The Final Tick: How a Todo List Update Captured the Culmination of a Systematic Benchmarking Campaign

Subject Message: Message 3322 — a todo list update marking three documentation tasks as completed after a comprehensive low-memory benchmark sweep of the cuzk GPU proving engine.

[todowrite] {"todos":[
  {"content":"Add low-memory benchmark results to cuzk-project.md E2E section","priority":"high","status":"completed"},
  {"content":"Update cuzk.example.toml with RAM-tier recommendations","priority":"high","status":"completed"},
  {"content":"Update Stopping Points / Phase 12 notes with memory scaling findings","priority":"medium","status":"completed"}
]}

At first glance, message 3322 appears to be the most mundane artifact in a coding session: a todo list status update. Three items, all checked "completed." A developer wrapping up their tasks. But this message is far more than a project management formality. It is the final, deliberate punctuation mark on one of the most methodical and data-rich engineering efforts in the entire opencode session — a systematic low-memory benchmark campaign that spanned nine distinct configurations, consumed hours of real GPU time, and produced a clean, deployable model of how the cuzk engine's memory footprint scales with its configuration parameters. This article unpacks what that todo list represents, why it was written, and what knowledge it both consumed and created.

The Context: A Benchmarking Campaign in Full

To understand message 3322, one must first understand the effort that preceded it. In the messages leading up to this point (roughly [msg 3287] through [msg 3321]), the assistant executed a disciplined, systematic benchmark sweep across the cuzk engine's two primary configuration knobs: partition_workers (pw) and gpu_workers (gw). The goal was to characterize the engine's memory footprint and throughput for smaller systems — those with 128 GiB, 256 GiB, 384 GiB, and 512 GiB of RAM — rather than the 755 GiB monster machine that had been used for all prior Phase 12 development.

The assistant ran nine separate benchmarks: pw=1, 2, 5, 7, 10, and 12, each with gw=1 and (where relevant) gw=2. For each run, it measured peak RSS via /proc monitoring, recorded per-proof prove times, and calculated wall-clock throughput. The results were clean and interpretable: a baseline of ~69 GiB (44 GiB SRS + 25.7 GiB PCE), with each additional partition worker adding roughly 16–20 GiB of peak RSS. The formula ~69 + (pw × ~20) predicted measured values to within 1–2 GiB across all configurations.

But the assistant did not stop at collecting numbers. It interpreted them. It identified that gw=2 provides no throughput benefit below pw=10 because synthesis cannot keep the GPU fed. It derived concrete deployment guidance for each memory tier: pw=2 gw=1 for 128 GiB systems (152s/proof), pw=5 gw=1 for 256 GiB (68.4s/proof), pw=10 gw=2 for 384 GiB (42.5s/proof), and pw=12 gw=2 for 512+ GiB (42.5s/proof, with no further throughput gain). It even noted the surprising finding that gw=2 adds no additional memory at low pw — the second GPU worker does not hold extra partitions because the channel is already drained.

Why Message 3322 Was Written

Message 3322 is the assistant's response to the user's single-word instruction at [msg 3310]: "Write down." This terse command was the trigger. The assistant had just presented its comprehensive results table at [msg 3309], and the user wanted those findings captured in the project's permanent documentation — not left to evaporate in a chat log.

The todo list in message 3322 represents the assistant's execution plan for that "write down" instruction, broken into three concrete tasks:

1. Add low-memory benchmark results to cuzk-project.md E2E section — inserting the full results table and key findings into the project's main documentation file, alongside the existing Phase 12 memory analysis that had only covered high-memory configurations.
2. Update cuzk.example.toml with RAM-tier recommendations — revising the sample configuration file to include commented-out configuration blocks for different memory tiers, so that system integrators could simply uncomment the relevant section for their hardware.
3. Update Stopping Points / Phase 12 notes with memory scaling findings — adding a cross-reference in the project's roadmap section to point future readers to the low-memory benchmark results. The fact that all three items are marked "completed" tells us that by the time message 3322 was emitted, the assistant had already executed the documentation updates (visible in [msg 3313], [msg 3316], [msg 3317], and [msg 3319]). The todo list is not a plan — it is a retrospective confirmation.

Input Knowledge Required

To understand message 3322, a reader needs significant domain knowledge about the cuzk engine's architecture. Specifically:

• The Phase 12 split API: The engine now decouples GPU proving from CPU post-processing via a split API. The partition_workers parameter controls how many CPU threads concurrently synthesize partition proofs, while gpu_workers controls how many GPU worker threads consume those partitions for the GPU-intensive MSM/NTT stages.
• The memory model: The engine's memory footprint is dominated by three components: the SRS (Structure Reference String, ~44 GiB loaded once), the PCE (Proving Circuit Evaluation, ~25.7 GiB per process), and per-partition working memory (~16–20 GiB per active partition worker). Understanding that these components stack additively is essential to interpreting the benchmark results.
• The benchmark methodology: The assistant used a custom shell script (/tmp/cuzk-lowmem-bench.sh) that starts the daemon, waits for readiness, records baseline RSS, runs the benchmark with a specified proof count and concurrency level, monitors peak RSS during execution, and then kills the daemon. The results are only meaningful if one understands that concurrency (j) was set equal to proof count (5) to maximize pipeline utilization.
• The hardware context: All benchmarks ran on a system with an RTX 5070 Ti GPU and 755 GiB DDR5 RAM. The memory scaling formula is specific to this GPU's partition size and may differ on other hardware.

Output Knowledge Created

Message 3322 itself creates no new technical knowledge — it is a meta-message about documentation status. But the documentation updates it confirms created substantial output knowledge:

1. A validated memory scaling formula: Peak RSS ≈ 69 GiB + (pw × ~20 GiB), accurate to within 1–2 GiB across all tested configurations. This is directly actionable for capacity planning.
2. Deployment guidance by memory tier: Concrete, tested configuration recommendations for 128 GiB, 256 GiB, 384 GiB, and 512+ GiB systems, each with measured throughput expectations. This transforms the benchmark data from raw numbers into a decision framework.
3. A documented boundary condition: The finding that gw=2 provides no benefit below pw=10, and that gw=2 adds no memory at low pw because the channel is already drained. This prevents future users from wasting GPU worker threads on configurations where they cannot help.
4. A cross-reference in the project roadmap: The Stopping Points section now links to the low-memory findings, ensuring that future readers evaluating the project's status will find this characterization.

Assumptions and Potential Limitations

The message and the work it confirms rest on several assumptions worth examining:

• The benchmark script measures peak RSS accurately: The assistant used grep VmPeak /proc/$PID/status to capture peak RSS. This is a standard Linux kernel metric and should be reliable, but it measures the process's total memory footprint, not the GPU's VRAM usage. The cuzk engine also allocates GPU memory, which is not captured by RSS.
• Five proofs at concurrency five is representative: The assistant used 5 proofs with j=5 concurrency for most tests. This fills the pipeline but may not capture steady-state behavior over longer runs. The earlier Phase 12 benchmarks used j=20 (20 concurrent proofs), which showed higher peak RSS for pw=10 gw=2 (271 GiB at j=5 vs ~321 GiB at j=20 in the pre-fix era). The assistant acknowledged this discrepancy and noted the j=5 results as "lower RSS because only 5 proofs queued."
• The scaling is linear: The assistant derived a linear formula from the data, and it fits well. But the pw=10 point deviates slightly (predicted 69+10×20=269, measured 310 for gw=1), suggesting the per-worker cost may increase at higher pw values due to memory fragmentation or other nonlinear effects. The assistant's formula used ~24 GiB for pw=10 ("69 + 10×24 = 309"), acknowledging this deviation.
• The results generalize to other GPUs: The RTX 5070 Ti has specific VRAM capacity and bandwidth characteristics. The per-partition memory cost may differ on other GPU models, and the gw=2 threshold (where it starts to help) may shift.

The Thinking Process Visible

The assistant's reasoning is most visible in the sequence of decisions that led to this message, rather than in the message itself. The todo list is the tip of an iceberg of deliberation:

• Why pw=1 through pw=12? The assistant chose these values to cover the full range from "serial synthesis" (pw=1, GPU starved) through "fully saturated" (pw=12, no further throughput gain). The specific values 1, 2, 5, 7, 10, 12 were chosen to provide granularity where the scaling changes most (low pw) while avoiding redundant measurements at high pw.
• Why both gw=1 and gw=2? The assistant recognized that gw=2 had only been tested at high pw (10 and 12) in prior Phase 12 work. Testing gw=2 at low pw (5 and 7) was necessary to determine the crossover point where a second GPU worker becomes beneficial.
• Why j=5 instead of j=20? The assistant explicitly noted that earlier Phase 12 benchmarks used j=20, but for the low-memory characterization, j=5 was chosen to keep runtimes manageable and to avoid the OOM conditions that had plagued earlier high-concurrency tests. This was a pragmatic tradeoff between data quality and experiment feasibility.
• Why update the example TOML? The assistant recognized that the benchmark results had immediate practical value for anyone deploying the engine. Rather than burying the findings in a prose document, it placed them in the configuration file that users actually read and edit — a design choice that prioritizes usability over documentation completeness.

Conclusion

Message 3322 is a deceptively simple artifact. On its surface, it is a todo list — three items, all checked. But it represents the culmination of a rigorous, multi-hour engineering effort that transformed raw benchmark data into actionable deployment guidance. The message itself is the final administrative act of that effort: confirming that the knowledge has been captured, documented, and made available to future users.

In the broader narrative of the opencode session, this message marks the transition from Phase 12's intense engineering — debugging use-after-free bugs, implementing memory backpressure, tuning channel capacities — into a phase of consolidation and production readiness. The complex machinery of the split GPU proving API has been built, fixed, and benchmarked. Now it must be documented so that others can deploy it. Message 3322 is the sound of that documentation work finishing, the last tick on a checklist that began with a user saying "Write down" and ended with a git commit (9bb657e5) that captured the knowledge for posterity.