: fixing one bottleneck often reveals another, and the new bottleneck may be more complex than the original. The GPU idle gap was a clean, visible problem with a clear solution (parallel synthesis). The CPU contention problem is messier—it involves shared resources, thread scheduling, and workload-dependent interference patterns.

The message also highlights the importance of measuring the right things. GPU utilization alone was a misleading metric: 99.3% utilization looked like success, but it masked the fact that the GPU was making slower progress due to CPU starvation. The waterfall timeline instrumentation proved critical for seeing beyond the surface-level metric and understanding the true dynamics.

Conclusion

Message [msg 1877] captures a pivotal moment in the optimization of a high-performance cryptographic proving system. The assistant recognized that saturating the GPU was not enough—the system's shared CPU resources created a new binding constraint that required a more sophisticated approach. The proposed solution of partitioning CPU resources between synthesis and GPU proving represents a shift from adding parallelism to managing contention. Whether through Rayon thread limiting, client concurrency tuning, or deeper architectural changes, the path forward requires acknowledging that in a system with shared computational resources, optimization is not about maximizing any single metric but about balancing the competing demands of interdependent components.