The Quietest Line in Performance Engineering: Measuring Peak Memory After a Revert

In the middle of an intense debugging session — where CUDA timing instrumentation has just revealed a 5.7-second penalty from cudaHostRegister, where a carefully planned optimization has been reverted, and where the total proof time has dropped from 101.3 seconds to 94.4 seconds — there is a moment that could easily be overlooked. The assistant types two bash commands:

kill $(pgrep -f cuzk-memmon) 2>/dev/null
sort -t, -k2 -n /tmp/cuzk-phase4-mem3.csv | tail -1

This is message [msg 985] in the conversation, and on its surface, it is almost invisible: a process kill and a CSV sort. But this message represents a critical discipline in performance engineering — the insistence on measuring everything that matters, even when the primary question appears to be answered. The assistant has just reverted the B1 optimization (memory pinning via cudaHostRegister) and confirmed that total proof time improved by ~7 seconds. The natural instinct would be to declare victory and move on to the next problem. Instead, the assistant pauses to check peak memory consumption.

Why This Message Was Written

The context is essential. The assistant is deep in Phase 4 of optimizing the cuzk project — a pipelined Groth16 proof generation engine for Filecoin's Proof-of-Replication (PoRep). Five optimizations were implemented in this wave: A1 (SmallVec for linear combination indexing), A2 (pre-sizing vectors), A4 (parallelizing B_G2 CPU MSMs), B1 (pinning a/b/c vectors with cudaHostRegister), and D4 (per-MSM window tuning). The first end-to-end test revealed a catastrophic regression: 106 seconds versus an 88.9-second baseline.

Through disciplined instrumentation — adding CUZK_TIMING printf's to the CUDA kernel code and fixing a buffering issue that initially swallowed the output — the assistant identified B1 as the primary culprit. The cudaHostRegister calls were pinning approximately 125 GiB of host memory, and the mlock-equivalent operation touched every page, costing 5.7 seconds instead of the estimated 150–300 milliseconds. B1 was reverted, and a fresh test produced 94.4 seconds.

But a performance optimization that reduces time at the cost of increased memory is often a net loss in production. The assistant needed to verify that reverting B1 did not silently increase memory pressure. After all, B1 was intended to improve DMA transfer performance; if removing it caused memory consumption to spike, the trade-off might be different. The question was: does the code without B1 still fit within the ~203 GiB envelope established during Phase 2/3 baselines?

How the Decision Was Made

The decision to check memory was not arbitrary. It followed a clear diagnostic workflow that the assistant had established across dozens of previous messages:

1. Measure the holistic impact of all Phase 4 changes together (106s — regression).
2. Add instrumentation to isolate phase-level costs (CUZK_TIMING printf's).
3. Revert the most harmful change (B1) based on data.
4. Re-measure the new total (94.4s).
5. Verify collateral effects — specifically memory, since the reverted optimization was memory-related. Step 5 is where message [msg 985] lives. The assistant had started a memory monitor (cuzk-memmon.sh) in the background before running the proof test. This monitor sampled /proc/[pid]/status every second and wrote timestamped RSS values to a CSV file. After the test completed, the assistant killed the monitor and extracted the peak value. The choice of sort -t, -k2 -n is deliberate. The CSV format is timestamp,RSS_bytes,RSS_GiB. By sorting numerically on the second field (RSS in bytes) and taking the last line (tail -1), the assistant gets the maximum resident set size across the entire test run. This is a robust way to find peak memory without needing a separate analysis script.

Assumptions Embedded in This Message

Every measurement carries assumptions, and this one is no exception. The assistant assumes that:

• The memory monitor was running throughout the test. The kill command uses pgrep -f cuzk-memmon, which matches any process whose command line contains "cuzk-memmon". If the monitor had crashed or failed to start, the kill would silently do nothing, and the CSV file might be empty or missing.
• The CSV file exists and is correctly formatted. The sort command will fail noisily if the file is missing, but the assistant doesn't check the exit code. In a production debugging session, this is a reasonable risk — the file was created by a script the assistant wrote and has used successfully before.
• Peak RSS is the right metric. For a proof-generation pipeline that allocates ~200 GiB of temporary vectors, peak memory is the critical constraint. If the system runs out of physical RAM, it will swap or OOM-kill the process. The assistant implicitly assumes that average memory or memory at specific phases is less important than the absolute maximum.
• The memory profile is stable across runs. The assistant runs only one test before checking memory. If there were run-to-run variability (e.g., due to NUMA effects or background system activity), a single sample might not represent typical behavior. None of these assumptions are unreasonable, and the assistant later confirms stability by running a second proof ([msg 987]) that produces nearly identical timing.

Input Knowledge Required

To understand this message, a reader needs to know several things that are established earlier in the conversation:

• The memory monitor infrastructure: A script at /tmp/cuzk-memmon.sh that samples RSS every N seconds and writes to a CSV. This was created in earlier segments to track the ~200 GiB memory footprint of the PoRep C2 prover.
• The CSV format: timestamp,RSS_bytes,RSS_GiB. The third column is a human-readable GiB value, but the assistant sorts on the raw byte count (column 2) for precision.
• The test configuration: The proof is a single 32 GiB PoRep C2 proof, using the pipelined engine with 10 partitions (circuits). The baseline peak RSS was 202.9 GiB.
• The B1 optimization and its revert: B1 added cudaHostRegister calls to pin a, b, c vectors (3 arrays × 4.17 GB each × 10 circuits ≈ 125 GB). The assistant reverted it in messages [msg 969]–[msg 973] after the instrumented test showed a 5.7s penalty.
• The overall Phase 4 context: Five optimizations were implemented, two have been reverted (A2 partially, B1 fully), and the remaining regression is in synthesis (60.3s vs 54.7s baseline), likely caused by A1 (SmallVec).

Output Knowledge Created

The result, visible in the next message ([msg 986]), is:

1771368917639,212983712,203.11
1771368918733,212983720,203.11
1771368919829,212983736,203.11

Peak RSS: 203.11 GiB. This is essentially identical to the baseline of 202.9 GiB. The B1 revert did not increase memory consumption. This is an important negative result — it confirms that the 5.5-second gap to baseline (94.4s vs 88.9s) is purely a synthesis-time issue, not a memory-related artifact.

This knowledge directly shapes the next steps. With memory verified as unchanged, the assistant can confidently focus on the synthesis regression. And indeed, in the following messages ([msg 989]–[msg 990]), the assistant pivots to investigating the A1 (SmallVec) optimization, building a synth-only microbenchmark to isolate the slowdown without GPU overhead.

The Thinking Process Visible in This Message

Although the message contains only two bash commands, the thinking behind it is rich. The assistant is operating in a disciplined debugging loop that resembles the scientific method:

1. Hypothesis: B1 reverted, all other Phase 4 changes kept → total time should improve, memory should be unchanged.
2. Experiment: Run a single proof with the modified code, with memory monitoring enabled.
3. Data collection: Kill the monitor, sort the CSV, extract peak RSS.
4. Analysis (next message): Peak RSS = 203.11 GiB ≈ baseline → hypothesis confirmed.
5. Next hypothesis: The remaining 5.5s regression is caused by A1 (SmallVec). The kill command before sort is a deliberate sequencing choice. The memory monitor is a background process that writes to the CSV file at 1-second intervals. If the assistant sorted the file while the monitor was still writing, it might get an incomplete view — the peak might occur in a sample that hasn't been written yet. By killing the monitor first, the assistant ensures the CSV is final. The 2>/dev/null on the kill command is another thoughtful detail. If no cuzk-memmon process exists (e.g., because it already exited or failed to start), pgrep returns a non-zero exit code and kill prints an error. Suppressing this error keeps the output clean for the next command.

Conclusion

Message [msg 985] is, in isolation, a trivial data retrieval operation. But within the arc of the Phase 4 debugging session, it represents a commitment to holistic measurement. The assistant does not assume that a timing improvement comes without cost. It verifies. This is the difference between optimization that works in a benchmark and optimization that works in production.

The peak memory result — 203.11 GiB, unchanged from baseline — is a quiet but important validation. It means the B1 revert is clean: no timing regression from the revert itself (GPU time is actually slightly better at 33.8s vs 34.0s baseline), and no memory regression. The remaining work is purely about understanding why a SmallVec optimization — which should reduce heap allocations — is making synthesis 10.5% slower on an AMD Zen4 Threadripper PRO 7995WX system. That investigation, which leads to building a synth-only microbenchmark and gathering perf stat hardware counters, is the direct consequence of the clean bill of health this memory check provides.

In performance engineering, the most important measurements are often the ones that tell you what is not wrong.