The Quiet Instrument: How a Single RSS Monitor Command Anchored a Performance Investigation

In the middle of a complex optimization campaign for the cuzk SNARK proving engine, a seemingly trivial bash command appears:

nohup bash -c 'while true; do ps -o rss= -p $(pgrep -f cuzk-daemon | head -1) 2>/dev/null | awk "{printf \"%s RSS: %.1f GiB\n\", strftime(\"%H:%M:%S\"), \$1/1048576}"; sleep 5; done' > /tmp/rss-nodebug-pw10.log 2>&1 & echo "RSS=$!"
RSS=859673

This is message [msg 3197]. On its surface, it is nothing more than a background process that polls the Resident Set Size (RSS) of the cuzk-daemon every five seconds and logs it to a file. But to understand why this command was issued at this precise moment requires unpacking the entire trajectory of the Phase 12 optimization effort, the memory backpressure fixes that preceded it, and the performance mystery that had just emerged.

The Context: Phase 12 and the Memory Backpressure Breakthrough

The cuzk project is a CUDA-accelerated Groth16 proof generation engine for Filecoin's Proof-of-Replication (PoRep). Phase 12 had introduced a "split GPU proving API" that decoupled the GPU worker's critical path from CPU post-processing, allowing the GPU to begin proving the next partition while the CPU finalized the previous one. This architectural change improved throughput but introduced a severe memory pressure problem: synthesized partitions could pile up in memory when synthesis outpaced GPU consumption, leading to out-of-memory (OOM) conditions.

In the messages immediately preceding [msg 3197], the agent had implemented three critical fixes to address this memory pressure:

  1. Early a/b/c free: Clearing approximately 12 GiB per partition of evaluation vectors immediately after prove_start returned, since the GPU no longer needed them.
  2. Channel capacity auto-scaling: Sizing the synthesis-to-GPU channel to max(synthesis_lookahead, partition_workers) instead of the hardcoded value of 1, preventing completed syntheses from blocking on send() while holding large allocations.
  3. Partition permit held through send: Releasing the semaphore permit only after the channel send succeeded (not just after synthesis), bounding total in-flight outputs to partition_workers without adding latency since the channel had room for all of them. The results were dramatic. With partition_workers=12 (pw=12), the daemon now completed proofs at 38.4 seconds per proof with a peak RSS of 383.8 GiB. Previously, the same configuration had OOM'd at 668 GiB. The memory backpressure design — using channel capacity as a natural throttle rather than coarse semaphore gating — had eliminated the OOM condition while preserving the throughput gains from the split API.

The Mystery: A 1.8-Second Regression

But a nagging question remained. The Phase 12 baseline benchmark (before the memory backpressure fixes) had achieved 37.1 seconds per proof. The new code, with all the memory fixes in place, was delivering approximately 38.8–38.9 seconds per proof — a regression of about 1.7–1.8 seconds. The agent needed to understand whether this regression was inherent to the memory backpressure changes or whether it was an artifact of the instrumentation added alongside those changes.

The instrumentation in question was the log_buffers() function in pipeline.rs, which used eprintln! to emit buffer flight counts at every synthesis start, synthesis completion, prove start, and prove finalization event. For a 15-proof benchmark with 10 partitions each, this meant approximately 600+ synchronous stderr writes. The agent hypothesized that these synchronous writes could be causing contention on the Tokio async runtime, stealing cycles from the synthesis and GPU dispatch tasks.

This hypothesis led to a quick refactoring: converting the eprintln! calls in log_buffers() to tracing::debug! calls, which are async-friendly and can be disabled at the log level. A similar eprintln! in the bellperson library's finish_pending_proof function was also converted. The daemon was rebuilt and restarted, and message [msg 3196] confirmed it was ready.

The Instrument: Why This RSS Monitor Matters

Message [msg 3197] is the RSS monitor startup for this "nodebug" benchmark run. The agent launches a nohup'd background shell loop that polls ps -o rss= every five seconds and writes timestamped RSS readings to /tmp/rss-nodebug-pw10.log. The echo "RSS=$!" at the end confirms the background process PID.

This is not the first RSS monitor the agent has launched in this session — similar monitors were started for the pw=10 semaphore+channel fix benchmark ([msg 3172]) and the pw=12 benchmark ([msg 3185]). Each monitor writes to a distinct log file named after the configuration being tested (rss-semchan-pw10.log, rss-semchan-pw12.log, rss-nodebug-pw10.log), creating a systematic record of memory behavior across optimization iterations.

The RSS monitor serves a dual purpose. First, it validates that the memory backpressure fixes are holding — that peak RSS stays within the 755 GiB budget and does not approach OOM territory. Second, it provides a memory profile over time, showing how quickly memory accumulates and whether it plateaus or grows unboundedly. The sort -n | tail -3 analysis pattern used in previous messages ([msg 3174], [msg 3187]) extracts peak RSS from these logs.

Assumptions and the Thinking Process

The agent's reasoning at this point reveals several assumptions. The primary assumption is that synchronous eprintln! writes to stderr could cause measurable throughput degradation in a highly concurrent async system. This is a plausible hypothesis — Tokio's blocking I/O avoidance guidelines warn against performing synchronous I/O in async tasks, and eprintln! acquires a lock on stderr for each write. However, the agent is about to discover that this assumption is incorrect.

A secondary assumption is that the 37.1 seconds per proof baseline is the "true" performance of the Phase 12 code and that any deviation from it represents a regression worth investigating. This assumption will be challenged when the nodebug benchmark returns 38.8 seconds per proof — essentially identical to the previous runs with the eprintln! instrumentation still in place. The agent will then pivot to investigating whether the early a/b/c free deallocations (120 GiB of munmap calls per proof) are the actual source of the overhead ([msg 3200]).

The agent also assumes that the RSS monitor itself does not perturb the benchmark results. A bash loop calling ps every five seconds is lightweight, but it does consume a small amount of CPU and memory. In practice, this overhead is negligible compared to the 200+ GiB memory footprint and 38+ second proof times, but it is an assumption worth noting.

What This Message Creates

Message [msg 3197] creates the measurement infrastructure for the nodebug benchmark. The RSS log file /tmp/rss-nodebug-pw10.log will be consumed in subsequent messages to determine peak memory usage. The background process PID (859673) allows the agent to kill the monitor after the benchmark completes. The naming convention rss-nodebug-pw10.log ties this measurement to the specific configuration being tested, enabling later comparison with other configurations.

More broadly, this message represents a methodological commitment: every optimization iteration must be measured, and memory is as important as throughput. The systematic RSS monitoring across configurations — from the original Phase 12 code through the semaphore+channel fix to the nodebug variant — creates a dataset that allows the agent to correlate memory usage with throughput and to validate that memory reductions do not come at the cost of performance regressions.

The Outcome That Follows

The benchmark launched in message [msg 3198] will return 38.8 seconds per proof with 317 GiB peak RSS ([msg 3199]), essentially identical to the previous runs. This disproves the eprintln! hypothesis and forces the agent to look deeper. The investigation that follows in [msg 3200] examines GPU timing distributions and questions whether the early a/b/c free deallocations — 120 GiB of munmap calls per proof — are the actual source of the 1.8-second gap. The agent will ultimately discover that the 37.1 second baseline was an outlier, and the consistent performance of the memory backpressure code is approximately 38.8 seconds per proof.

In the end, message [msg 3197] is a testament to the discipline of measurement-driven optimization. A single RSS monitor command, launched at the right moment, provides the data needed to separate signal from noise. Without it, the agent might have chased the eprintln! hypothesis into a dead end or, worse, accepted a regression that didn't exist. The quiet background process, polling ps every five seconds, is the unsung hero of this investigation.