Validating the PCE Memory Model Under Parallel Pipelines

The Message

The subject message (global index 1524) is a single action by the AI assistant in an opencode coding session: the execution of a parallel benchmark to validate the Pre-Compiled Constraint Evaluator (PCE) memory model for multi-GPU deployments. The message reads:

Good. Now let me run the parallel benchmark. I'll test with -j 2 first (2 concurrent pipelines — realistic for 1 GPU with double-buffering), then -j 4:

``bash FIL_PROOFS_PARAMETER_CACHE=/data/zk/params /usr/bin/time -v ./target/release/cuzk-bench pce-pipeline --c1 /data/32gbench/c1.json --num-proofs 4 -j 2 2>&1 ``

=== PCE Pipeline Benchmark === c1: /data/32gbench/c1.json sector: 1 (miner 1000) num_proofs: 4 parallel: 2 (concurrent pipelines) compare: PCE only

c1 loaded: 51510727 bytes [RSS] after c1 load 0.1 GiB

--- PCE Extraction (one-time) --- 2026-02-18T03:30:00.527246Z INFO cuzk_pce::recording_cs: RecordingCS: extracted pre-compiled circuit num_inputs=328 num_aux=130169893 num_cons...

On the surface, this is a straightforward command invocation. But to understand why this message exists and what it accomplishes, we must trace the chain of reasoning, debugging, and architectural validation that led to this specific moment in the conversation.

The Problem: A 375 GiB Memory Anomaly

The story begins with a troubling number. In the previous chunk of work (segment 17, chunk 0), the assistant had implemented Phase 5 of the cuzk proving engine — the Pre-Compiled Constraint Evaluator. The PCE was designed to eliminate the overhead of constraint evaluation during Groth16 proof synthesis by pre-compiling the circuit's constraint system into a compressed sparse row (CSR) matrix format, then evaluating it with a fast multi-threaded MatVec multiplication. The initial benchmarks were promising: a 1.42× speedup over the old path, with PCE synthesis completing in 35.5 seconds versus 50.4 seconds for the baseline.

But then the user raised a pointed question about memory. The PCE's static data — the CSR matrices for 10 circuits — consumed approximately 25.7 GiB. During the pce-bench comparison run, the benchmark had been observed peaking at roughly 375 GiB of resident memory. The user's concern was immediate and practical: if the PCE required 375 GiB per machine, it would be economically infeasible for multi-GPU deployments where multiple proving pipelines run concurrently on the same hardware.

The assistant's first response was to trace the exact origin of the 375 GiB peak. By reading the benchmark source code, the assistant confirmed that the PCE itself was stored in a single static OnceLock — a global singleton that is never duplicated across circuits. The 375 GiB was identified as a benchmark artifact: the pce-bench subcommand held both the old-path baseline results (~163 GiB) and the PCE path results (~125 GiB) simultaneously in memory for validation comparison. In production, only one path would be active at a time. The real overhead was just 25.7 GiB of static CSR matrix data, shared across all pipelines, with the per-pipeline working set remaining at approximately 21 GiB — essentially unchanged from the old path.

The Need for Empirical Validation

This analysis was sound, but it was still a desk-check. The assistant had not yet measured the memory behavior of the PCE under realistic pipeline conditions. To address this, the assistant designed and implemented a new pce-pipeline subcommand for the cuzk-bench benchmarking tool. This subcommand was engineered with several key features:

Inline RSS tracking via /proc/self/status, allowing memory snapshots at every phase of the pipeline without requiring an external monitoring script.
malloc_trim calls between phases to aggressively release memory back to the operating system, preventing allocator hysteresis from masking true memory usage.
A --compare-old flag to optionally run the old-path baseline alongside the PCE path for validation.
A --parallel (-j) flag to simulate concurrent pipeline execution, directly addressing the multi-GPU deployment question. The sequential benchmark had already produced clean results: RSS dropped 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 dropped. No memory leak. No unexpected accumulation. But the user's question — "Couldn't this run parallel?" ([msg 1517]) — cut to the heart of the matter. The sequential benchmark showed that a single pipeline's memory was well-behaved, but production systems would run multiple pipelines concurrently. An 8-GPU machine might have 2 pipelines per GPU, for 16 concurrent syntheses. If the per-pipeline working set simply multiplied, the total memory could be prohibitive.

The Reasoning Behind the Parallel Benchmark

The assistant's reasoning in this message is visible in the choice of parameters. The -j 2 flag was chosen first because, as the assistant notes, it is "realistic for 1 GPU with double-buffering." Double-buffering is a common technique in GPU programming where two command buffers or data buffers are used in交替: while the GPU processes one buffer, the CPU prepares the next. For a single GPU running proofs, this means two concurrent pipelines — one being consumed by the GPU, one being produced by the CPU.

The --num-proofs 4 parameter with -j 2 means the benchmark would run four proofs total, with at most two in flight simultaneously. This is sufficient to show the steady-state memory behavior: the first two proofs would peak together, then as one completes and its memory is freed, the next proof would start, maintaining the same peak.

The assistant also planned to test -j 4 afterward, which would simulate a more aggressive scenario — perhaps two GPUs with double-buffering each, or a single GPU with deeper pipelining.

Assumptions Embedded in the Test

Several assumptions underlie this benchmark. First, the assistant assumes that the per-pipeline working set is independent and additive — that two concurrent pipelines will consume approximately twice the per-pipeline working set, plus the shared PCE static data. This is a reasonable assumption given that each pipeline has its own WitnessCS, ProvingAssignment, and temporary vectors, but it needed empirical confirmation.

Second, the assistant assumes that malloc_trim is effective at releasing memory. Linux's malloc implementation (glibc) does not always return freed memory to the OS immediately; it may hold onto it in per-thread caches. The malloc_trim call forces a release of all freed memory back to the kernel, giving a true picture of the minimum RSS at each phase. Without this, the benchmark might show artificially high RSS due to allocator caching.

Third, the assistant assumes that the PCE's OnceLock-based singleton is truly shared across threads. In Rust, OnceLock (formerly OnceCell) provides thread-safe lazy initialization, but the assistant had to verify that the benchmark code was not inadvertently cloning or copying the PCE data into each thread's local scope.

The Execution and Its Significance

The message captures the moment of execution. The benchmark begins, loading the C1 circuit file (51,510,727 bytes), then proceeds to PCE extraction. The output is truncated in the conversation data, but the full results — as documented in the chunk summary — showed a peak of 310.9 GiB for 2 concurrent pipelines (2 × ~156 GiB working set + 25.7 GiB static), dropping cleanly back to the PCE baseline after completion.

This result was significant for several reasons. First, it confirmed that the memory model was additive and well-behaved: 2 pipelines consumed roughly 2× the per-pipeline working set, plus the fixed PCE overhead. There was no super-linear scaling, no hidden duplication, no memory leak. Second, it demonstrated that the PCE's static 25.7 GiB was truly a one-time cost — it did not multiply with the number of pipelines. For an 8-GPU system with 2 pipelines per GPU, the total memory would be approximately 738 GiB (16 × ~44.5 GiB per-pipeline + 25.7 GiB static), compared to approximately 712 GiB without PCE — a mere 3.6% overhead.

The Thinking Process Visible in the Message

The assistant's thinking is revealed in the structure of the command and the commentary. The choice of -j 2 before -j 4 shows a deliberate incremental approach: start with the most realistic scenario, validate, then stress-test. The parenthetical "realistic for 1 GPU with double-buffering" shows the assistant reasoning about the production deployment context, not just the benchmark numbers.

The use of /usr/bin/time -v is also telling. This is not just a timing tool — it reports peak RSS, page faults, and other memory statistics. The assistant is cross-referencing the inline RSS tracking with the kernel's own accounting, providing a belt-and-suspenders approach to memory measurement.

The assistant also chose to run with --compare-old disabled (PCE only), unlike the sequential run which compared both paths. This is a deliberate simplification: the parallel benchmark is focused purely on the PCE path's memory behavior under concurrency, not on validation against the old path. The validation had already been done in the sequential run.

Input and Output Knowledge

To understand this message, one needs input knowledge of: the Groth16 proof generation pipeline for Filecoin PoRep, the concept of constraint evaluation in R1CS circuits, the CSR matrix format for sparse linear algebra, the Linux memory model including RSS and malloc_trim, the Rust OnceLock synchronization primitive, and the double-buffering pattern in GPU programming.

The output knowledge created by this message is the empirical validation that the PCE's memory overhead is a one-time static cost that scales gracefully with concurrent pipelines. This directly addresses the user's core concern about multi-GPU deployment feasibility. The benchmark results, combined with the sequential results, provide a complete picture of the PCE's memory characteristics: no leak, no unexpected accumulation, and a predictable additive model for concurrency.

Mistakes and Corrective Context

There is no mistake in this message itself — the command is correct and the reasoning is sound. However, it is worth noting that the entire parallel benchmark was a response to an oversight in the initial sequential benchmark. The assistant had originally designed the pce-pipeline subcommand without a parallel mode, implicitly assuming that sequential execution was sufficient to validate the memory model. The user's question revealed this gap, and the assistant correctly pivoted to add parallel support.

This is a pattern worth observing: the assistant tends to think in terms of single-pipeline performance (synthesis time, memory per proof) and can miss the system-level question of how multiple pipelines interact. The user, thinking about production deployment, naturally asks about concurrency. The assistant's willingness to rapidly implement the parallel mode — going from idea to working benchmark in a few messages — shows the value of having a flexible benchmarking infrastructure that can be extended on demand.

Conclusion

Message 1524 is a moment of empirical validation in a longer investigation. It represents the point where a theoretical memory model — "the PCE's static data is shared, so it doesn't multiply with pipelines" — is tested against reality. The parallel benchmark confirmed the model, and the results were documented in the project file and staged for commit. For the reader, this message illustrates the importance of testing assumptions under realistic concurrency conditions, and the power of a well-designed benchmarking tool that can be rapidly extended to answer new questions.