The Pivot to Instrumentation: Building a Memory-Aware Benchmark for the Pre-Compiled Constraint Evaluator

A Single Line of Code That Changed the Investigation

In the middle of an intense debugging session on the SUPRASEAL_C2 Groth16 proof generation pipeline, the assistant issued a message that, at first glance, appears trivial: a simple grep for subcommand names in a Rust source file. But this message, <msg id=1472>, represents a critical inflection point in the investigation — the moment the assistant shifted from analyzing a memory problem to building the tools needed to measure it empirically.

The message reads in full:

No RSS helper exists yet. Let me look at the full CLI definition to find where to add the new subcommand: [grep] PceBench|pce.bench|synth.only|SynthOnly Found 16 matches /home/theuser/curio/extern/cuzk/cuzk-bench/src/main.rs: Line 165: /// synthesis. Requires 'pce-bench' feature. Line 166: PceBench { Line 193: SynthOnly { Line 657: Commands::PceBench { c1, sector_num, miner_id, save_pce, validate } => { Line 658: run_pce_bench(c1, sector_num, miner_id, save_pce, validate)?; Line 661: Commands::SynthOnly { c1, sector_num, miner_id, iterations, partition } => { Line 662: run_synth_o...

The Context: A 375 GB Mystery

To understand why this message matters, we must trace back through the conversation. The user had just asked a pointed question about memory scaling: "pce ram - is it static/amortised even with multiple GPUs? How much ram will an 8GPU system need with strong pipelining?" This question came after the assistant reported a concerning 375 GB peak memory usage during the PCE benchmark — a number that seemed to contradict the promise of the Pre-Compiled Constraint Evaluator (PCE) as a memory-efficient optimization.

The assistant had spent the preceding messages tracing the memory issue through the source code. By reading pipeline.rs and the benchmark's main.rs, the assistant discovered that the 375 GB peak was 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. The PCE itself was stored in a single static OnceLock and never duplicated across circuits. The real production overhead was just 25.7 GiB of static CSR matrix data shared across all pipelines — a one-time cost amortized across every proof.

This analysis was thorough and convincing, but it was still analysis. The assistant had traced code paths, calculated memory budgets, and explained why the 375 GB number was misleading. But the user wanted more: "Run a benchmark which demonstrates lower memory use + heavier pipelining (maximizing gpu use)." This was a demand for empirical proof, not just theoretical reasoning.

The Decision Point: Why Build a New Tool?

The assistant's response in <msg id=1472> reveals a crucial design decision. Rather than trying to adapt the existing pce-bench subcommand — which was designed for correctness validation and inherently held both result sets — the assistant chose to build an entirely new benchmarking subcommand called pce-pipeline.

The reasoning is visible in the assistant's preceding message ([msg 1470]): "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 decision reflects several key assumptions:

First, the assistant assumed that the existing pce-bench could not be easily modified to demonstrate the desired memory profile. The benchmark's architecture was fundamentally tied to validation — it needed both result sets to compare against golden data. Retrofitting memory-efficient behavior would require significant restructuring.

Second, the assistant assumed that inline RSS tracking via /proc/self/status was the right approach. The user mentioned a pre-existing script (/tmp/cuzk-memmon.sh) that monitored the daemon process externally, but the assistant opted for internal instrumentation. This choice reflects a preference for precise, phase-aligned measurements over external polling.

Third, the assistant assumed that malloc_trim calls would be necessary to aggressively release memory between phases. This is a Linux-specific optimization that tells the allocator to return freed memory to the OS — critical for demonstrating clean memory drops in a benchmark scenario where the allocator might otherwise hold onto freed pages.

The Input Knowledge Required

Understanding this message requires familiarity with several layers of the system:

The cuzk-bench CLI structure: The assistant needed to know that subcommands are defined in main.rs using clap derive macros, and that adding a new subcommand requires defining both the CLI enum variant and the dispatch logic in the match statement.
The PCE pipeline architecture: The assistant needed to understand that extract_and_cache_pce_from_c1 stores the PCE in a static OnceLock, that synthesize_with_pce reads from this shared state, and that the per-circuit working set is dominated by the a/b/c vectors and witness data.
Memory measurement on Linux: The assistant needed to know that /proc/self/status exposes VmRSS in kilobytes, and that RSS (Resident Set Size) is the appropriate metric for physical memory usage.
The Rust memory model: The assistant needed to understand that dropping Vec<Fr> (where Fr is a 32-byte field element) releases the underlying allocation, but that the allocator may not immediately return the memory to the OS without malloc_trim.

The Output Knowledge Created

This message, though brief, set in motion the creation of several artifacts:

A new benchmark subcommand: The pce-pipeline subcommand would become a standalone tool capable of running N sequential proofs with RSS tracking at each stage, optional baseline comparison, and parallel pipeline simulation. This tool would produce concrete numbers that could settle the memory debate definitively.

Empirical validation of the memory model: The benchmarks that followed this message would demonstrate 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 after results were dropped. The parallel benchmark with 2 concurrent pipelines would peak at 310.9 GiB and drop cleanly back to the PCE baseline.

Documentation updates: The assistant would update cuzk-project.md with the Phase 5 memory analysis, the sequential and parallel benchmark results, and the updated roadmap — creating a permanent record of the memory characterization for future developers.

The Thinking Process Visible in the Message

The assistant's reasoning is visible in the very structure of the message. The grep command searches for PceBench, pce.bench, synth.only, and SynthOnly — the existing subcommands that serve as templates for the new one. By examining how these are defined and dispatched, the assistant can determine the exact code locations that need modification.

The message also reveals the assistant's awareness of what doesn't exist yet: "No RSS helper exists yet." This observation drives the decision to build RSS tracking from scratch rather than relying on existing infrastructure. The assistant had searched for fn rss_mib, fn get_rss, proc/self/status, VmRSS, and resident across the codebase and found nothing — confirming that memory measurement was a greenfield addition.

The Broader Significance

This message exemplifies a pattern that recurs throughout the opencode session: the assistant's willingness to build new instrumentation rather than rely on existing tools. Earlier in the session, the assistant built a synth-only microbenchmark to isolate synthesis performance from GPU overhead. Later, the assistant would add CUDA timing instrumentation to diagnose GPU wrapper regressions. Each new tool was purpose-built for a specific measurement need.

The decision to build a new benchmark rather than modify the existing one also reflects a deeper engineering philosophy: separate validation from demonstration. The pce-bench subcommand was designed for correctness — it needed to hold both result sets to compare them. The pce-pipeline subcommand was designed for memory characterization — it needed to drop results aggressively to show clean memory transitions. These are fundamentally different design goals, and trying to serve both with a single tool would have produced a compromise that satisfied neither.

Conclusion

Message <msg id=1472> is a turning point in the Phase 5 investigation. It marks the transition from theoretical analysis to empirical measurement, from explaining why the 375 GB peak was a benchmark artifact to building the tool that would prove it. The assistant's decision to build a new subcommand rather than retrofit the existing one reflects a clear understanding of the difference between validation and demonstration tools. The grep output, while seemingly mundane, represents the first step toward the concrete benchmarks that would ultimately validate the PCE's memory model and confirm that its 25.7 GiB static overhead scales gracefully across multiple concurrent pipelines — directly answering the user's core concern about multi-GPU deployment.