The Last Partition Worker: Why pw=14 Delivered No Gain and 57 GiB of Waste

Subject Message: [assistant] **pw=14: 37.8s/proof!** Let me check RSS: [bash] cat /tmp/rss-nodebug-pw14.log | awk -F'RSS: ' '{print $2}' | sort -t. -k1 -n | tail -5 443.9 GiB 443.9 GiB 445.4 GiB 446.4 GiB 456.9 GiB

At first glance, this message from an opencode optimization session looks like a routine benchmark result: the assistant has tried a new configuration parameter (partition_workers = 14, or pw=14) and reports both throughput and peak memory. But beneath the terseness lies a pivotal moment in a months-long optimization campaign. This single data point — 37.8 seconds per proof with 456.9 GiB peak RSS — tells a story of diminishing returns, a system hitting its fundamental bottleneck, and the disciplined empirical methodology required to find the true optimum in a complex GPU proving pipeline.

The Optimization Arc That Led Here

To understand why this message matters, one must appreciate the journey that preceded it. The assistant had been working on Phase 12 of the cuzk SNARK proving engine, a Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep). Earlier phases had identified that the pipeline's peak memory footprint (~200 GiB in earlier designs) was a critical problem, causing out-of-memory (OOM) failures on the target hardware (a dual-GPU server with 755 GiB of DDR5 memory).

The breakthrough in Phase 12 was a "split API" that decoupled GPU proving work from CPU post-processing, allowing the two GPUs to operate more independently. But this split created a new problem: synthesized partitions could pile up in memory when the CPU synthesis outran the GPU consumption rate. The assistant had just implemented a three-pronged memory backpressure mechanism — early deallocation of evaluation vectors, auto-scaling channel capacity, and holding partition permits through channel sends — that finally allowed pw=12 to run without OOM. Previously, pw=12 crashed at 668 GiB; now it ran successfully at 399.7 GiB peak RSS, delivering 37.7 seconds per proof.

That success naturally raised a question: if pw=12 works and improves over pw=10 (which delivered ~38.5–38.9 s/proof at ~317 GiB), would pw=14 push throughput even higher?

The Experiment: Pushing the Synthesis Parallelism Dial

The assistant's decision to test pw=14 reflects a core engineering instinct: when you remove a bottleneck (memory pressure), you probe to see what the next bottleneck is. The partition_workers parameter controls how many partitions can be synthesized concurrently. Each partition represents a chunk of the Groth16 proof computation — roughly 12 GiB of evaluation vectors that must be synthesized before being sent to the GPU for the heavy number-theoretic transform (NTT) and multi-scalar multiplication (MSM) work.

The experiment was straightforward: create a daemon configuration with partition_workers = 14, start the daemon, run a benchmark of 20 proofs at concurrency 20 (matching the earlier pw=12 benchmark), and measure both throughput and peak RSS. The assistant had already verified that the memory backpressure mechanism was working correctly — the channel capacity auto-scaled to max(synthesis_lookahead, partition_workers) = 14, and the semaphore permit was held through channel send to bound total in-flight outputs.

The result came back: 37.8 seconds per proof. Almost identical to pw=12's 37.7 seconds. But the memory cost was starkly different.

Reading the RSS Numbers

The assistant pipes the RSS log through awk to extract the five highest readings:

443.9 GiB
443.9 GiB
445.4 GiB
446.4 GiB
456.9 GiB

The peak of 456.9 GiB represents a ~57 GiB increase over pw=12's 399.7 GiB — a 14% memory penalty for zero throughput gain. The progression is telling: the values cluster in the 443–446 GiB range with a single spike to 456.9 GiB, suggesting that the system is operating near its steady-state memory ceiling but occasionally experiencing a transient spike, likely when multiple partitions complete synthesis simultaneously and await GPU consumption.

This is precisely the behavior the memory backpressure mechanism was designed to prevent — but at pw=14, the natural throttle of GPU consumption rate means that the extra two synthesis workers simply produce partitions faster than the GPUs can drain them, filling the channel buffer and consuming memory without improving end-to-end throughput.

What This Reveals About the Bottleneck

The near-identical throughput between pw=12 and pw=14 (37.7 vs 37.8 s/proof) is diagnostic. It tells the assistant that the bottleneck is no longer synthesis parallelism — it is something downstream. Earlier analysis in the session had identified DDR5 memory bandwidth contention as a likely candidate. The two GPUs, each performing NTT and MSM operations, compete with the CPU synthesis threads for access to the same memory bus. Adding more synthesis workers (pw=14) cannot improve throughput because the GPUs are already saturated, and the CPU cannot prepare partitions faster than the GPUs can consume them when memory bandwidth is the limiting factor.

This is a classic systems optimization lesson: removing one bottleneck (memory pressure causing OOM) reveals the next one (memory bandwidth contention). The assistant's earlier work on Phase 11 had targeted DDR5 bandwidth with three interventions, and Phase 12's split API was designed to hide GPU latency. But the fundamental physics of the system — two GPUs and multiple CPU cores sharing a memory bus — imposes a ceiling that no amount of software tuning can突破.

The Optimal Configuration Emerges

The message implicitly establishes pw=12 as the optimal partition_workers setting. The data across the session now tells a clear story:

The Broader Engineering Lesson

This message, for all its brevity, exemplifies a rigorous approach to performance optimization. The assistant does not assume that more parallelism is better; it tests the hypothesis empirically. It does not report only throughput; it always checks memory consumption, understanding that in a memory-constrained environment, peak RSS is as important as latency. And it does not stop at the first successful configuration; it probes the edges of the parameter space to confirm that the chosen operating point is truly optimal.

The result — 37.8 s/proof at 456.9 GiB — is a negative result in the sense that it does not improve on pw=12. But negative results are essential in engineering: they define the boundary of the feasible region, prevent wasted effort on fruitless tuning, and provide confidence that the chosen configuration is near the true optimum. The assistant's next steps, visible in the surrounding conversation, involve documenting this finding and moving on to other optimization targets, secure in the knowledge that the synthesis parallelism dimension has been fully explored.

In the broader narrative of the cuzk proving engine optimization, this message marks the moment when the synthesis side of the pipeline was declared "good enough" and attention shifted to the remaining bottlenecks — GPU utilization, memory bandwidth, and the PCIe transfer path. It is a small message with large implications, and it deserves to be read not as a routine benchmark update but as a carefully considered experimental result that closes one chapter of the optimization story and opens the next.