The Quiet Diagnostic: Why a Five-Second RSS Poll Reveals the Soul of GPU Proving Optimization

DAEMON_PID=$(pgrep -f "target/release/cuzk-daemon" | head -1)
(while kill -0 $DAEMON_PID 2>/dev/null; do echo "$(date +%T) RSS=$(ps -o rss= -p $DAEMON_PID | awk '{printf "%.1f", $1/1048576}') GiB"; sleep 5; done) > /tmp/rss-pw12-v3.log 2>&1 &
echo "Monitor PID=$DAEMON_PID"
Monitor PID=3960482

At first glance, this is a mundane shell incantation — a background loop that polls a process's RSS every five seconds and logs it to a file. A system administrator's reflex, nothing more. But in the context of the high-stakes optimization campaign documented across Phases 9 through 12 of the SUPRASEAL_C2 Groth16 proof generation pipeline, this single message ([msg 3070]) represents a critical juncture: the moment when a carefully reasoned hypothesis about memory management meets the unforgiving reality of a 755 GiB production server.

To understand why this message matters, we must trace the chain of reasoning that led to it.

The Memory Ceiling

The Phase 12 split GPU proving API had just been committed at commit 99c31c2c. Its headline achievement was a 2.4% throughput improvement (37.1s/proof vs 38.0s) achieved by decoupling the b_g2_msm CPU computation from the GPU worker's critical path. But Phase 12 had also revealed a hard memory ceiling: attempting to increase synthesis parallelism beyond pw=10 caused the daemon to be killed by the OOM killer, with RSS peaking at 668 GiB on a 755 GiB system. The configuration pw=12 (12 concurrent partition synthesis workers) was tantalizingly out of reach — if it could be made to work, it would almost certainly improve throughput further by keeping the GPU pipeline better fed.

The assistant's initial diagnosis, stated in [msg 3054], was that "the extra memory isn't a leak — RSS returns to 71 GiB after completion." The memory was legitimately in use, not leaked. But the question remained: why did two extra workers (from 10 to 12) consume 301 GiB of additional memory, far more than the ~26 GiB that two additional partitions' synthesis data should require?

The User's Prompt and the Investigation It Triggered

The user's question in [msg 3055] — "the pending proof handle has nothing that can be freed early?" — was the catalyst. It was a deceptively simple question that cut to the heart of the memory problem. The PendingProofHandle was the Rust-side struct that held all data needed until the proof finalization step completed. If it held data that was no longer needed after prove_start returned, that memory could be freed ~1.7s earlier per partition, reducing peak pressure.

The assistant's response was a model of systematic investigation. It read the C++ source code of the prep_msm_thread — the background thread that runs b_g2_msm while the GPU worker moves on — and traced every field access. The critical discovery, documented across messages [msg 3056] through [msg 3062], was that the prep_msm_thread reads inp_assignment_data, aux_assignment_data, and the density bitvectors, but it does not read prover.a, prover.b, or prover.c — the NTT evaluation vectors. Each of these vectors is a Vec<Scalar> with approximately 130 million elements of 32 bytes each: roughly 4 GiB per vector, 12 GiB per partition. The GPU kernel region that reads these vectors completes inside generate_groth16_proofs_start_c, before the function returns — confirmed by the fact that cudaHostUnregister is called on these buffers before the function exits.

This was the insight: 12 GiB per partition was being held hostage by the pending proof handle for no reason. The assistant immediately implemented an early deallocation in prove_start, clearing the a, b, and c vectors (along with r_s and s_s) before constructing the PendingProofHandle. The fix compiled cleanly in 18.9 seconds.

The Moment of Verification

With the fix compiled and the daemon started with pw=12 (message [msg 3068]), the assistant now faced the verification step. Would the early deallocation be sufficient to bring peak memory below the 755 GiB ceiling? Or was the memory problem deeper than a single 12 GiB-per-partition savings?

The subject message — the RSS monitoring script — is the answer to that question. It is the diagnostic instrument deployed to measure whether the hypothesis holds.

The script itself is carefully constructed. It uses pgrep -f "target/release/cuzk-daemon" to find the daemon PID, then spawns a background subshell that loops while kill -0 confirms the process is still alive. Every five seconds, it captures RSS via ps -o rss= and converts from kilobytes to gibibytes using awk. The output is redirected to /tmp/rss-pw12-v3.log — the "v3" suffix indicating this is the third attempt at pw=12, after the initial OOM and any intervening adjustments.

Assumptions Embedded in the Approach

This message makes several implicit assumptions. First, that RSS polling at five-second granularity is sufficient to capture the peak memory usage. Given that a single proof generation takes ~37 seconds, five-second sampling should catch the envelope. Second, that the daemon is properly configured and running — the sleep 30 in [msg 3069] before checking for the "ready" log line was a necessary precondition. Third, that the early deallocation fix is correct and doesn't introduce a use-after-free or data corruption — a nontrivial assumption given that the assistant had just fixed a genuine use-after-free bug in the same code path (the provers stack reference captured by prep_msm_thread).

There is also an implicit assumption that the memory pressure at pw=12 is primarily driven by the a/b/c vectors' lifetime, and that freeing them 1.7 seconds earlier per partition will reduce peak accumulation. This assumption turned out to be incorrect — as the chunk summary reveals, the real bottleneck was the interaction between the partition semaphore and the GPU channel capacity. The semaphore released immediately after synthesis, allowing tasks to pile up while blocking on the single-slot GPU channel, causing 28 synthesized partitions to queue simultaneously. The a/b/c early deallocation helped but was not the root cause.

What This Message Reveals About the Engineering Process

The RSS monitor is a textbook example of instrumentation-driven optimization. The assistant did not guess at memory usage patterns — it built a tool to measure them. This pattern recurs throughout the optimization campaign: in Phase 9, timing instrumentation was added to identify PCIe transfer bottlenecks; in Phase 10, a two-lock design was benchmarked and abandoned based on data; in Phase 11, waterfall timing analysis identified DDR5 memory bandwidth contention. Each phase was driven by measurement, not intuition.

The five-second polling interval is a deliberate choice. Too frequent would add noise and overhead; too infrequent would miss the peak. Five seconds, relative to a ~37-second proof time, provides approximately 7-8 samples per proof cycle — enough to reconstruct the memory envelope without perturbing the system.

The Broader Significance

This message sits at the intersection of two optimization philosophies. The first is the "big win" approach: the early deallocation of a/b/c vectors was a targeted fix based on precise code analysis, expected to save ~12 GiB per partition. The second is the "measure everything" approach: the RSS monitor would reveal whether that fix was sufficient, and if not, what the actual memory behavior looked like.

As it turned out, the early deallocation was necessary but not sufficient. The RSS trace would reveal that the provers counter peaked at 28 — meaning 28 synthesized partitions were queued with their full ~16 GiB datasets. The semaphore was releasing too early, allowing synthesis to outpace GPU consumption. The fix that ultimately worked was holding the semaphore permit until the job was delivered to the GPU channel, which reduced peak RSS from 668 GiB to 294.7 GiB — but at the cost of throughput regression (39.9s vs 37.1s). The final resolution was increasing the GPU channel capacity from 1 to partition_workers, allowing a natural buffer without blocking the semaphore.

None of this subsequent diagnosis would have been possible without the RSS monitor. The subject message is the seed from which the full memory diagnosis grew. It is the moment when the assistant stopped reasoning from code structure alone and started reasoning from system behavior — a shift from static analysis to dynamic measurement that characterizes the most effective optimization work.

Input and Output Knowledge

To understand this message, one must know: the Phase 12 split API architecture, the role of PendingProofHandle in holding synthesis data until finalization, the size of the a/b/c NTT evaluation vectors (~12 GiB per partition), the previous OOM failure at pw=12 (668 GiB peak), and the early deallocation fix just implemented. One must also understand the tooling conventions: pgrep for process discovery, kill -0 for existence checking, ps -o rss= for memory measurement, and the background subshell pattern for continuous monitoring.

The output knowledge created by this message is the RSS trace file at /tmp/rss-pw12-v3.log. This trace would become the primary evidence driving the next round of optimization — the discovery of the semaphore/channel mismatch, the implementation of the hold-permit fix, the measurement of its throughput regression, and the final channel capacity increase. A single five-second polling loop generated the data that unlocked the next phase of the optimization campaign.

Conclusion

The subject message is unremarkable in isolation — a shell one-liner that any engineer might type without a second thought. But in context, it represents the disciplined application of measurement to hypothesis, the willingness to let data override intuition, and the recognition that even the most carefully reasoned code change must be validated against real system behavior. The RSS monitor is not the hero of this story — the hero is the engineering mindset that deploys it as a matter of course, treating every optimization as an experiment and every fix as a hypothesis awaiting confirmation.