The Methodical Benchmarker: A Single Message in the cuzk Low-Memory Sweep

Introduction

In the lifecycle of any performance-critical software system, there comes a moment when the intense phase of feature engineering and optimization gives way to a quieter, equally important phase: systematic characterization. The engineer must step back from the whiteboard of architectural improvements and ask the practical question: what does this actually cost, and for whom does it work? This article examines a single message from an opencode coding session—message index 3294—in which an AI assistant launches a benchmark run to measure the memory footprint and throughput of the cuzk Groth16 proving engine at a specific configuration point. Though brief in appearance, this message crystallizes the transition from optimization to production readiness, and reveals the careful reasoning that underpins a methodical performance investigation.

The Message

The subject message reads as follows:

[assistant] Now I already know pw=1 results (104 GiB, ~290s/proof). Let me run pw=2 first. With pw=2, a sector takes ~29s×5=145s for synthesis (2 workers doing 10 partitions means ~5 rounds), so 3 proofs at j=3 should take about 7-8 min:

>

[bash] /tmp/cuzk-lowmem-bench.sh /tmp/cuzk-lowmem-pw2-gw1.toml lowmem-pw2-gw1 3 3 === Starting benchmark: lowmem-pw2-gw1 === Config: /tmp/cuzk-lowmem-pw2-gw1.toml Count: 3, Concurrency: 3 Daemon log: /home/theuser/cuzk-lowmem-pw2-gw1.log Daemon PID: 1572438 Daemon ready after 28s Baseline RSS: 69 GiB Running benchmark... [2m2026-02-20T15:46:06.315453Z[0m [32m INFO[0m [2mcuzk_bench[0m[2m:[0m loading C1 output [3mpath[0m[2m=[0m/data/32gbench/c1.json === Batch Benchmark === proof type: porep count: 3 concurrency: 3

>

[1/3] COMPLETED — 155.5s (...

At first glance, this appears to be a routine execution command. The assistant launches a benchmark script with specific parameters and the output begins to stream in. But embedded within this short message is a dense layer of reasoning, context, and decision-making that merits close examination.

The Context: From Engineering to Characterization

To understand why this message was written, we must trace the preceding conversation. The session had just completed Phase 12 of the cuzk engine—a major architectural intervention that introduced a split GPU proving API, memory backpressure mechanisms, and channel capacity auto-scaling. Phase 12 had been a grueling engineering effort spanning multiple iterations: a two-lock design was attempted and abandoned ([msg 3288]), a use-after-free bug was discovered and fixed, and the memory footprint was brought under control through early deallocation of synthesis vectors and partition permit-through-send semantics. The result was a stable engine capable of producing proofs at 37.7 seconds each with a peak memory of approximately 400 GiB at pw=12 (12 partition workers).

But 400 GiB is not accessible to every operator. The user, recognizing this, issued a request at message 3272: "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 request reframes the problem. No longer is the goal "maximize throughput at any memory cost"; the goal is now "characterize the full spectrum of memory/throughput tradeoffs so that operators with 128 GiB, 256 GiB, or 512 GiB systems can make informed deployment decisions."

The assistant's response to this request was not to immediately start running benchmarks. Instead, it first explored the codebase to understand the memory-affecting configuration knobs ([msg 3273]), created a todo list ([msg 3274]), examined existing config files ([msg 3275]), and wrote configuration files for each target partition_workers value ([msg 3276]). It then ran the first benchmark (pw=1 gw=1) using an ad-hoc approach with a background RSS monitor, but encountered issues: the monitor process produced an empty log file, and the daemon's PID resolution was complicated by multiple matching processes (<msg id=3283-3287>). Despite these difficulties, the assistant extracted the key result: pw=1 achieved a peak RSS of only 104 GiB but required approximately 290 seconds per proof due to fully serialized synthesis.

Recognizing that the ad-hoc approach was fragile, the assistant wrote a proper benchmark script at message 3292—/tmp/cuzk-lowmem-bench.sh—that handles daemon startup, RSS monitoring, benchmark execution, and cleanup in a single orchestrated workflow. This script was made executable at message 3293. The subject message (3294) is the first use of this new script.

Reasoning and Decision-Making

The subject message opens with a concise summary of what is already known: "Now I already know pw=1 results (104 GiB, ~290s/proof)." This statement serves multiple purposes. It confirms that the previous benchmark completed successfully despite the tooling issues, it establishes a baseline for comparison, and it signals that the assistant is tracking results across the sweep rather than treating each run as an isolated experiment.

The assistant then performs a mental calculation to estimate the runtime for pw=2: "With pw=2, a sector takes ~29s×5=145s for synthesis (2 workers doing 10 partitions means ~5 rounds), so 3 proofs at j=3 should take about 7-8 min." This reasoning reveals the assistant's internal model of how the engine works. The 29-second figure is the per-partition synthesis time, derived from the pw=1 run where 10 partitions were processed sequentially (10 × 29s ≈ 290s total synthesis time per proof). With two partition workers, the 10 partitions are divided into 5 rounds of 2 parallel syntheses each, yielding 5 × 29s = 145s for synthesis. Adding the GPU prove time (~33s from the pw=1 results) gives approximately 178s per proof. With concurrency 3, three proofs overlap, so the total wall time is approximately 3 × 178s / 3 = 178s plus queueing overhead—roughly 3 minutes per proof, or about 7-8 minutes for all three. This estimate is remarkably close to the actual result (152s/proof throughput, yielding ~7.6 minutes for three proofs).

The choice of benchmark parameters—3 proofs at concurrency 3 rather than 5 at 5—is itself a decision worth examining. The assistant could have used the same parameters as the pw=1 run (5 proofs, concurrency 5), but that would have required approximately 12-13 minutes for pw=2. By reducing to 3 proofs at concurrency 3, the assistant trades some statistical confidence (fewer samples) for faster turnaround. This is a pragmatic choice: the primary goal is to characterize the memory/throughput relationship across multiple config points, not to achieve high statistical precision at a single point. Three proofs at matching concurrency still provide steady-state measurements (the engine reaches thermal equilibrium after the first proof or two), and the faster turnaround allows the sweep to complete in a reasonable timeframe.

Assumptions Embedded in the Message

Every benchmark carries assumptions, and this message is no exception. The assistant assumes that the newly-written benchmark script works correctly—that it starts the daemon, monitors RSS via /proc/PID/status, runs the bench tool, and captures results without error. This assumption is justified by the script's design (it was written specifically to address the fragility of the earlier ad-hoc approach), but it remains untested at the moment of execution.

The assistant assumes that the daemon from the previous pw=1 run has been properly terminated. The preceding messages show the assistant killing daemon processes with pkill -f "cuzk-daemon" and verifying with pgrep (<msg id=3289-3291>). However, the PID-based killing had earlier produced confusing results due to multiple matching processes ([msg 3286]), so there is some residual risk of port conflicts or stale state.

The assistant assumes that the synthesis time per partition (~29s) is invariant with respect to the number of partition workers. This is a reasonable first-order approximation—synthesis is CPU-bound and partition workers operate independently on different partitions—but it may break down if there is contention for shared resources (e.g., L3 cache, memory bandwidth, or SRS access). The Phase 11 analysis had identified DDR5 memory bandwidth contention as a bottleneck ([msg 3288]), and adding more partition workers could exacerbate this contention, potentially increasing per-partition synthesis time. The assistant's estimate of 145s for synthesis at pw=2 implicitly assumes no such degradation.

The assistant assumes that 3 proofs at concurrency 3 provides sufficient data for characterization. For a system with no warm-up effects, a single proof might suffice. But the cuzk engine has significant state (SRS loaded into pinned memory, GPU context initialization, thread pool warm-up) that may cause the first proof to be slower or more memory-intensive than subsequent ones. Running 3 proofs allows the assistant to observe whether the system reaches a steady state and to compute an average.

Input Knowledge Required

To fully understand this message, a reader needs familiarity with several domains. First, the cuzk engine architecture: the concept of partition workers (partition_workers or pw), which are CPU threads that synthesize circuit partitions in parallel; GPU workers (gpu_workers_per_device or gw), which handle the GPU-side MSM and NTT computations; and the pipeline that connects them via bounded channels. Second, the Groth16 proving pipeline for Filecoin's Proof-of-Replication (PoRep): the 10-partition structure of the circuit, the ~29s per-partition synthesis time, and the ~33s GPU prove time. Third, Linux memory measurement: the distinction between VmRSS (current resident memory) and VmHWM (peak resident memory) in /proc/PID/status, and the fact that RSS is measured in kilobytes. Fourth, the benchmark tooling: cuzk-bench batch with its --count and --concurrency flags, and the meaning of the output fields (prove time, queue time, wall time).

The message also assumes knowledge of the preceding conversation: the pw=1 results (104 GiB, ~290s/proof), the existence and location of the benchmark script (/tmp/cuzk-lowmem-bench.sh), and the config file naming convention (/tmp/cuzk-lowmem-pw2-gw1.toml). Without this context, the message's opening statement—"Now I already know pw=1 results"—would seem cryptic.

Output Knowledge Created

The immediate output of this message is the pw=2 gw=1 benchmark data point. The subsequent message ([msg 3295]) reveals the results: 110 GiB peak RSS, 152 seconds per proof throughput, and 33.7 seconds average prove time. This data point, combined with the pw=1 results and the later pw=5, pw=7, pw=10, and pw=12 runs, enables the assistant to derive a linear memory scaling formula (~69 GiB baseline + pw × ~20 GiB) and to provide concrete deployment guidance for systems ranging from 128 GiB to 768 GiB ([msg 3309]).

But the output is not just a number. The successful execution of this benchmark validates the benchmark script itself, establishing a repeatable methodology for the remaining config points. It also validates the assistant's mental model of the engine: the estimate of 7-8 minutes for 3 proofs at pw=2 was accurate, confirming that the synthesis time model (29s per partition, linear scaling with workers) is a reasonable approximation. This confidence carries forward into the subsequent benchmarks, where the assistant makes similarly accurate estimates.

The Broader Significance

This message, though brief, exemplifies a pattern that recurs throughout engineering: the transition from building to measuring. The Phase 12 engineering effort had consumed many messages, involving complex debugging of use-after-free bugs, memory backpressure tuning, and channel capacity optimization. But once the engine was stable, the question shifted from "does it work?" to "how well does it work, and for whom?" The low-memory benchmark sweep is the answer to that question.

The message also illustrates the importance of tooling in systematic investigation. The assistant's first attempt at measuring RSS (a background subshell writing to a log file) failed due to process lifecycle issues. Rather than continuing with fragile ad-hoc methods, the assistant invested in a proper benchmark script. This investment paid immediate dividends: the pw=2 run completed cleanly, and the remaining configs (pw=5, pw=7, pw=10, pw=12) were executed with the same script without incident. The discipline to fix the measurement tool before continuing the investigation is a hallmark of rigorous engineering.

Conclusion

Message 3294 is a single data point in a systematic characterization of the cuzk proving engine's memory/throughput tradeoffs. It represents the confluence of several threads: the completion of Phase 12 engineering, the user's request for low-memory deployment guidance, the assistant's exploration of configuration knobs, the creation of a robust benchmark script, and the execution of a methodical sweep. The message reveals the assistant's mental model of the engine, its ability to estimate runtime from first principles, its pragmatic choices about benchmark parameters, and its commitment to repeatable methodology. In the broader arc of the conversation, this message is the moment when the project shifts from "can we make it faster?" to "how should different users configure it?"—a transition that marks the difference between a research prototype and a production system.