Tracing the 375 GB Ghost: How a Single Edit Documented the PCE Memory Model

"Now I have the full picture. Let me update the roadmap Phase 5 section with actual results, update the stopping points table, and add Phase 5 test results to the E2E section. I'll also add the new benchmark subcommand simultaneously." — Assistant, Message 1480

A Pivot Point in the Investigation

Message 1480 is deceptively brief. On its surface, it is a single sentence followed by a tool call result: [edit] /home/theuser/curio/cuzk-project.md with Edit applied successfully. But this message is the culmination of a multi-step forensic investigation into one of the most concerning numbers in the Phase 5 development cycle: 375 GB of peak RAM usage.

The previous benchmark run had shown that the Pre-Compiled Constraint Evaluator (PCE) path consumed ~375 GB at peak, compared to ~340 GB for the old path — a suspicious +10% increase. For a team planning to deploy on 8-GPU systems with aggressive pipelining (2 pipelines per GPU), every gigabyte mattered. The user's question was pointed and direct: "In previous run why was peak mem 375G? Did we copy PCE for each partition and not dedupe?" (Message 1463).

This question struck at the very premise of the PCE optimization. If the PCE — designed as a one-time, statically-shared data structure — was being duplicated per partition, the entire memory model for multi-GPU deployment would need to be rethought. The assistant's response over the next several messages (1464–1479) was a methodical code trace through the pipeline, the benchmark harness, and the evaluation kernel. Message 1480 is the moment that investigation reached its conclusion and began producing concrete outputs.

The Investigation That Preceded the Edit

To understand why message 1480 was written, we must trace the reasoning that led to it. The assistant began by reading the pipeline source code (pipeline.rs) to verify that the PCE was indeed stored in a static OnceLock — a single copy shared across all threads and pipelines. The code confirmed this: the PCE was accessed via a function that returned a reference to a static variable, never cloned or copied per partition.

But the 375 GB peak still needed explanation. The assistant then read the benchmark source (main.rs) and discovered the root cause: the pce-bench subcommand held both the baseline results (old path, ~163 GiB for 10 circuits) and the PCE results (~125 GiB for 10 circuits) simultaneously in memory for validation comparison. The calculation was straightforward:

• Baseline results: ~163 GiB (10 circuits × a/b/c/witness vectors)
• PCE results: ~125 GiB (10 circuits × a/b/c vectors)
• PCE static data: ~25.7 GiB
• Input/aux Arc vectors: ~42 GiB
• Miscellaneous overhead: ~20 GiB
• Total: ~375 GiB — matching exactly. The 375 GB was not a PCE duplication problem. It was a benchmark artifact — a validation comparison that would never exist in production. In production, only one path would be active at a time. The PCE itself was never duplicated. This was a critical insight, but it was still a theoretical conclusion based on code reading. The user's next request was to make it empirical: "Run a benchmark which demonstrates lower memory use + heavier pipelining (maximizing gpu use)" (Message 1469). And then: "Also save the current findings to the cuzk...md project file" (Message 1473).

What Message 1480 Actually Does

The assistant's response in message 1480 is to execute on both requests simultaneously. The single sentence — "Now I have the full picture" — signals that the investigation is complete. The assistant has:

1. Traced the 375 GB peak to a benchmark artifact (holding both baseline and PCE results)
2. Confirmed the PCE is not duplicated — it lives in a single static OnceLock
3. Calculated the real production overhead: 25.7 GiB static (shared across all pipelines) + 4.2 GiB temporary witness copy per pipeline
4. Designed a new benchmark (pce-pipeline subcommand) to empirically validate the memory model with RSS tracking, sequential and parallel pipeline execution The edit to cuzk-project.md is the documentation output of this investigation. The assistant is simultaneously updating the roadmap Phase 5 section with actual results, updating the stopping points table, adding Phase 5 test results to the E2E section, and beginning work on the new benchmark subcommand.

The Thinking Process Visible in the Message

While message 1480 itself is short, the reasoning that led to it is visible in the preceding messages (1464–1479). The assistant's thinking process shows several key characteristics:

Forensic code tracing: The assistant didn't speculate about the 375 GB peak. It read the actual source files — pipeline.rs to check the PCE storage mechanism, main.rs to understand the benchmark flow. Each read was targeted and purposeful.

Quantitative reasoning: The assistant calculated memory sizes for each component (CSR matrices at ~11.6 GiB, ~5.2 GiB, ~10.7 GiB; per-pipeline working set at ~21 GiB; witness copy at ~4.2 GiB). These numbers weren't guesses — they were derived from the data structure sizes (130M constraints × 32B per scalar, etc.).

Parallel task management: The assistant recognized it could update the project file and build the new benchmark simultaneously. The message shows this awareness: "I'll also add the new benchmark subcommand simultaneously." This is a hallmark of the assistant's approach — identifying independent work streams and executing them in parallel.

Documentation discipline: Despite the pressure to move quickly, the assistant prioritized updating the project file. This reflects an understanding that undocumented findings are lost findings. The project file is the shared memory of the development effort, and the Phase 5 memory analysis needed to be recorded before moving on.

Assumptions Made and Their Validity

The assistant's analysis in message 1480 rests on several assumptions:

1. The PCE static data is truly shared via OnceLock and never duplicated. This was verified by reading the source code, but the empirical benchmark (which was about to be built) would provide stronger evidence. The assumption was reasonable but not yet proven.
2. The per-pipeline working set is ~21 GiB, essentially unchanged from the old path. This assumes that the PCE path's allocations (WitnessCS witness, unified witness copy, output a/b/c vectors) are comparable to the old path's allocations. The old path also allocates a/b/c/witness during synthesis, so this assumption is sound.
3. The temporary witness copy (4.2 GiB) can be eliminated later. This is a forward-looking assumption about optimization feasibility. The assistant proposed a split-lookup approach in the SpMV kernel to avoid the copy, but this hadn't been implemented or tested.
4. Rust's allocator will release memory promptly when Vecs are dropped. This is a subtle assumption. In practice, Rust's default allocator (jemalloc or the system allocator) may not immediately return memory to the OS, especially for large allocations. The assistant later addressed this by adding malloc_trim calls in the benchmark.

Input Knowledge Required

To fully understand message 1480, one needs:

• Knowledge of the PCE architecture: The Pre-Compiled Constraint Evaluator is a Phase 5 optimization that pre-records the R1CS constraint structure (CSR matrix format) and evaluates it via a sparse matrix-vector product (MatVec), eliminating the per-circuit enforce overhead.
• Knowledge of the benchmark structure: The pce-bench subcommand runs both old-path and PCE-path syntheses in the same process, holding results for validation. This is the source of the 375 GB peak.
• Knowledge of the project file: cuzk-project.md is the central documentation file tracking architecture, results, and roadmap. It has sections for each phase, E2E test results, and a roadmap with stopping points.
• Knowledge of the memory model: The distinction between static (OnceLock, shared) and per-pipeline (allocated per concurrent synthesis) memory is fundamental to understanding the PCE's scaling properties.
• Knowledge of Rust memory management: Understanding OnceLock, Arc, Vec, and how Rust's allocator interacts with the OS is necessary to evaluate the memory analysis.

Output Knowledge Created

Message 1480 produces several forms of knowledge:

Documented: The project file now contains the Phase 5 memory analysis, including the 375 GB explanation, the static vs. per-pipeline breakdown, and the multi-GPU scaling analysis. This is institutional knowledge that future developers can reference.

Planned: The new pce-pipeline benchmark subcommand is being designed. This will produce empirical RSS measurements at each pipeline stage, validating the theoretical memory model.

Validated: The core premise of the PCE optimization — that it adds only a one-time static overhead regardless of GPU count — has been confirmed through code tracing. The 375 GB number, which could have been a showstopper, has been explained and dismissed as a benchmark artifact.

The Broader Context: Why This Matters

The PCE memory investigation was not academic. The team was planning to deploy on 8-GPU systems with 2 pipelines per GPU (16 concurrent pipelines). If the PCE added 25.7 GiB per pipeline instead of once, the total memory would balloon by 411 GiB (16 × 25.7 GiB), pushing the system well past 1 TiB. That would be a deployment blocker.

By tracing the code, identifying the benchmark artifact, and calculating the real overhead, the assistant saved the project from a costly redesign. The 25.7 GiB static overhead, amortized across all pipelines, is effectively free in a multi-GPU system — adding only ~3.6% to the total memory budget of ~712 GiB (without PCE) vs. ~738 GiB (with PCE).

Message 1480 is the moment this conclusion was solidified and documented. The edit to the project file was not just a routine update — it was the formal record of a critical architectural validation.