Tracing a 375 GB Phantom: How a Benchmark Artifact Nearly Derailed a Multi-GPU Memory Analysis

Introduction

In the high-stakes world of Filecoin proof generation, memory is the invisible adversary. The SUPRASEAL_C2 Groth16 proving pipeline, responsible for generating zero-knowledge proofs for Filecoin's Proof-of-Replication (PoRep), consumes approximately 200 GiB of RAM during a single proof run. When the Phase 5 Pre-Compiled Constraint Evaluator (PCE) was benchmarked and reported a peak memory usage of 375 GB, it triggered immediate alarm. For a team planning 8-GPU deployments with aggressive pipelining, a memory footprint that large could render the entire architecture infeasible.

This article examines a single message in the opencode coding session — message index 1467 — where the assistant, responding to the user's pointed question "In previous run why was peak mem 375G? Did we copy PCE for each partition and not dedupe?", traced the exact origin of that alarming memory number. What the assistant discovered was not a memory leak, not a deduplication failure, and not a fundamental flaw in the PCE design, but something far more mundane: a benchmark artifact caused by holding two complete sets of synthesis results simultaneously for validation comparison.

The Context: Phase 5 of the cuzk Proving Engine

The conversation leading up to this message documents an intensive optimization campaign for the cuzk proving engine, a custom GPU-accelerated Groth16 prover for Filecoin. By Phase 5, the team had already achieved significant wins: a 1.42× speedup in synthesis through the Pre-Compiled Constraint Evaluator, which replaces the expensive enforce() calls during circuit synthesis with a pre-compiled sparse matrix multiplication. The PCE works by recording the R1CS constraint structure once (a one-time 46.9s extraction cost), then reusing that structure for all future proofs — replacing ~24s of per-proof enforce overhead with an 8.8s CSR MatVec.

But the 375 GB peak memory reported during the PCE benchmark cast a shadow over these achievements. The user's question in message 1463 — "In previous run why was peak mem 375G? Did we copy PCE for each partition and not dedupe?" — cut to the heart of the concern. If the PCE was being duplicated per partition, the memory overhead would scale linearly with the number of concurrent pipelines, making multi-GPU deployment economically impossible.

The Subject Message: A Moment of Diagnostic Insight

Message 1467 is the assistant's response to that question. It begins with the assistant reading the source code and spotting a potential issue:

OK I see a problem on lines 429-445. For each circuit (10 times), we call density_tracker_from_words() which allocates a new BitVec from the PCE density words. Each DensityTracker contains a BitVec<usize, Lsb0> with 130M bits = ~16 MiB. Three trackers × 10 circuits = 30 copies × 16 MiB = ~480 MiB. That's small — not the source of the 375 GB.

This opening is characteristic of the assistant's debugging methodology: start with the most obvious suspect, quantify its impact, and rule it out. The density tracker duplication is real — 30 copies of 16 MiB each — but at 480 MiB total, it's a rounding error in a system that deals in hundreds of gigabytes. The assistant immediately recognizes this and pivots to the real question.

The critical insight comes next:

But that's not the real memory problem. Let me think about the benchmark process memory. The pce-bench runs:

>

1. Step 1 — Baseline: synthesizes 10 circuits via old path → 10 ProvingAssignments with a/b/c/witness vectors each ~21 GiB total 2. Step 2 — PCE extraction: builds one circuit, runs RecordingCS → 25.7 GiB PCE allocated 3. Step 3 — PCE path: synthesizes 10 circuits via PCE → 10 more sets of a/b/c/witness

>

The benchmark keeps Step 1 results alive for validation comparison in Step 5. So both the baseline and PCE results are in memory simultaneously.

This is the moment of clarity. The assistant realizes that the benchmark is fundamentally flawed as a memory measurement tool: it holds the old-path results (Step 1) alive while running the PCE path (Step 3), precisely so it can compare them for correctness validation in Step 5. The 375 GB peak is not a property of the PCE design — it's an artifact of the benchmark's validation methodology.## The Reasoning Process: Tracing Memory Through Source Code

What makes this message remarkable is not just the conclusion, but the reasoning process it reveals. The assistant is working with incomplete information — it has identified that the density tracker duplication is not the culprit, and it has formed a hypothesis about the benchmark holding both result sets. But at this point in the conversation, the assistant has not yet verified this hypothesis by reading the benchmark source code. The message ends with the assistant initiating a read tool call on the benchmark's main.rs file.

The thinking here is structured like a forensic investigation. The assistant first identifies a concrete code path (lines 429-445, the density_tracker_from_words() call), quantifies its impact (480 MiB), and dismisses it. Then it shifts to a higher-level analysis of the benchmark's control flow, reconstructing the sequence of operations from memory. This reconstruction is based on the assistant's knowledge of the pce-bench subcommand, which it helped design and implement earlier in the conversation.

The assistant's mental model of the benchmark execution is:

  1. The old-path synthesis produces 10 ProvingAssignments, each containing a/b/c vectors of ~130M field elements (32 bytes each) plus witness data — totaling ~163 GiB
  2. The PCE extraction builds the CSR matrices — ~25.7 GiB
  3. The PCE path synthesis produces another set of results — ~125 GiB for a/b/c alone
  4. Both result sets are kept alive for validation This reconstruction is remarkably accurate. In the follow-up message (message 1468), the assistant confirms the hypothesis by reading the benchmark source and produces a precise breakdown: ~163 GiB baseline + ~125 GiB PCE results + ~42 GiB input/aux arcs + ~25.7 GiB PCE static + ~20 GiB misc = ~375 GiB. The numbers match perfectly.

Assumptions and Their Validation

The assistant makes several assumptions in this message, some explicit and some implicit:

Assumption 1: The PCE itself is not duplicated. The assistant has already verified (in message 1466) that the PCE is stored in a static OnceLock — a single copy shared across all threads and pipelines. This assumption is correct and is the foundation of the entire memory analysis.

Assumption 2: The benchmark keeps Step 1 results alive. This is the hypothesis the assistant is forming, and it drives the read tool call at the end of the message. The assistant assumes that the validation logic in Step 5 requires both result sets to be in memory simultaneously. This turns out to be correct.

Assumption 3: The per-pipeline working set is ~21 GiB. This is derived from the assistant's understanding of the ProvingAssignment structure: a/b/c vectors (each 130M × 32B ≈ 4.2 GiB) plus witness data. This assumption is reasonable and consistent with the old path's memory footprint.

Assumption 4: The 375 GB peak is a benchmark artifact, not a production concern. This is the key conclusion, but at this point in the message it remains a hypothesis awaiting verification. The assistant is careful not to state it as fact — instead, it says "Let me verify" and initiates the read.

The most significant potential mistake would be if the benchmark did duplicate the PCE per partition despite the OnceLock. The assistant's earlier verification (message 1466) showed the PCE is stored in a static variable, but the synthesize_with_pce function (lines 358-372) could theoretically clone or copy portions of it. The density tracker duplication the assistant spots at the beginning of this message is a real (if minor) instance of per-circuit allocation from shared PCE data. The assistant correctly judges that 480 MiB is negligible, but the pattern of per-circuit allocations from the PCE deserves attention — if any of those allocations scaled with the full PCE size (25.7 GiB), the memory model would break down.

Input Knowledge Required

To fully understand this message, the reader needs knowledge of several domains:

Groth16 proof structure: The ProvingAssignment contains a/b/c vectors representing the R1CS constraint evaluation. Each vector has one element per constraint variable (input + aux), and each element is a field element (32 bytes in the BLS12-381 scalar field used by Filecoin).

CSR sparse matrix format: The PCE stores constraints as Compressed Sparse Row matrices, where each row represents one constraint and stores only the non-zero coefficients. For 130M constraints with ~309M non-zeros in the A matrix, the storage is ~11.6 GiB.

Rust memory management: The OnceLock (a std::sync::OnceLock or similar) provides lazy initialization with static lifetime — the PCE is initialized once and never freed. Arc<Vec<Fr>> provides shared ownership of vectors. The assistant's analysis of which data is shared vs. per-pipeline depends on understanding Rust's ownership and lifetime semantics.

Filecoin PoRep circuit structure: The circuit has 10 partitions, each with ~13M constraints. The benchmark synthesizes all 10 partitions (10 circuits) to represent a full proof.

Linux process memory measurement: The 375 GB figure comes from RSS (Resident Set Size) measurement, which counts physical RAM pages mapped into the process. RSS can overcount due to shared memory mappings but is the standard metric for peak memory usage.## Output Knowledge Created

This message creates several pieces of actionable knowledge:

  1. The 375 GB peak is a benchmark artifact. The PCE itself is not duplicated. The peak comes from holding old-path and PCE-path results simultaneously for validation.
  2. The real PCE memory overhead is 25.7 GiB static + 4.2 GiB temporary per pipeline. The static CSR matrices are shared across all pipelines via OnceLock. The temporary witness copy during MatVec is freed before the GPU phase begins.
  3. The per-pipeline working set is unchanged from the old path. Both old-path synthesis and PCE-path synthesis allocate ~21 GiB per pipeline for a/b/c/witness vectors. The PCE does not increase per-pipeline memory.
  4. A benchmark redesign is needed. The existing pce-bench subcommand is unsuitable for memory measurement because it holds both result sets. A new benchmark is needed that drops results between phases and tracks RSS at each stage.
  5. The density tracker allocation pattern is a minor optimization opportunity. 30 copies of 16 MiB BitVecs = 480 MiB is small but wasteful. This could be eliminated by sharing the density trackers or constructing them lazily. The most important output is the confidence it provides for multi-GPU deployment planning. For an 8-GPU system with 2 pipelines per GPU (16 concurrent pipelines), the PCE adds only ~25.7 GiB static overhead — about 3.6% of the total ~738 GiB system memory. This is dramatically different from the ~375 GB per-pipeline nightmare that the raw benchmark number suggested.

The Thinking Process: A Window into Debugging Methodology

The assistant's reasoning in this message reveals a systematic debugging methodology that is worth examining in detail.

Step 1: Scan for obvious culprits. The assistant reads the source code and immediately spots the density_tracker_from_words() call in a loop over circuits. This is a classic "low-hanging fruit" check — look for places where data is allocated per-circuit that could be shared.

Step 2: Quantify and dismiss. Rather than stopping at the discovery, the assistant immediately quantifies the impact: 30 copies × 16 MiB = 480 MiB. This quantification is crucial because it prevents chasing a red herring. 480 MiB in a 375 GB system is 0.13% — not worth optimizing.

Step 3: Reconstruct the execution flow. The assistant mentally replays the benchmark's sequence of operations, identifying which data structures are alive at each stage. This is a form of manual memory tracing, done without a profiler.

Step 4: Form a hypothesis. The hypothesis — that both result sets are held simultaneously — explains the 375 GB peak without requiring any PCE duplication. It's the simplest explanation that fits the data.

Step 5: Verify with source code. The assistant initiates a read tool call on the benchmark source to confirm the hypothesis. This is the critical step that separates speculation from diagnosis.

What's notable is what the assistant does not do. It does not immediately blame the PCE design. It does not assume the worst-case scenario (PCE copied per partition). It does not propose a complex fix before understanding the root cause. Instead, it follows the evidence from small to large, from specific code paths to overall process structure.

The Broader Significance

This message is a turning point in the Phase 5 investigation. Before it, the team was operating under the assumption that the PCE had a memory problem — 375 GB peak was baked into the project documentation and the commit message. After it, the focus shifts from "how do we fix the PCE memory leak" to "how do we build a benchmark that accurately measures production memory usage."

The user's response in message 1469 — "Run a benchmark which demonstrates lower memory use + heavier pipelining (maximizing gpu use)" — shows that the assistant's analysis was accepted and the team moved to validation. The subsequent work (messages 1470-1526) involved designing and implementing a new pce-pipeline subcommand with inline RSS tracking, malloc_trim calls, and parallel pipeline simulation. The sequential benchmark confirmed the memory model: RSS dropped cleanly from 155.7 GiB (old path) to 25.8 GiB (PCE static), rose to 181.6 GiB during PCE synthesis, and dropped back to 25.9 GiB after results were freed. The parallel benchmark with 2 concurrent pipelines peaked at 310.9 GiB and dropped cleanly.

This message also illustrates a broader principle in systems optimization: benchmark artifacts are often more dangerous than actual bugs. A real bug has predictable symptoms and can be fixed. A benchmark artifact creates misleading data that can send optimization efforts in entirely wrong directions. The 375 GB number, if left unexplained, could have led the team to spend weeks optimizing the wrong thing — perhaps trying to reduce PCE static size or implement per-partition PCE sharing that was never needed.

Conclusion

Message 1467 is a masterclass in diagnostic reasoning under uncertainty. The assistant, faced with an alarming memory number and a pointed question from the user, systematically traced the 375 GB peak to its source: not a memory leak, not a deduplication failure, but a benchmark that held two complete synthesis results simultaneously for validation. The reasoning process — from density trackers to execution flow reconstruction to hypothesis formation to source code verification — demonstrates a methodical approach that any systems engineer would recognize.

The message's lasting contribution is not just the explanation of the 375 GB peak, but the framework it establishes for thinking about PCE memory in production. The distinction between static shared data (25.7 GiB, paid once) and per-pipeline working set (~21 GiB, unchanged from old path) became the foundation for all subsequent memory modeling. When the team later validated this model with empirical benchmarks, they were confirming a prediction that this message made possible.

In the end, the phantom 375 GB was not a ghost in the machine — it was a reflection in the mirror, a double image of the benchmark's own validation logic staring back at the team. The assistant's willingness to question the benchmark rather than the architecture it measured is what turned a potential crisis into a footnote in the project documentation.