Tracing Memory: How One Message Resolved a 375 GB Mystery and Validated Multi-GPU Scaling

Introduction

In the middle of a deep optimization session for the cuzk proving engine — a GPU-accelerated SNARK prover for Filecoin's Proof-of-Replication (PoRep) — a single question from the user triggered a cascade of investigation, design, implementation, and validation that would reshape the team's understanding of their system's memory behavior. The question was deceptively simple: "In previous run why was peak mem 375G? Did we copy PCE for each partition and not dedupe?"

The answer, it turned out, was no — but proving it required building an entirely new benchmark, adding memory tracking infrastructure, designing parallel execution modes, and conclusively demonstrating that the Pre-Compiled Constraint Evaluator (PCE) — Phase 5 of the optimization roadmap — added only 25.7 GiB of static overhead, not the terrifying 375 GiB peak that had appeared in the validation benchmark. This article examines the pivotal message ([msg 1470]) where the assistant received the user's request and formulated the plan that would resolve the mystery.

Context: The Memory Mystery

The conversation leading up to [msg 1470] had been intense. Phase 5 of the cuzk proving engine — the Pre-Compiled Constraint Evaluator — had just been implemented, debugged, and benchmarked. The PCE promised to eliminate the overhead of R1CS constraint enforcement during synthesis by pre-recording the constraint structure and evaluating it via a fast CSR matrix-vector product. The initial results were promising: a 1.42× speedup (35.5s vs 50.4s for 10 circuits), with all 130 million constraints per circuit validating bit-for-bit correct.

But a dark cloud hung over the achievement. The benchmark had reported a peak memory usage of 375 GiB. For a system being designed for multi-GPU deployment — the user had explicitly asked about an 8-GPU configuration with 2 pipelines per GPU — this was potentially catastrophic. If the PCE was duplicating its data structures per partition or per circuit, the memory cost would scale linearly with the number of concurrent pipelines, making multi-GPU deployment economically infeasible.

The assistant had initially analyzed the memory model theoretically, calculating that the PCE added only 25.7 GiB of static CSR matrix data (shared via a OnceLock) and that per-pipeline working set remained ~21 GiB — essentially unchanged from the old path. But the user's follow-up question in [msg 1463] cut 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 was the moment of truth. The theoretical model said one thing; the empirical measurement said another. Something was wrong, and the assistant needed to trace the exact origin of those 375 GiB.

The Message: Formulating the Plan

Message [msg 1470] is the assistant's response to the user's request to "Run a benchmark which demonstrates lower memory use + heavier pipelining (maximizing gpu use)." It is a planning message — no code is written, no commands are executed. Instead, the assistant thinks through what the benchmark needs to demonstrate and sets the direction for the subsequent implementation.

The message opens with a reasoning block that reveals the assistant's mental model:

Let me think about what this benchmark needs to demonstrate. The real production scenario is: PCE path with the daemon, where synthesis overlaps with GPU proving. The current pce-bench holds both results in memory for validation. I need a benchmark that: 1. Drops baseline results before running PCE path (lower peak memory) 2. Runs multiple sequential proofs to show PCE amortization 3. Measures memory at each stage

This reasoning is crucial. The assistant immediately identifies the root cause of the 375 GiB peak: the existing pce-bench subcommand was a validation benchmark, not a production benchmark. It held both the old-path results (~163 GiB for 10 circuits) and the PCE-path results (~125 GiB) simultaneously in memory so it could compare them for correctness. The 375 GiB was a benchmark artifact — real memory usage, but not representative of production behavior.

The three requirements the assistant identifies are:

  1. Drop baseline results before running the PCE path — this alone would cut peak memory roughly in half
  2. Run multiple sequential proofs to demonstrate PCE amortization — the first proof extracts the PCE (one-time 46.9s cost), subsequent proofs reuse it
  3. Measure memory at each stage — inline RSS tracking to show exactly where memory goes The message then sets up a todowrite with the plan: - "Design memory-efficient PCE benchmark that drops baseline before PCE run" - "Add memory tracking (RSS snapshots) at each pipeline stage" - "Run 2+ sequential proofs to show PCE amortization (1st extracts, 2nd+ reuses)" - "Simulate pipelined execution: synthesis..." The final todo is cut off but clearly points toward the parallel execution mode that would be added later in the chunk.

Assumptions and Knowledge

The assistant makes several assumptions in this message that are worth examining:

Assumption 1: The 375 GiB peak is a benchmark artifact, not a real memory leak. This assumption is based on the assistant's understanding of the pce-bench code — it knew that both baseline_synth and pce_synth result vectors were held alive simultaneously for validation comparison. This turned out to be correct, as confirmed by subsequent source code reading in [msg 1468].

Assumption 2: The PCE is stored in a static OnceLock and never duplicated. The assistant had designed the PCE infrastructure and knew that PreCompiledCircuit was stored in a static OnceLock — a single copy shared across all pipelines. This assumption was validated by reading the pipeline source code in <msg id=1465-1466>.

Assumption 3: Memory can be accurately tracked via inline RSS snapshots. The assistant planned to add RSS tracking by reading /proc/self/status — a standard Linux mechanism. This assumption proved correct, though the external memmon.sh script had issues with process name matching (as seen in [msg 1516] where it monitored its own shell process instead of cuzk-bench).

Input knowledge required to understand this message includes:

The Thinking Process

The assistant's reasoning in [msg 1470] reveals a sophisticated debugging methodology. Rather than immediately diving into code, the assistant first establishes what the "real production scenario" looks like. This is a critical step — it separates the artifact (benchmark validation) from the essence (production memory behavior).

The assistant identifies three specific design criteria for the new benchmark, each addressing a different aspect of the memory question:

  1. Drop baseline → answers "is the 375 GiB from holding both paths?"
  2. Multiple sequential proofs → answers "does PCE memory grow with each proof or stay stable?"
  3. Memory measurement at each stage → answers "where exactly does memory go and when does it get freed?" The todo list shows the assistant's execution plan: design first, then implement RSS tracking, then build the subcommand, then run it. This is a classic engineering approach — plan before building, measure before concluding. Notably, the assistant does not jump to conclusions. The message says "Let me first check what the daemon E2E path looks like, then build a targeted benchmark." This shows intellectual humility — the assistant acknowledges it needs to verify the daemon's production flow before designing the benchmark around it.

What Followed

The subsequent messages in the chunk ([msg 1471] through [msg 1525]) execute the plan laid out in [msg 1470]. The assistant:

  1. Read the daemon/pipeline code ([msg 1471]) to understand the production synthesis flow
  2. Checked for existing RSS helpers ([msg 1471]) — finding none, it would build its own
  3. Updated the project documentation (<msg id=1474-1482>) with Phase 5 findings
  4. Found the existing memmon script at /tmp/cuzk-memmon.sh ([msg 1497]) — a bash script that monitored RSS of cuzk-daemon
  5. Implemented the pce-pipeline subcommand (<msg id=1501-1512>) with inline RSS tracking, malloc_trim calls, and a --compare-old flag
  6. Built and ran the sequential benchmark ([msg 1515]) — showing RSS dropping cleanly from 155.7 GiB (old path) to 25.8 GiB (PCE static), rising to 181.6 GiB during PCE synthesis, and dropping back to 25.9 GiB
  7. Added parallel mode (<msg id=1518-1523>) in response to the user's question "Couldn't this run parallel?"
  8. Ran the parallel benchmark ([msg 1524]) with 2 concurrent pipelines, peaking at 310.9 GiB and dropping cleanly back to the PCE baseline The results conclusively demonstrated that the PCE's memory overhead was a one-time static cost of 25.7 GiB, shared across all pipelines, with no memory leak and no per-circuit duplication. The 375 GiB peak was indeed a benchmark artifact from holding both old-path and PCE-path results simultaneously.

Significance

Message [msg 1470] is a masterclass in responding to a pointed technical question. The user asked about a specific anomaly (375 GiB peak) and requested a specific demonstration (lower memory benchmark with heavier pipelining). The assistant's response:

  1. Acknowledged the question by immediately pivoting from theoretical analysis to empirical validation
  2. Identified the root cause (benchmark artifact) through reasoning about the production scenario vs the validation scenario
  3. Designed a targeted experiment that would either confirm or refute the hypothesis
  4. Set up the execution plan with clear todos and priorities The message also demonstrates the importance of separating measurement artifacts from system behavior. The 375 GiB was a real measurement — the system did allocate that much memory. But it was not representative of production behavior, where only one path is active at a time. The assistant's ability to distinguish between "what the benchmark does" and "what the system does in production" was the key insight that drove the entire investigation.

Conclusion

Message [msg 1470] represents the turning point in the Phase 5 memory investigation. Before this message, the team had a theoretical model suggesting 25.7 GiB static overhead and a measurement showing 375 GiB peak — a contradiction that needed resolution. After this message, the team had a plan to build the right benchmark, run the right experiment, and get the right answer.

The message itself is brief — just a reasoning block and a todo list — but it encapsulates the engineering thinking that would guide the next 50+ messages of implementation and validation. It shows that sometimes the most valuable contribution is not writing code, but thinking clearly about what question to ask and how to ask it.