Planning a Systematic Benchmark Campaign: The Low-Memory Sweep

In the course of optimizing the cuzk SNARK proving engine for Filecoin's PoRep pipeline, a critical question emerged: how does the engine behave on systems with limited memory? The Phase 12 optimizations had delivered impressive throughput—37.7 seconds per proof at 400 GiB peak RSS—but this assumed a server-class machine with ample RAM. Real-world deployments, especially in heterogeneous cloud rental markets, span a wide range of hardware configurations. A 128 GiB machine is far cheaper than a 768 GiB one, but can it run the proving engine at all? And if so, at what throughput?

The user's request in [msg 3272] was direct and practical: "Run cuzk with lower parallelsms to see how low we can get RSS requirements on smaller systems and the timings. Try 1/2/5/7, also lower queues/gpu workers that would also hold a lot of memory." This was not a vague exploration—it was a targeted request to characterize the engine's memory-versus-throughput tradeoff curve, with specific configuration values to test. The user understood the architecture well enough to know that partition_workers (pw) and gpu_workers_per_device (gw) were the primary knobs controlling memory footprint, and they wanted to see the full spectrum from minimal memory (pw=1) through the known-good configuration (pw=12).

The Subject Message: A Planning Artifact

The assistant's response in [msg 3274] is, on its surface, unremarkable: a todo list enumerating the benchmark steps. It reads:

[todowrite] {"todos":[{"content":"Create config files for pw=1/2/5/7 with gw=1 gt=32","priority":"high","status":"in_progress"},{"content":"Run benchmark pw=1 gw=1 and measure RSS + timing","priority":"high","status":"pending"},{"content":"Run benchmark pw=2 gw=1 and measure RSS + timing","priority":"high","status":"pending"},{"content":"Run benchmark pw=5 gw=1 and measure RSS + timing","priority":"high","status":"pending"},{"content":"Run benchmark pw=7 gw=1 and measure RSS + timing","priority":"high","status":"pending"},{"content":"Run benchmark pw=7 gw=2 and measure RSS + timing","priority":"medium","status":"pending"},{"content":"Run benchmark pw=5 gw=2 and measure RSS + timing","priority":"medium","status":"pending"},{"content":"Compile results and write deployment guidance","priority":"high","status":"pending"}]}

This is a planning artifact—a structured decomposition of a multi-step empirical investigation. But to understand why this message matters, we must look beneath its surface. This todo list encodes a sophisticated experimental design, reflects deep architectural knowledge, and reveals the assistant's reasoning about which variables matter and how to isolate them.

Why This Message Was Written

The message was written in direct response to the user's request, but it was not a simple acknowledgment. The assistant had just spent the previous message ([msg 3273]) exploring the cuzk configuration structure via a subagent task, reading config.rs and related files to understand which knobs affect memory. That exploration was necessary because the user's request mentioned "lower queues/gpu workers" in addition to partition_workers—the assistant needed to know exactly which configuration parameters existed and how they interacted.

The todo list represents the synthesis of that exploration into an actionable plan. The assistant had to decide:

• Which parameter combinations to test
• What order to run them in (pw=1 first, then ascending)
• What metrics to collect (RSS + timing)
• How many proofs to run per configuration
• What concurrency level to use The choice of gw=1 (single GPU worker) for the primary sweep, with gw=2 as a secondary comparison at pw=5 and pw=7, reveals a key assumption: that at low partition_worker counts, the second GPU worker would be idle because synthesis cannot keep the GPU fed. This assumption turned out to be correct—the benchmarks later showed that gw=2 provides no throughput benefit below pw=10.

The Experimental Design Embedded in the Todo List

The todo list is ordered strategically. The assistant planned to run pw=1 first because it would be the slowest (serial synthesis) but also the most informative about the memory baseline. Each subsequent configuration adds one more partition worker, allowing the assistant to isolate the marginal memory cost per worker.

The priority assignments are telling: pw=1/2/5/7 with gw=1 are all "high" priority—these are the core sweep. The gw=2 variants at pw=5 and pw=7 are "medium" priority—comparison points that could be skipped if time runs short. This prioritization reflects an understanding that the primary goal is characterizing the memory scaling curve, and the gw=2 comparison is secondary.

Notably absent from the initial plan: pw=10 and pw=12. The user explicitly asked for 1/2/5/7, and the assistant respected that scope. However, as the benchmarks progressed (visible in later messages), the assistant voluntarily extended the sweep to include pw=10 and pw=12 with both gw=1 and gw=2, recognizing that the curve needed those upper points to be complete.

Assumptions and Knowledge Required

To understand this message, one must know:

• What partition_workers controls: the number of CPU threads that synthesize circuit partitions in parallel. Each worker holds one partition's worth of intermediate data (~16-20 GiB).
• What gpu_workers_per_device controls: the number of concurrent GPU proof pipelines. Each worker holds GPU-side state including NTT evaluation vectors.
• The baseline memory footprint: ~69 GiB from SRS (44 GiB) + PCE (25.7 GiB), independent of pw/gw.
• The Phase 12 architecture: the split API that decouples b_g2_msm from the GPU worker loop, and the memory backpressure mechanisms (early a/b/c free, channel capacity auto-scaling, permit-through-send). The assistant assumed that RSS (resident set size) would be the right metric for memory pressure, and that /proc/PID/status (VmRSS/VmHWM) would provide accurate measurements. It also assumed that running 5 proofs at concurrency 5 would be sufficient to reach steady-state memory usage—an assumption validated by the consistent results across runs.

What This Message Created

This todo list was the blueprint for a benchmark campaign that produced concrete, actionable knowledge:

• A clean linear memory scaling formula: ~69 GiB baseline + pw × ~20 GiB
• Deployment guidance for every common system size: 128 GiB (pw=2, 152s/proof), 256 GiB (pw=5, 68.4s/proof), 384 GiB (pw=10, 42.5s/proof), 768 GiB (pw=12, 37.7s/proof)
• The discovery that gw=2 provides no benefit below pw=10 due to synthesis starvation
• The surprising finding that gw=2 adds no memory at low pw because the channel is already drained This knowledge was immediately written into the project's documentation (cuzk-project.md and cuzk.example.toml) and committed as part of the 9bb657e5 commit, making it available to every future deployer of the engine.

The Thinking Process

The todo list reveals the assistant's structured thinking: decompose the user's request into atomic steps, order them by dependency (config files must exist before benchmarks can run), assign priorities based on information value, and leave room for opportunistic extension (the gw=2 variants). The assistant recognized that this was not just a matter of running a few commands—it was a systematic characterization that would produce a deployment guide, and the plan needed to be comprehensive enough to support that goal.

The message also shows the assistant's discipline in using the todowrite tool to track progress. This is not merely cosmetic—it creates a persistent record of what has been done and what remains, enabling the assistant to resume work after interruptions and to communicate progress to the user. In a long-running optimization session spanning dozens of messages, this tracking is essential for maintaining coherence.

Conclusion

Message [msg 3274] is a planning message, but it is planning in service of a deeper goal: transforming the cuzk engine from a black box with unknown memory characteristics into a well-characterized system with documented deployment guidance. The todo list is the visible artifact of a sophisticated experimental design that balanced information value against execution cost, respected the user's scope while leaving room for extension, and ultimately produced the data needed to guide real-world deployments across the entire spectrum of available hardware.