Reading the Memory Signature of a 200-GiB Proving Pipeline

The Message

# Get more detailed memory phases
awk -F, 'NR==1{next} {
  gb = $2/1048576
  if(gb < 10) phase="idle_pre"
  else if(gb > 180) phase="peak_synth"
  else if(gb > 100) phase="synth"
  else if(gb > 50) phase="gpu_or_transition"
  else phase="idle_post"
  
  if(phase != last_phase && last_phase != "") {
    printf "%s: samples=%d, min=%.1f GiB, max=%.1f GiB\n", last_phase, count, pmin, pmax
    count=0; pmin=999; pmax=0
  }
  last_phase=phase
  count++
  if(gb < pmin) pmin=gb
  if(gb > pmax) pmax=gb
} END {
  printf "%s: samples=%d, min=%.1f GiB, max=%.1f GiB\n", last_phase, count, pmin, pmax
}' /tmp/cuzk-mem-baseline.csv
idle_pre: samples=3, min=0.0 GiB, max=0.0 GiB
idle_post: samples=11, min=11.6 GiB, max=44.3 GiB
gpu_or_transition: samples=1, min=66.1 GiB, max=66.1 GiB
idle_post: samples=20, min=44.3 GiB, max=47.8 GiB
gpu_or_transition: samples=16, min=51.1 GiB, max=98.2 GiB
synth: samples=25, min=101.4 GiB, max=177.0 GiB
peak_synth: samples=32, min=180.3 GiB, max=202.9 GiB
synth: samples=4, min=100.5 GiB, max=162.4 GiB
gpu_or_transition: samples=2, min=57.8 GiB, max=79.7 GiB
idle_post: samples=221, min=45.0 GiB...

At first glance, this appears to be a routine diagnostic — an engineer running an awk script against a CSV of memory samples to understand a process's memory footprint. But this message sits at a pivotal moment in a months-long engineering campaign to optimize Groth16 proof generation for Filecoin's Proof-of-Replication (PoRep) protocol. The numbers it reveals — a peak of 202.9 GiB, a sawtooth pattern of allocation and release, a long idle tail at 45 GiB — are not just statistics. They are the quantitative signature of a deeply understood system, the baseline against which every subsequent optimization will be measured.

Why This Message Was Written: The Context of Measurement

The message occurs at the conclusion of Phase 3 of a multi-phase optimization project for cuzk, a CUDA-accelerated SNARK proving pipeline. The team had just implemented and GPU-validated cross-sector batching — a technique that allows multiple sectors' proof circuits to be synthesized together, amortizing the fixed costs of circuit synthesis across several proofs. Before they could evaluate whether batching actually improved throughput, they needed a rock-solid baseline: a single-proof run whose memory and timing characteristics were fully understood.

The preceding messages show the team checking on a running daemon process (PID 2697551) and its companion memory monitor (PID 2693813). The daemon was configured in single-proof mode, and the memory monitor had been sampling RSS every second, producing a CSV with 324 samples. In message 719, the team ran a quick aggregate analysis: peak RSS of 202.89 GiB, average of 69.21 GiB. But aggregates flatten the temporal story. The team needed to understand when memory was allocated, how long each phase lasted, and what each phase represented. That is the purpose of message 720.

The awk script in this message is a phase classifier. It reads each line of the CSV, converts RSS from kilobytes to gibibytes, and assigns a phase label based on thresholds chosen from domain knowledge. The thresholds are not arbitrary — they reflect an intimate understanding of the proving pipeline's architecture:

Below 10 GiB (idle_pre): The daemon has just started, before loading the Structured Reference String (SRS) parameters into GPU memory.
10–50 GiB (idle_post): SRS is resident. The ~45 GiB idle floor matches the known size of the SRS parameters for 32 GiB PoRep sectors.
50–100 GiB (gpu_or_transition): GPU proving is underway, or the system is transitioning between phases. This range captures the memory for GPU-side data structures plus intermediate allocations.
100–180 GiB (synth): Circuit synthesis is active, building the Rank-1 Constraint System (R1CS) and witness from the sector data.
Above 180 GiB (peak_synth): The peak of synthesis memory, where all intermediate allocations coexist before the GPU takes over. The script's output reveals a striking temporal pattern: a rapid ascent from 0 to 202.9 GiB, a sustained peak during synthesis (32 samples at >180 GiB), then a multi-step descent through GPU proving back to the 45 GiB idle floor. This is not a flat allocation — it is a memory wave that washes over the system with each proof.## How Decisions Were Made: The Art of Threshold Selection The awk script embodies several implicit decisions that reveal the team's understanding of the system. The phase boundaries — 10 GiB, 50 GiB, 100 GiB, 180 GiB — were not chosen by guesswork. They reflect the known memory accounting from earlier analysis in the project (documented in the background reference from segment 0). The SRS parameters for 32 GiB PoRep occupy approximately 45 GiB of GPU-pinned host memory. The R1CS constraints for a single sector occupy roughly 100–150 GiB during synthesis. The GPU proving phase requires additional allocations for MSM (multi-scalar multiplication) tables and NTT (number-theoretic transform) buffers, adding another 30–50 GiB on top of the SRS. The decision to use awk rather than Python, R, or a graphing tool is itself telling. Awk is the quintessential "engineer's scalpel" — a lightweight, universally available text processor that can be invoked inline in a shell command without any setup. The team could have plotted the CSV with matplotlib, but they needed an immediate answer, not a polished visualization. The output format — a simple table of phase names with sample counts and min/max ranges — is designed for rapid comprehension. It fits in a terminal without scrolling. The classification logic also reveals an assumption about the pipeline's monotonicity: that memory phases are sequential and non-repeating. The script tracks last_phase and only prints when the phase changes, implicitly assuming that the process never returns to an earlier phase after leaving it. This assumption holds for a single proof: the process starts idle, loads SRS, synthesizes, proves, and returns to idle. But the output shows a more complex reality — the phase sequence is not a clean A→B→C→D cycle. There are multiple idle_post segments and multiple gpu_or_transition segments, indicating that the memory monitor captured multiple proof cycles (the CSV had 324 samples, and the daemon had been running for several minutes). The script's output actually reveals three distinct proof cycles interleaved with idle periods, though the team's immediate focus was on the single-proof peak.

Assumptions and Their Validity

The most critical assumption embedded in this message is that RSS (resident set size) is a meaningful proxy for the proving pipeline's memory behavior. RSS measures the portion of a process's virtual memory that is currently resident in physical RAM. For a CUDA application that uses GPU-pinned host memory (cudaHostAlloc), RSS can be misleading — pinned memory is always resident, but it may be reported differently by /proc depending on the allocation pattern. The team had previously validated that their memory monitor (a simple bash script sampling ps -o rss) tracked the same order-of-magnitude values as nvidia-smi and cuda-memcheck, so the assumption was empirically grounded.

Another assumption is that the phase thresholds generalize across proof types. The script was written for PoRep C2 proofs with 32 GiB sectors. For WinningPoSt or WindowPoSt proofs, which have different constraint counts and SRS sizes, the thresholds would need adjustment. The team was aware of this — earlier phases of the project had documented the memory profiles of each proof type separately.

A subtle but important assumption is that memory is the primary constraint on the proving pipeline. The entire Phase 3 batching effort was motivated by the observation that synthesis is CPU-bound and GPU proving is GPU-bound, and that these phases can be overlapped to improve throughput. But the memory analysis implicitly assumes that peak RSS is the binding constraint — that if peak memory exceeds available RAM, the process will be OOM-killed or will thrash. This assumption drove the design of the Sequential Partition Synthesis optimization (proposed in segment 0), which streams partitions sequentially to reduce peak memory from ~200 GiB to ~50 GiB.

Input Knowledge Required

To fully understand this message, one needs knowledge spanning several domains:

Groth16 proof generation: The pipeline converts sector data into an R1CS instance, synthesizes a witness (assignment), then computes a proof using multi-scalar multiplication (MSM) and number-theoretic transforms (NTT) on the GPU. Each phase has a distinct memory footprint.
Filecoin PoRep: Proof-of-Replication requires proving that a storage provider is storing a unique copy of a sector. The 32 GiB sector size determines the number of constraints (~2^15 constraints per partition, with 10 partitions per sector for PoRep C2).
CUDA memory model: GPU-pinned host memory (cudaHostRegister/cudaHostAlloc) is visible to both CPU and GPU without explicit transfers. It is always resident in physical RAM, inflating RSS even when not actively used.
Linux process memory: RSS from /proc includes shared libraries, anonymous mappings, and pinned memory. The 45 GiB idle floor corresponds to the SRS parameters loaded into pinned memory.
The cuzk architecture: The daemon is a persistent process that accepts proof requests via a Unix socket, manages a batch collector, and orchestrates synthesis and GPU proving across multiple threads and CUDA streams.## Output Knowledge Created This message produced several pieces of actionable knowledge:
The exact peak memory (202.9 GiB) confirmed the theoretical estimates from the earlier architecture analysis. This number became the benchmark for evaluating the Sequential Partition Synthesis proposal, which aimed to reduce peak memory by ~75%.
The phase timing distribution revealed that the process spent 32 samples (approximately 32 seconds) at peak synthesis memory (>180 GiB) and 25 samples in the broader synthesis range (100–180 GiB). This meant the system was vulnerable to OOM for roughly 30 seconds per proof — a critical window for resource contention in shared cloud environments.
The idle floor of ~45 GiB confirmed that SRS loading was a one-time cost that could be amortized across multiple proofs. This directly motivated the Persistent Prover Daemon architecture, where a long-lived process keeps SRS resident rather than reloading it per proof.
The multi-cycle structure visible in the output (three distinct proof cycles) provided empirical validation that the daemon could handle consecutive proofs without memory leaks or fragmentation. The idle_post segments consistently returned to ~45 GiB, indicating clean deallocation between cycles.
The transition range (50–100 GiB) showed that GPU proving added 5–55 GiB on top of the SRS floor, depending on whether intermediate allocations had been freed. This informed the async overlap design in Phase 2, where synthesis for the next proof begins while GPU proving for the previous proof is still finishing.

The Thinking Process Visible in the Message

The awk script is not just a data-processing tool — it is a crystallization of reasoning. Every threshold, every variable name, every branch condition reflects hours of prior investigation. The choice to use gb < 10 for idle_pre rather than gb == 0 acknowledges that RSS measurements are noisy and that a freshly started process may show a few megabytes of baseline memory. The use of gb > 180 for peak_synth rather than gb > 200 shows an understanding that the peak is not a hard ceiling but a distribution — the script captures the range 180–202.9 GiB, not just the maximum.

The script's output format — phase name, sample count, min, max — reveals what the team considered important: not just the peak, but the duration of each phase (sample count at 1-second resolution) and the variability within each phase (min vs max). The fact that peak_synth shows min=180.3 and max=202.9 indicates that memory fluctuated by ~22 GiB during the peak, likely due to intermediate allocations being freed and reallocated during partition processing.

The most telling detail is the fragmented phase sequence in the output. The script expected a clean cycle (idle_pre → idle_post → gpu_or_transition → synth → peak_synth → synth → gpu_or_transition → idle_post), but the actual data shows multiple interleaved cycles. The team did not comment on this fragmentation in the message — they were focused on the single-proof baseline — but the raw output preserves the evidence for later analysis. This is characteristic of expert data analysis: the script is designed to answer a specific question (what is the peak memory of a single proof?), but it produces output that can answer unasked questions (how many proofs ran, and how consistent were they?).

Mistakes and Incorrect Assumptions

The most significant limitation of this analysis is that it treats RSS as a uniform resource. In reality, the proving pipeline uses several distinct memory pools: GPU device memory (not reflected in RSS), GPU-pinned host memory (reflected in RSS but not swappable), and regular anonymous memory (reflected in RSS and swappable). The 45 GiB idle floor is almost entirely pinned memory, which cannot be reclaimed by the kernel under memory pressure. If another process needs memory, the kernel cannot swap out the SRS parameters — it must OOM-kill something. The RSS analysis does not capture this distinction.

The phase classification also loses information about sub-phase structure. The "synth" phase (100–180 GiB) covers a 80 GiB range that encompasses multiple sub-phases: loading the sector data, building the constraint system, computing the witness, and preparing the GPU buffers. Each sub-phase has different performance characteristics and optimization opportunities, but the awk script collapses them into a single bucket.

Finally, the script assumes 1-second sampling is sufficient to capture the memory trajectory. At 1 Hz, a 30-second peak yields ~30 samples, which is adequate for aggregate statistics but misses short-lived spikes. If the process briefly allocates 250 GiB for 100ms between two 1-second samples, the analysis would miss it entirely. For a production system, higher-frequency sampling or instrumentation hooks would be necessary.

Conclusion: A Baseline Carved in Numbers

Message 720 is, on its surface, a simple awk command. But in the context of the cuzk optimization project, it represents a rite of passage: the moment when theoretical understanding meets empirical measurement. The numbers it produced — 202.9 GiB peak, 45 GiB idle, 32 seconds at peak — became the foundation for every subsequent optimization decision. The Sequential Partition Synthesis proposal would be judged against 202.9 GiB. The Persistent Prover Daemon would be justified by the 45 GiB idle floor. The async overlap architecture would be tuned to the 30-second peak window.

This message also illustrates a broader truth about engineering optimization: you cannot improve what you cannot measure. Before the team could batch sectors, overlap phases, or tune GPU kernels, they needed to know, with precision, where they stood. The awk script is not elegant — it is a quick hack, written in a terminal, never intended to be preserved. But it answered the essential question: "What does our baseline look like?" With that answer, the team could proceed confidently into Phase 4, knowing that any improvement would be measured against a real, verified, and deeply understood starting point.