The Pivot to Automation: Why a Single Shell Script Defined a Benchmark Campaign

In the middle of an intense optimization session for the cuzk Groth16 proving engine—a system designed to generate Filecoin Proofs-of-Replication (PoRep) with ~200 GiB peak memory—a single, unassuming message marks a critical inflection point. The message, from the assistant to itself, reads:

Let me write a proper benchmark script that handles everything — start daemon, monitor RSS, run bench, collect results, then kill daemon: [write] /tmp/cuzk-lowmem-bench.sh Wrote file successfully.

This is message [msg 3292] in a conversation spanning thousands of exchanges. On its surface, it is a trivial action: the assistant decides to write a shell script. But beneath this mundane surface lies a rich story of failure recognition, methodological correction, and the transition from ad-hoc exploration to systematic characterization. This message is the pivot point where the session stops debugging individual configurations and starts building a repeatable measurement apparatus.

The Context: A Failed First Attempt

To understand why this message was written, one must examine the events immediately preceding it. The user had requested ([msg 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 was a straightforward request for a benchmark sweep across multiple configurations. The assistant's first attempt at fulfilling it was, by any measure, a failure. Starting at [msg 3278], the assistant launched the daemon with pw=1 gw=1 (one partition worker, one GPU worker), then attempted to monitor RSS by spawning a background shell loop that read /proc/$PID/status every two seconds. The RSS monitor was started as a background subshell with output redirected to a log file. Then the benchmark was launched with cuzk-bench batch.

The results were messy. The RSS monitor log was completely empty ([msg 3285]): wc -l /tmp/cuzk-rss-pw1-gw1.log returned 0 lines. The pgrep -f pattern used to identify the daemon PID was too broad, returning multiple PIDs and causing a grep error ([msg 3286]). The benchmark itself only completed 4 of 5 proofs before the session timeout killed it ([msg 3288]). The assistant had to manually recover the peak RSS by querying VmHWM from /proc after the fact, and even that required fixing the PID grep ([msg 3287]).

The root cause of the RSS monitor failure is instructive. The background subshell used while kill -0 $DAEMON_PID 2>/dev/null; do ... done to poll RSS. When the session timed out and killed the benchmark process, the entire subshell tree was likely terminated before it could flush its output buffer to the log file. The while loop's output was redirected to a file, but the shell process that owned that redirection was killed, leaving the file empty. This is a classic shell scripting pitfall: background processes spawned within a session are not guaranteed to survive session termination, and buffered output may be lost.

The assistant's response to this failure is the subject message. Rather than fixing each individual problem—the PID matching, the RSS monitor fragility, the manual daemon lifecycle—the assistant made a strategic decision to step back and build a comprehensive solution.## The Reasoning: From Ad-Hoc to Systematic

The message reveals a clear reasoning process: "Let me write a proper benchmark script that handles everything." The word "proper" is the key. It signals the assistant's recognition that the previous approach—manually typing bash commands, starting background monitors, hoping they survive—was inadequate for the task at hand. The assistant was not just writing a script; it was designing a methodology.

The requirements the assistant enumerated are telling: "start daemon, monitor RSS, run bench, collect results, then kill daemon." This is a complete lifecycle. The script would:

Start the daemon with a given config file, ensuring clean state
Monitor RSS throughout the benchmark run, using a robust mechanism
Run the benchmark with controlled parameters
Collect results including both timing data and memory measurements
Kill the daemon to clean up for the next configuration This lifecycle automation was essential because the benchmark sweep would involve at least nine configurations (pw=1/2/5/7/10/12 × gw=1/2, as later revealed in the chunk summary). Running each manually would have been error-prone and time-consuming. More importantly, manual operation introduced variability: different shell environments, leftover daemon processes from previous runs, stale memory mappings, and inconsistent measurement intervals could all corrupt the results. The assistant was also responding to a deeper problem: the session's tool model. In the opencode architecture, each round of tool calls is synchronous—the assistant issues tools, waits for all results, then produces the next message. This means a background RSS monitor started in one round might not survive to the next round if the session is interrupted or if the tool execution environment reaps child processes. By encapsulating the entire benchmark lifecycle in a single script invoked as a single tool call (a bash command), the assistant ensured that all components—daemon, monitor, benchmark, cleanup—ran within the same process tree and would be properly sequenced.

Assumptions and Input Knowledge

The message makes several implicit assumptions that are worth examining.

Assumption 1: A shell script is the right abstraction. The assistant assumes that a bash script can reliably orchestrate a multi-process workflow involving a long-running daemon, a concurrent benchmark client, and a periodic monitoring loop. This is a reasonable assumption for Linux systems, but it carries risks: shell scripts have poor error handling, race conditions in PID management, and no built-in mechanism for detecting hung processes. The assistant's earlier failure with the background subshell demonstrated exactly these risks.

Assumption 2: The daemon can be cleanly started and stopped between configurations. The script would need to kill any existing daemon, wait for port release, start a new one with a different config, wait for it to be ready, run the benchmark, then kill it. This assumes clean shutdown and startup semantics, which may not hold if the daemon leaves GPU state or pinned memory allocated.

Assumption 3: RSS monitoring via /proc is sufficient. The assistant planned to use VmRSS from /proc/$PID/status to track memory usage. This gives resident set size, which includes shared memory pages (like the SRS file mapping) and may overcount or undercount depending on page sharing. For a system with a 44 GiB SRS file mapped into memory, RSS includes the daemon's share of that mapping, which may be misleading when comparing configurations that share the same SRS.

Input knowledge required to understand this message includes: familiarity with the cuzk proving engine's architecture (partition workers, GPU workers, the split proving API from Phase 12), understanding of the Linux /proc filesystem for memory monitoring, knowledge of the cuzk-daemon and cuzk-bench binaries and their command-line interfaces, and awareness of the benchmark sweep context (the user wanted to test pw=1/2/5/7 with lower queue/GPU worker counts).

The Output Knowledge Created

This message produced a single artifact: the file /tmp/cuzk-lowmem-bench.sh. While we cannot see its contents directly in this message, the chunk summary reveals what the benchmark campaign ultimately discovered:

A clean linear memory scaling formula: ~69 GiB baseline + pw × ~20 GiB
That gw=2 provides no throughput benefit below pw=10 due to synthesis starvation
Concrete deployment guidance for 128 GiB through 768 GiB systems The script was the instrument that produced this knowledge. It enabled the systematic sweep across nine configurations that would have been impractical to run manually. More importantly, it created repeatability: the same script could be run days later with the same results, and the methodology was transparent enough to be reviewed and improved.

Mistakes and Incorrect Assumptions

The most significant mistake in this message is the assumption that a single shell script would solve the reliability problems that plagued the manual approach. The assistant's earlier failure was not fundamentally a scripting problem—it was a session lifecycle problem. Background processes spawned by one tool call could be killed when the session moved on. A shell script invoked as a single tool call would indeed keep all subprocesses alive within that call, but if the benchmark took longer than the session timeout (as the pw=1 run did at ~300 seconds per proof), the entire script would be killed mid-execution.

The assistant also did not address the PID matching issue that caused the earlier failure. The pgrep -f pattern matching problem—where a broad pattern returned multiple PIDs—would still exist inside the script unless the assistant specifically used a more precise matching strategy (e.g., matching on the config file path or using a PID file).

Additionally, the assistant assumed that "monitor RSS" was sufficient for memory characterization. In reality, the benchmark sweep later needed VmHWM (peak resident set size, or "high-water mark") rather than instantaneous VmRSS, because instantaneous RSS varies throughout a proof's lifecycle. The assistant's earlier recovery used VmHWM from /proc/$PID/status, which gives the kernel-tracked peak since process start. A proper benchmark script should capture VmHWM at the end of each run, not just periodic VmRSS snapshots.

The Thinking Process Visible in the Message

The message itself is brief, but its reasoning is visible through the surrounding context. The assistant had just spent several messages ([msg 3283] through [msg 3291]) diagnosing the failed pw=1 run. It discovered the empty RSS log, fixed the PID grep, extracted the peak RSS from VmHWM, and observed that pw=1 was too slow for practical use (~290s wall time per proof). The thought process was: "This manual approach is fragile and slow. I need to automate the entire lifecycle so I can run the remaining configurations (pw=2, 5, 7, plus gw=2 variants) without repeating these failures."

The decision to write a script rather than fix each individual problem is a classic engineering tradeoff: invest time in automation now to save time later. The assistant correctly judged that the benchmark sweep would involve enough repetitions to justify the upfront investment. This is the same reasoning that drives test automation, CI/CD pipelines, and infrastructure-as-code in production systems.

Conclusion

Message [msg 3292] is a small but significant moment in a larger engineering narrative. It represents the transition from exploration to measurement, from ad-hoc debugging to systematic characterization. The assistant recognized that the quality of the benchmark results would be limited by the quality of the measurement apparatus, and that building a proper script was a prerequisite for producing trustworthy data. The resulting benchmark campaign—documented in the chunk summary as a nine-configuration sweep—produced the memory scaling formula and deployment guidance that would inform production configuration decisions for the cuzk engine. In this sense, the shell script was not just a tool; it was the methodological foundation for everything that followed.