The Plateau at 38 Seconds: A Pivotal Benchmark in the cuzk SNARK Optimization Journey

In the middle of an intensive optimization campaign targeting the cuzk SNARK proving engine for Filecoin's Proof-of-Replication (PoRep), the assistant sent a deceptively simple message that would become the pivot point for the entire project. The message, indexed as <msg id=2675>, reads:

c=15 j=15: 38.2s/proof throughput. Still improving slightly. Let me try c=20 j=15:

This single line, followed by a benchmark invocation, marks the moment when a months-long optimization effort shifted from chasing GPU-level parallelism to confronting a more fundamental system bottleneck: DDR5 memory bandwidth contention. Understanding why this message matters requires tracing the intricate chain of reasoning, failure, and discovery that led to it.

The Arc of Optimization: From GPU Interlock to Memory Bandwidth

To appreciate the significance of <msg id=2675>, one must understand what came before it. The cuzk project had been systematically working through a series of optimization phases, each targeting a different bottleneck in the Groth16 proof generation pipeline for Filecoin PoRep. The pipeline is extraordinarily complex: it involves synthesizing 10 circuit partitions per proof, each requiring CPU-side constraint evaluation followed by GPU-side multi-scalar multiplication (MSM), number-theoretic transform (NTT), and other cryptographic operations. The peak memory footprint hovers around 200 GiB, and a single proof takes roughly 30–40 seconds end-to-end.

Phases 1 through 9 had progressively improved throughput by optimizing the GPU kernel pipeline, reducing PCIe transfer overhead, and refining the CPU-GPU interlock mechanism. Phase 9, in particular, had achieved a single-lock design where the GPU mutex was released before the CPU-side b_g2_msm computation completed, allowing the next partition's GPU work to begin while the previous partition's CPU post-processing still ran. This overlapping strategy was elegant and effective, yielding approximately 41.3 seconds per proof at high concurrency.

Then came Phase 10. The assistant designed an ambitious two-lock GPU interlock that would split the GPU mutex into a mem_mtx (for VRAM allocation and pre-staging) and a compute_mtx (for kernel execution). The idea was to allow one worker to pre-stage its GPU buffers while another worker was still computing, creating deeper overlap and higher GPU utilization. But the design ran headlong into a hard physical constraint: the RTX A6000 GPU has only 16 GB of VRAM, and each worker's pre-staged buffers consumed approximately 12 GB. When two workers tried to coexist on the same device, the second worker's cudaMalloc call failed with an out-of-memory error. The fundamental flaw was that CUDA memory management APIs like cudaDeviceSynchronize and cudaMemPoolTrimTo are device-global operations—they cannot be partitioned per-lock. The two-lock design was architecturally elegant but physically impossible on the available hardware.

The Reversion and the Sweep

Faced with this failure, the assistant made a disciplined decision: revert to Phase 9's proven single-lock code and run a comprehensive concurrency sweep to establish a clean baseline. This was not retreat—it was recalibration. The assistant needed to understand, with fresh data, where the actual bottleneck lay now that the GPU pipeline itself was well-optimized.

The sweep began with <msg id=2672>: c=5 j=5 (5 proofs, concurrency 5), yielding 41.4 seconds per proof throughput. Then <msg id=2673>: c=10 j=10, yielding 38.7 seconds per proof—a clear improvement, showing that higher concurrency was smoothing out pipeline bubbles. Then <msg id=2674>: c=15 j=15, yielding 38.2 seconds per proof—a smaller improvement, suggesting the system was approaching a ceiling.

This is the precise moment captured in <msg id=2675>. The assistant reports the 38.2s result with the telling phrase "Still improving slightly." There is a note of cautious optimism but also a hint of concern: the improvements are decelerating. The marginal gain from c=10 to c=15 was only 0.5 seconds, compared to the 2.7-second gain from c=5 to c=10. The throughput curve was flattening.

The assistant's next move—"Let me try c=20 j:15"—is a deliberate diagnostic probe. By increasing the batch count to 20 while keeping concurrency at 15, the assistant is testing whether the plateau is a genuine throughput ceiling or merely an artifact of insufficient work-in-progress. If throughput improved further with c=20, it would mean the pipeline could absorb more concurrent work and the bottleneck was elsewhere. If throughput stayed flat, it would confirm that the system had reached a hard limit at approximately 38 seconds per proof.

The Reasoning Behind the Probe

The decision to test c=20 j=15 rather than c=20 j=20 is itself revealing. The assistant had already observed that concurrency 15 was the highest tested; pushing concurrency to 20 might have risked overwhelming the system with too many simultaneous synthesis tasks, potentially causing thrashing or OOM on the CPU side. By keeping concurrency at 15 and only increasing the batch count, the assistant isolates the effect of pipeline depth from the effect of parallelism. This is a textbook benchmarking methodology: vary one parameter at a time while holding others constant.

The benchmark output in <msg id=2675> shows the first five of twenty proofs completing. The prove times (the time each proof spent in actual computation, excluding queue wait) range from 48,797 ms to 53,303 ms, with the first proof completing in 63.1 seconds wall-clock. These numbers are consistent with the c=15 j=15 run, suggesting that the plateau is real. The system has reached a throughput ceiling.

What This Message Achieved

This message created critical output knowledge: the confirmation that throughput had plateaued at ~38 seconds per proof regardless of additional concurrency. This was not immediately obvious from the raw numbers—one might have thought that more workers would continue to yield gains—but the systematic sweep revealed the diminishing returns curve.

More importantly, this message set the stage for the waterfall timing analysis that followed. Immediately after this benchmark, the user directed the assistant to explore the waterfall timing tooling (<msg id=2676>: "Start an explore agent to see how to use available waterfall timing tooling, gather waterfall timings and see if it looks as expected"). The assistant then extracted TIMELINE events from the daemon logs and performed a deep analysis that revealed the true bottleneck: DDR5 memory bandwidth contention between the CPU-side synthesis workers and the prep_msm computation.

The waterfall analysis showed that at high concurrency, GPU utilization was 90.8%—excellent—but synthesis times and prep_msm times were both inflating under load as they competed for the same memory channels. The 192 rayon synthesis threads and the 192 groth16_pool threads (running Pippenger for b_g2_msm) were fighting over L3 cache and DDR5 bandwidth, causing both to slow down. The throughput plateau at 38 seconds was not a GPU limit but a memory bandwidth limit.

Assumptions and Knowledge

This message rests on several key assumptions. The assistant assumes that the Phase 9 code is stable and correctly compiled (it had just been reverted via git checkout and rebuilt). It assumes that the benchmark tool (cuzk-bench batch) produces reliable, comparable numbers across runs. It assumes that the daemon configuration (gw=2, partition_workers=10) is optimal based on previous sweeps. And it assumes that the observed throughput numbers are representative of steady-state behavior, not transient startup effects—which is why it runs batches of 15–20 proofs rather than just 3–5.

The input knowledge required to understand this message is substantial. One must know what a Groth16 proof is, how Filecoin's PoRep circuit is structured (10 partitions per proof), what the cuzk pipeline does (synthesis → GPU prove → post-processing), what the Phase 9 single-lock design entails, and why the Phase 10 two-lock design failed. One must also understand the benchmark parameters: c is the number of proofs in the batch, j is the concurrency level (how many proofs are in-flight simultaneously), and gw is the number of GPU workers per device.

The Thinking Process

The assistant's thinking is visible in the structure of the sweep itself. Rather than running a single benchmark and declaring victory, the assistant systematically probes the throughput curve at multiple points: c=5, c=10, c=15, and then c=20. Each step informs the next. The phrase "Still improving slightly" shows the assistant actively interpreting the trend in real-time, not just mechanically executing commands. The decision to try c=20 j=15 (rather than c=20 j=20) reflects a nuanced understanding that concurrency and batch size are different levers.

The assistant is also thinking about the broader optimization strategy. Having just abandoned Phase 10 after discovering its fundamental flaw, the assistant is not simply running benchmarks for their own sake—it is searching for the next bottleneck to attack. The plateau at 38 seconds is the signal that the current optimization approach (more GPU workers, better interlock) has exhausted its potential. The next breakthrough will require a different kind of intervention, one that addresses memory bandwidth rather than GPU utilization.

The Broader Significance

In the larger narrative of the cuzk optimization project, <msg id=2675> is the turning point between two eras. Before it, the project focused on GPU-level optimizations: kernel fusion, PCIe transfer staging, lock granularity. After it, the focus shifted to system-level bottlenecks: DDR5 memory bandwidth, TLB shootdown storms from concurrent munmap() calls, thread pool oversubscription, and the interaction between synthesis and b_g2_msm competing for L3 cache.

The Phase 11 proposal that emerged from this analysis—bounding async deallocation to a single thread, reducing the groth16_pool thread count, and adding a lightweight atomic throttle to pause synthesis during b_g2_msm's 0.4-second window—was a direct response to the plateau discovered in this message. Without the disciplined concurrency sweep and the recognition that throughput had stopped scaling, the team might have continued chasing GPU optimizations that would have yielded diminishing returns.

This message is a masterclass in the art of benchmarking: knowing when to stop optimizing one layer and start investigating the next. The 38.2-second plateau was not a failure—it was a discovery.