The Commit That Captured 407 GiB: Parallel Pipeline Benchmarking for PCE

Message Under Analysis

The subject of this article is message <msg id=1541>, a single bash command issued by the AI assistant during an opencode coding session focused on optimizing the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep). The message is deceptively simple — a git commit — but it represents the culmination of an intensive investigation into the memory and performance characteristics of a novel proving engine component called the Pre-Compiled Constraint Evaluator (PCE).

Here is the message exactly as it appeared:

git commit -m "feat(cuzk): pce-pipeline bench subcommand + parallel (j=2) results

Add PcePipeline subcommand to cuzk-bench for testing PCE memory behavior
under sequential and parallel pipelining modes:
- RSS tracking via /proc/self/status at each pipeline stage
- malloc_trim() between proofs for clean memory release
- Wave-based parallel execution using std::thread::scope (-j N flag)
- compare_old flag for A/B comparison in first iteration

Update cuzk-project.md with j=2 parallel pipeline benchmark results:
- 2 concurrent syntheses: 49s wall vs 71s sequential (1.45x wall speedup)
- Per-proof degradation: 46-49s (vs 35.5s j=1) due to BW contention
- Peak RSS: 407 GiB (2x working sets + PCE static + transient)"

At first glance, this looks like a routine commit. But within its 414 lines of changed code and its carefully crafted commit message lies a critical inflection point in the optimization journey: the moment when the team validated — and discovered the limitations of — their memory model for concurrent proof generation.

Context: Where This Message Fits

To understand <msg id=1541>, one must understand the arc of the session. The overall project is optimizing the SUPRASEAL_C2 Groth16 prover, a GPU-accelerated proof generator used by Filecoin storage miners. The baseline system consumed approximately 200 GiB of peak memory per proof and took roughly 50 seconds for synthesis alone. The optimization effort proceeded in phases, each targeting a different bottleneck.

Phase 5 introduced the Pre-Compiled Constraint Evaluator (PCE), a transformative architectural change. Instead of re-synthesizing the entire circuit constraint system for every proof (which involved expensive enforce() calls and allocation-heavy LinearCombination operations), PCE records the constraint structure once — producing sparse CSR (Compressed Sparse Row) matrices A, B, and C — and then reuses them across proofs. Each subsequent proof only needs fast witness generation (WitnessCS) followed by matrix-vector multiplication (MatVec) using the pre-recorded CSR matrices. This two-phase approach was measured at 1.42× synthesis speedup (50.4s → 35.5s).

However, the PCE approach came with a trade-off: the CSR matrices consumed 25.7 GiB of static memory, loaded once per process into OnceLock globals. The per-pipeline working set remained large at ~156 GiB (10 circuits × ~16 GiB each during parallel synthesis). For a production deployment targeting multiple concurrent proofs — say, on an 8-GPU system with 2 pipelines per GPU — understanding how these memory footprints interacted was essential.

The immediate predecessor messages in the conversation show the assistant designing and running a pce-pipeline benchmark subcommand. Message <msg id=1524> ran the parallel benchmark with -j 2 (two concurrent pipelines), and the results were dramatic: peak RSS hit 407 GiB, and per-proof synthesis time degraded from 35.5s to 46-49s due to memory bandwidth contention on the 96-core Zen4 CPU. Message <msg id=1525> then began the process of committing these results, and <msg id=1541> is the actual commit that sealed them into the project's history.

Why This Message Was Written

The commit serves multiple purposes, each revealing a layer of reasoning.

First, it captures a benchmark tool. The PcePipeline subcommand was not trivial — it required RSS tracking via /proc/self/status, malloc_trim() calls between proofs to measure clean memory release, wave-based parallel execution using std::thread::scope, and a compare_old flag for A/B comparison on the first iteration. This is infrastructure that future optimization work will depend on. Committing it now ensures it doesn't get lost in the working tree.

Second, it records a critical empirical finding. The parallel benchmark revealed that the PCE memory model, while correct, has a subtle performance characteristic: memory bandwidth contention. When two syntheses run concurrently, they share the 96-core CPU's memory channels. The per-proof time jumps from 35.5s to 46-49s — a 30-38% degradation. The wall-clock speedup is still positive (49s for two proofs vs 71s sequentially = 1.45×), but the efficiency per proof drops. This finding directly impacts deployment decisions: running more concurrent pipelines does not linearly increase throughput.

Third, it establishes a baseline for future optimization. The 407 GiB peak RSS and the bandwidth contention numbers are now permanent reference points. When Phase 6 (the slotted pipeline) or other optimizations attempt to reduce memory or improve throughput, these numbers serve as the "before" state. The commit message's explicit mention of "2x working sets + PCE static + transient" also provides a mental model for understanding the RSS composition.

Fourth, it fulfills a project management requirement. The todo list in the assistant's context (visible in <msg id=1530>) shows "Update cuzk-project.md with parallel pipeline (j=2) benchmark results" and "Commit pce-pipeline subcommand + project doc updates" as high-priority items. This commit closes those tickets.## How Decisions Were Made

The commit message reveals a series of deliberate design decisions embedded in the benchmark tooling.

The decision to track RSS via /proc/self/status rather than using a GPU-side memory monitor or a system-level tool like nvidia-smi reflects a focus on CPU-side memory pressure. The PCE static data (25.7 GiB) and the per-pipeline working sets (~156 GiB each) dwarf the GPU's 16 GiB VRAM. The bottleneck is host memory, not device memory. By reading the kernel's own memory accounting, the benchmark gets precise, per-process RSS numbers at each pipeline stage — essential for understanding where memory peaks occur.

The decision to call malloc_trim() between proofs is a subtle but important one. Rust's default allocator (glibc's malloc) does not eagerly return memory to the OS. After dropping large vectors (~156 GiB per pipeline), the memory remains in the process's address space as cached free pages. malloc_trim() forces the allocator to release these pages back to the kernel, allowing the benchmark to measure the "true" steady-state memory after cleanup. Without this, successive proofs would show artificially high RSS as freed-but-unreleased pages accumulate. The commit message's inclusion of this detail signals that the benchmark was designed for accuracy, not just convenience.

The wave-based parallel execution model using std::thread::scope is another deliberate choice. Rather than spawning independent threads or using a thread pool, scoped threads ensure that all parallel work completes before the scope exits, providing clean synchronization boundaries. This is important for memory measurement: the benchmark can log RSS before the wave, during the wave (from a monitoring thread), and after the wave (after malloc_trim). The -j N flag makes the parallelism level configurable, allowing future benchmarks to test j=1, j=2, j=4, etc., and build a scaling curve.

The compare_old flag for A/B comparison on the first iteration reveals another design choice: the first proof in any PCE-enabled process triggers PCE extraction (the old path runs once to record constraints), while subsequent proofs use the fast PCE path. By optionally running the old path on the first iteration even when PCE is available, the benchmark can produce a direct A/B comparison within a single run — eliminating noise from separate benchmark invocations.

Assumptions Made

The commit and its surrounding context rest on several assumptions, some explicit and some implicit.

Assumption: Memory bandwidth contention is the primary cause of per-proof degradation. The commit message attributes the 46-49s per-proof time (vs 35.5s at j=1) to "BW contention." This is a reasonable inference — on a 96-core Zen4 CPU, two concurrent syntheses each spawning 96 threads would compete for memory channels — but it is not directly proven. The benchmark does not measure memory bandwidth utilization directly (e.g., via perf stat or RAPL counters). It could also be cache thrashing, TLB pressure, or NUMA effects. The assumption is that bandwidth is the bottleneck, which is plausible given the memory-bound nature of sparse matrix-vector multiplication, but it remains an assumption.

Assumption: 407 GiB peak RSS is acceptable for the target deployment. The commit does not flag 407 GiB as a problem. It simply records it. This implies an assumption that the target machines (likely high-memory cloud instances or dedicated proving hardware) can accommodate this footprint. For a system with 512 GiB or 1 TiB of RAM, 407 GiB is manageable. But for smaller instances, this would be prohibitive — and the Phase 6 slotted pipeline design (mentioned in the segment summary) aims to reduce this by streaming partitions sequentially rather than synthesizing all 10 in parallel.

Assumption: The PCE static 25.7 GiB is a one-time cost worth paying. The commit message treats the PCE static memory as a fixed overhead that is acceptable because it is shared across all proofs. This assumes that the proving process is long-lived (a daemon that handles many proofs) rather than short-lived (a one-shot CLI tool). The daemon integration work in the same segment confirms this assumption: PCE is preloaded at daemon startup, and the first-proof penalty is eliminated by triggering PCE extraction during initialization.

Input Knowledge Required

To fully understand this commit, a reader needs knowledge spanning several domains.

Rust systems programming: Understanding std::thread::scope, OnceLock, malloc_trim from the libc crate, and the memory model of Rust's standard allocator. The commit modifies Rust source files in cuzk-bench/src/main.rs (282 lines) and cuzk-bench/Cargo.toml (adding the libc dependency).

Linux memory management: Knowing that /proc/self/status contains the VmRSS field, that RSS includes both anonymous pages and file-backed pages, and that malloc_trim() releases heap memory to the OS. The distinction between "peak RSS" (from /usr/bin/time -v) and "inline RSS" (from /proc/self/status at specific points) is important.

Groth16 proof generation: Understanding that PoRep C2 synthesis involves 10 partitions, each with ~130 million constraints, and that the constraint system is represented as sparse matrices A, B, C. The concept of "witness generation" vs "constraint evaluation" is central to the PCE architecture.

Benchmarking methodology: Knowing why malloc_trim() is necessary between iterations, why wave-based parallelism matters for memory measurement, and how to interpret per-proof degradation in concurrent workloads.

Output Knowledge Created

This commit produces several forms of knowledge that persist beyond the session.

A reusable benchmark tool. The PcePipeline subcommand can now be run by any developer working on the cuzk project to test memory behavior under different parallelism levels, different circuit sizes, or different hardware configurations. The tool's design — RSS tracking, malloc_trim, wave-based parallelism — sets a standard for future benchmarks.

A documented memory model for concurrent PCE proving. The commit message's breakdown of the 407 GiB peak RSS into "2x working sets + PCE static + transient" provides a mental model that future optimization work can target. If Phase 6 reduces the working set per pipeline, the expected peak RSS for j=2 would be 2 * reduced_working_set + 25.7 GiB + transient. This makes the optimization target explicit.

A benchmark result that informs deployment decisions. The 1.45× wall speedup at j=2 (49s vs 71s) with 30-38% per-proof degradation tells operators that running more concurrent pipelines has diminishing returns. This is the kind of data that informs hardware sizing: do you buy a machine with more memory channels (to reduce BW contention), or do you accept the degradation and run more pipelines to maximize throughput?

A git history checkpoint. The commit at 63ba20e5 is a clean, well-described snapshot that future developers can git checkout to reproduce the exact state of the PCE benchmark at this point in the optimization journey. The 414 lines of insertion (282 in the benchmark, 132 in the project doc) are now permanently linked to the commit message's summary of results.## Mistakes and Incorrect Assumptions

While the commit itself is a clean record of empirical data, the surrounding context reveals some assumptions that may need revisiting.

The bandwidth contention hypothesis is untested. As noted above, the 46-49s per-proof time is attributed to "BW contention," but no direct bandwidth measurement was performed. A more rigorous approach would use perf stat to measure LLC-load-misses, DRAM bandwidth (via uncore events on Zen4), or at least compare against a single-threaded-per-pipeline configuration to isolate the effect. Without this, the contention could be from other sources — for example, the 96 cores competing for L3 cache (the Zen4 Threadripper has 384 MiB of L3 cache across 8 CCDs, but each CCD has only 32 MiB, so cross-CCD communication could be a factor). The commit would be stronger if it included a perf stat run alongside the timing data.

The 407 GiB peak RSS may overstate the problem. The peak RSS reported by /usr/bin/time -v includes all pages that were ever resident, including pages that were freed but not yet reclaimed by the kernel. The inline RSS tracking (which logs RSS at specific pipeline stages) likely shows a lower peak. The commit message does not distinguish between these two measurements, potentially conflating "worst-case observed" with "typical working set." A more precise accounting would separate transient allocations (e.g., temporary vectors during MatVec) from the persistent working set.

The benchmark does not test GPU memory pressure. The PCE pipeline is CPU-focused — it replaces CPU-side constraint synthesis. But the GPU phase (NTT, MSM) still runs after synthesis, and the GPU has only 16 GiB VRAM. If the PCE path somehow changes the GPU memory footprint (e.g., by producing different-sized witness data), that would be invisible to this benchmark. The commit assumes that CPU-side memory is the only concern, which may be true for this phase but is not validated.

The assumption that j=2 is representative. The benchmark tests only j=2 (two concurrent pipelines). Real deployments might run 4, 8, or 16 concurrent pipelines. The scaling behavior at higher parallelism levels could be qualitatively different — for example, at j=4, the memory bandwidth contention might become severe enough that per-proof time approaches the sequential time, eliminating the wall-clock speedup entirely. The commit would be more useful if it included a scaling sweep (j=1, j=2, j=4, j=8) to characterize the knee of the curve.

The Thinking Process Visible in the Commit

The commit message itself is a compressed record of the assistant's reasoning. Let me unpack what it reveals.

The first paragraph — "Add PcePipeline subcommand to cuzk-bench for testing PCE memory behavior under sequential and parallel pipelining modes" — shows that the assistant was thinking about two regimes: sequential (one proof at a time, measuring clean memory release) and parallel (multiple proofs overlapping, measuring contention). This dual focus reflects an understanding that production systems operate in both modes: during low load, proofs run sequentially; during bursts, they queue up and overlap.

The bullet points about RSS tracking, malloc_trim, wave-based parallelism, and compare_old reveal a methodological mindset. The assistant was not just collecting numbers; it was designing an experiment with controls (A/B comparison), cleanup phases (malloc_trim), and precise measurement points (RSS at each stage). This is the thinking of an engineer who has been burned by noisy benchmarks before.

The second paragraph's breakdown of results — "49s wall vs 71s sequential (1.45× wall speedup)" and "Per-proof degradation: 46-49s (vs 35.5s j=1) due to BW contention" — shows the assistant interpreting the data in real time. The 1.45× speedup is the headline number (good), but the degradation footnote is the important caveat (nuanced). By including both in the commit message, the assistant ensures that future readers see the full picture, not just the marketing-friendly number.

The final line — "Peak RSS: 407 GiB (2x working sets + PCE static + transient)" — reveals a mental accounting model. The assistant is decomposing the 407 GiB into components: 2 × ~156 GiB working sets = ~312 GiB, plus 25.7 GiB PCE static = ~338 GiB, leaving ~69 GiB as "transient" (temporary allocations during synthesis that overlap). This decomposition is not just descriptive; it is predictive. If Phase 6 reduces the per-pipeline working set by 2.5× (as the slotted pipeline design claims), the assistant can immediately compute the expected peak RSS for j=2: 2 × (156/2.5) + 25.7 + transient ≈ 150 GiB. The model enables forward reasoning.

Conclusion

Message <msg id=1541> is a git commit, but it is far more than a version control operation. It is a scientific instrument — a tool for measuring and understanding the memory behavior of a complex system under realistic workloads. It is a documentation artifact — a permanent record of empirical findings that will inform future optimization decisions. And it is a reasoning trace — a window into the assistant's mental model of how memory, parallelism, and bandwidth interact in the PCE proving pipeline.

The 414 lines of code and the 132 lines of documentation changes that this commit captures represent the culmination of a multi-session investigation into the PCE architecture. The 407 GiB peak RSS and the 30-38% per-proof degradation are not just numbers; they are constraints that shape the design space for Phase 6 and beyond. The commit message's careful decomposition of the RSS into its components — working sets, static overhead, transient allocations — provides a vocabulary for reasoning about memory that will outlive the specific benchmark run.

In the broader arc of the optimization project, this commit marks the transition from Phase 5 validation (proving that PCE works correctly and provides speedup) to Phase 6 design (optimizing the pipeline for lower memory and better overlap). The slotted pipeline proposal, which was designed in the same segment as this commit, directly addresses the bandwidth contention and peak memory issues that this benchmark uncovered. The commit is thus both a conclusion and a beginning: it closes the chapter on understanding PCE's baseline behavior and opens the chapter on making it production-ready.