The Pivotal Config: How a Single TOML File Tested the Limits of Parallelism in GPU-Accelerated Proof Generation

Message Summary

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

At first glance, message [msg 1868] appears to be one of the most mundane events in a coding session: a configuration file being written to disk. The assistant writes a TOML file, the tool confirms success, and the conversation moves on. Yet this single message sits at a critical inflection point in the cuzk proving engine optimization effort. It is the bridge between a carefully reasoned architectural change and the experimental validation that would reveal a fundamental truth about the system's bottleneck structure. The file /tmp/cuzk-parallel.toml is not merely a configuration—it is a hypothesis made concrete, a prediction about how the system should behave, written in the declarative language of key-value pairs. And like many bold hypotheses in systems engineering, it would be both validated and humbled by the messy reality of hardware contention.

The Road to Parallel Synthesis

To understand why this message matters, we must trace the reasoning that led to it. The preceding segment of work had been a deep investigation into the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep). The proving engine, built in Rust with C++/CUDA kernels, exhibited a troubling pattern: GPU utilization hovered around 70%, meaning nearly a third of the expensive GPU hardware was sitting idle during each proof cycle.

The assistant had recently implemented "waterfall timeline instrumentation"—a detailed logging system that recorded wall-clock timestamps for every synthesis start, synthesis end, GPU pickup, and GPU completion event. The results, rendered as a text-based waterfall chart in [msg 1854], were starkly revealing:

        P1  SSSSSSSSSGGGGGGGG                                                   
        P2           SSSSSSSSSSSGGGGGGGG                                        
        P3                      SSSSSSSSSSSGGGGGGGG                             
        P4                                 SSSSSSSSSSGGGGGGGG                   
        P5                                           SSSSSSSSSSSGGGGGGGGG

Each proof's CPU synthesis (S) ran strictly sequentially—one after the other—while GPU work (G) overlapped with the next proof's synthesis. The arithmetic was damning: synthesis averaged 39 seconds per proof, while GPU time averaged only 27 seconds. The GPU finished its work and then waited 12 seconds for the next synthesis to complete. This was the structural GPU idle gap, and it was baked into the architecture.

The root cause was architectural: the engine's synthesis task ran as a single tokio::spawn loop that called process_batch().await, blocking the loop until synthesis completed before it could pull the next request from the scheduler. The synthesis_lookahead channel (capacity 1) provided some buffering but could not overcome the fundamental sequentiality of the synthesis task itself.

The Parallel Synthesis Implementation

In messages [msg 1857] through [msg 1866], the assistant designed and implemented a fix. The approach was elegant: replace the single sequential synthesis loop with a tokio-based semaphore pattern. Instead of awaiting process_batch directly, the loop would:

Acquire a permit from a tokio::sync::Semaphore (blocking if synthesis_concurrency tasks were already running)
Spawn process_batch as a fire-and-forget tokio task
Immediately return to pull the next request from the scheduler This allowed multiple proofs to be synthesized concurrently, with the semaphore providing controlled backpressure. The GPU channel's bounded capacity provided natural secondary backpressure—if the GPU fell behind, the channel would fill and the semaphore would eventually block new synthesis tasks. The implementation touched three files: cuzk-core/src/config.rs gained a synthesis_concurrency field in PipelineConfig, cuzk-core/src/engine.rs was refactored to use the semaphore pattern, and cuzk.example.toml was updated to document the new parameter. The build succeeded cleanly ([msg 1865], [msg 1866]).

The Config File as Hypothesis

Message [msg 1868] is where this implementation work meets the testing ground. The assistant writes /tmp/cuzk-parallel.toml—a new configuration file specifically for the parallel synthesis experiment. This is a deliberate choice: rather than modifying the existing /tmp/cuzk-waterfall.toml (which served as the baseline for the waterfall instrumentation), the assistant creates a separate file. This preserves the ability to run controlled comparisons between the old sequential architecture and the new parallel one.

The config file likely contains synthesis_concurrency = 2 as its key experimental parameter, alongside the existing synthesis_lookahead = 2 setting. The choice of concurrency = 2 is itself a hypothesis: the assistant calculated that with two concurrent syntheses each taking ~39 seconds, a new proof would become available every ~20 seconds (39/2), which is less than the GPU's 27-second processing time. If this held, the GPU would never idle—utilization would approach 100%.

The assistant also verified that the system had sufficient memory for two concurrent syntheses. In [msg 1854], they ran free -g and confirmed 754 GiB total RAM with 322 GiB used and 490 GiB available. Each synthesis holds approximately 136 GiB of intermediate data (10 partitions), so two concurrent syntheses would require ~272 GiB, plus 26 GiB for PCE and 44 GiB for SRS, totaling ~342 GiB—well within the available memory.

Assumptions Embedded in the Config

The config file encodes several assumptions, some explicit and some implicit:

Explicit assumption: Setting synthesis_concurrency = 2 assumes that the CPU has sufficient cores to run two full 10-partition syntheses without catastrophic slowdown. The system has 96 cores, and each synthesis uses rayon's default thread pool. The assistant implicitly assumed that 96 cores divided among two syntheses (roughly 48 cores each) would still complete synthesis faster than the sequential 39-second baseline.

Implicit assumption: The config assumes that the GPU's CPU-bound work (particularly the b_g2_msm step, which runs multi-threaded Pippenger on the CPU during GPU proving) would not significantly compete with synthesis for cores. This assumption would prove to be the critical blind spot.

Implicit assumption: The config assumes that synthesis_lookahead = 2 (channel capacity of 2 completed syntheses) provides sufficient buffering to absorb scheduling jitter between the parallel synthesis tasks and the GPU consumer.

Methodological assumption: By creating a separate config file rather than modifying the baseline, the assistant assumes that the only relevant difference between the two experiments is the synthesis_concurrency parameter. This is sound experimental practice—it ensures that any performance difference can be attributed to the parallelism change rather than incidental configuration drift.

What Happened Next: The Benchmark Results

The daemon was started with the new config in [msg 1869], and the first benchmark ran in [msg 1871] with -j 3 (three in-flight client requests). The results were simultaneously validating and disappointing:

| Config | s/proof | GPU util | GPU avg | Synth avg | |---|---|---|---|---| | Sequential (baseline) | 45.3 | 70.9% | 26.7s | 39.3s | | Parallel, -j 3 | 44.7 | 86.5% | 32.0s | inflated |

GPU utilization jumped from 70.9% to 86.5%—the parallel synthesis was working as intended. But the overall throughput improvement was marginal: 44.7 seconds per proof versus 45.3 seconds, barely a 1.3% gain. Worse, synthesis times were inflated: when two syntheses ran concurrently, individual synthesis times stretched from 39 seconds to as much as 54 seconds (42% slower in the worst case). GPU time also increased from 27 seconds to 32 seconds.

The waterfall analysis in [msg 1872] and [msg 1874] told the full story. With synthesis_concurrency=2 and client concurrency -j 2 (a more conservative setting), GPU utilization hit 99.3%—essentially eliminating the idle gap entirely. Yet throughput improved only to 42.2 seconds per proof, a ~7% gain. The GPU prove time had ballooned to 37.6 seconds on average.

The Deeper Lesson: Bottleneck Shifting

The parallel synthesis experiment revealed a fundamental principle of pipeline optimization: eliminating one bottleneck merely reveals the next. The waterfall instrumentation had identified GPU idleness as the visible symptom, but the root cause was not simply sequential synthesis—it was that the CPU was a shared resource between synthesis and the GPU's CPU-bound work.

The b_g2_msm step, which runs on the CPU during GPU proving, uses multi-threaded Pippenger's algorithm across all available cores. When two syntheses were also consuming CPU cores, all three workloads competed for the same 96-core pool. Synthesis slowed down, the GPU's CPU work slowed down, and the net throughput gain was modest despite perfect GPU utilization.

This is the law of diminishing returns in systems optimization: the most visible bottleneck is rarely the only one, and fixing it often shifts the constraint to a less visible but equally fundamental resource. In this case, the scarce resource was not GPU time but CPU core availability during the overlapping window where synthesis and GPU CPU-work must coexist.

Conclusion

Message [msg 1868] is a deceptively simple act of writing a configuration file. But that file represents the culmination of careful diagnostic work (the waterfall instrumentation), a reasoned architectural intervention (the semaphore-based parallel synthesis), and a set of hypotheses about system behavior that would be rigorously tested in the following messages. The config file is the experiment design, encoded in TOML.

The parallel synthesis experiment succeeded in its primary goal—eliminating GPU idle gaps—but revealed a deeper constraint that limited the overall benefit. This is not a failure of the implementation but a success of the diagnostic process: the waterfall instrumentation made the GPU gap visible, the parallel synthesis closed it, and the subsequent analysis made the CPU contention visible in turn. Each cycle of measurement, intervention, and analysis peels back another layer of the system's behavior, revealing the next bottleneck to be addressed.

The config file written in this message is a testament to the iterative nature of performance engineering: you form a hypothesis, you encode it in configuration, you test it, and you learn. The learning from this experiment would inform the next phase of optimization—not more parallelism, but reducing the absolute CPU time of synthesis through specialized MatVec kernels and pre-sorted SRS access patterns.