Tracing the 375 GB Peak: How One Question Uncovered a Benchmark Artifact

In the course of optimizing a Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep), a team had just completed Phase 5 Wave 1 of their Pre-Compiled Constraint Evaluator (PCE). The results were promising: a 1.42× speedup over the baseline, with all 10 circuits × 130 million constraints validating bit-for-bit correct. But one number stood out as troubling: 375 GB peak RAM usage, up from 340 GB in the old path. When the user asked about memory scaling for multi-GPU deployments, the assistant provided a detailed breakdown showing that the PCE added only 25.7 GiB of static overhead, amortized across all pipelines. But the user wasn't satisfied with the theoretical model. They asked a pointed question that cut straight to the heart of the matter:

"In previous run why was peak mem 375G? Did we copy PCE for each partition and not dedupe?"

This question, and the assistant's response in [msg 1464], marks a critical turning point — a moment where a seemingly straightforward benchmark number was interrogated rather than accepted, leading to a much deeper understanding of what was actually being measured.

The Message: A Commitment to Empirical Investigation

The assistant's response in [msg 1464] is deceptively simple. It begins with "Good catch. Let me trace exactly what happened in that run." — an immediate acknowledgment that the user's intuition was correct and that the 375 GB number deserved scrutiny. The assistant then performs a single tool call: reading the source file /home/theuser/curio/extern/cuzk/cuzk-core/src/pipeline.rs, specifically the extract_and_cache_pce_from_c1 function.

This is not a message that provides answers. It is a message that sets up the investigation. The assistant could have responded with more theoretical analysis, speculating about what might cause the 375 GB peak. Instead, it chose to go directly to the source code to trace the exact memory allocation pattern. This decision reveals several things about the assistant's methodology:

  1. Evidence over speculation: When confronted with a discrepancy between the theoretical memory model (~25.7 GiB static PCE overhead) and the observed measurement (375 GB peak), the assistant's first instinct is to check the code, not to rationalize the number.
  2. Respect for the user's insight: The user's question — "Did we copy PCE for each partition and not dedupe?" — was a specific hypothesis about a potential bug. The assistant treats this as a valid technical concern worth investigating, not a naive question.
  3. Systematic debugging approach: The assistant traces the problem to its root by following the code path. It reads the pipeline source to check whether the PCE is stored in a single static OnceLock (as designed) or whether it's being duplicated per partition.

The Context: Why 375 GB Mattered

To understand the significance of this message, we need to understand the stakes. The PCE was designed to reduce synthesis time by pre-compiling the constraint system into a CSR (Compressed Sparse Row) matrix format, then evaluating it with a fast parallel SpMV (Sparse Matrix-Vector) multiplication. The trade-off was a one-time memory cost for storing the CSR matrices — approximately 25.7 GiB for the PoRep-32GiB circuit.

But the user was planning for an 8-GPU deployment with 2 pipelines per GPU. In that scenario, memory is the critical resource. If the PCE's memory overhead scaled linearly with the number of pipelines — if each pipeline needed its own 25.7 GiB copy of the CSR matrices — then the total memory requirement would balloon far beyond what any reasonable system could provide. The user's question about the 375 GB peak was essentially asking: "Is this a sign that our memory model is broken?"

The assistant's earlier analysis in [msg 1462] had confidently stated that the PCE was stored in a static OnceLock — a single copy shared across all pipelines. But the 375 GB peak contradicted this model. If the static overhead was only 25.7 GiB, and the per-pipeline working set was ~21 GiB, then even running both the old path and the PCE path in the same process shouldn't have produced 375 GB. Something was wrong, and the user had spotted it.

The Investigation Begins

The assistant's message is the first step in a forensic investigation. By reading the pipeline source, the assistant is checking the actual implementation against the design. The code snippet shown in the message reveals the function signature:

#[cfg(feature = "cuda-supraseal")]
pub fn extract_and_cache_pce_from_c1(
    vanilla_proof_json: &[u8],
    _sector_number: u64,
    _miner_id: u64,
) -> anyhow::Result<()> {

This function takes a C1 output (the first phase of the PoRep proof) and extracts the PCE from it. The question the assistant is trying to answer: does this function store the extracted PCE in a global static cache (as designed), or does it return a new copy each time it's called?

The answer, which would be revealed in subsequent messages ([msg 1465]), is that the 375 GB peak was a benchmark artifact — the benchmark code was running both the old path and the PCE path in the same process, holding the results of both for validation comparison. The old path consumed ~163 GiB, the PCE path consumed ~125 GiB, and the PCE extraction itself consumed additional memory. When all three were held simultaneously, the peak reached 375 GB. In production, only one path would run at a time, and the PCE would be extracted once and shared.

What This Message Reveals About the Assistant's Thinking

The assistant's thinking process, visible in the structure of the response, shows several key characteristics:

First, the assistant immediately validates the user's concern. "Good catch" is not a throwaway compliment — it's an acknowledgment that the user has identified a genuine discrepancy that needs investigation. This sets a collaborative tone where the user's observations are treated as valuable contributions to the debugging process.

Second, the assistant frames the investigation in terms of tracing. "Let me trace exactly what happened in that run" signals a commitment to following the code path from input to output, rather than jumping to conclusions. This is a disciplined debugging methodology.

Third, the assistant identifies the specific question to answer. The user asked two questions: "why was peak mem 375G?" and "Did we copy PCE for each partition and not dedupe?" The assistant recognizes that the second question is the key to answering the first. If the PCE was being duplicated, that would explain the high memory usage. If it wasn't, then something else was causing it.

Fourth, the assistant goes directly to the source. Rather than reasoning from memory or theory, the assistant reads the actual code. This is a crucial point: the assistant has access to the entire codebase and can read any file. It chooses to read the pipeline source because that's where the PCE extraction and caching logic lives.

The Broader Significance

This message, while brief, represents a best practice in engineering communication. When confronted with a puzzling data point, the response is not to explain it away but to investigate it. The assistant's willingness to say "let me check" rather than "here's why it's fine" builds trust and leads to better outcomes.

The 375 GB peak turned out to be a benchmark artifact, not a memory leak or a deduplication failure. But that conclusion could only be reached by doing the investigation that this message begins. The user's pointed question forced a deeper examination of the memory model, which ultimately validated the PCE design and provided confidence for multi-GPU deployment.

In the end, the investigation showed that the PCE's memory overhead was exactly as designed: 25.7 GiB static, shared across all pipelines, with no per-pipeline duplication. The 375 GB peak was an artifact of running both paths simultaneously for validation. The real production memory profile was clean, scalable, and well-understood.

But none of that would have been discovered without the user's question — and the assistant's decision to trace it to the source.