The Benchmon Script: A Pivot from Tool-Building to Empirical Validation

Introduction

In the midst of a deep optimization campaign for the cuzk Groth16 proving engine, a single message from the assistant marks a critical inflection point: the transition from building a benchmark to running it. Message [msg 1514] is deceptively brief — a bash script, a chmod, and the words "Clean. Now let me start the memmon script in the background and run the benchmark." But this moment encapsulates the entire philosophy of the optimization effort: every theoretical claim about memory usage and pipelining must be validated empirically before it can be trusted.

Context: The Memory Question

To understand why this message matters, one must understand the problem it addresses. The cuzk project is a persistent GPU-resident SNARK proving engine for Filecoin's Proof-of-Replication (PoRep) protocol. The team had just completed Phase 5, implementing the Pre-Compiled Constraint Evaluator (PCE) — a mechanism that pre-computes the constraint system matrix once and reuses it across all subsequent proofs, amortizing a one-time extraction cost of ~46.9 seconds across potentially thousands of proofs.

The PCE had demonstrated a 1.42× speedup in synthesis (35.5s vs 50.4s baseline), but a critical question remained: what about memory? Earlier in the conversation, the user had pointedly asked about a 375 GB peak RAM usage observed during benchmarks. Was this a real production problem — perhaps caused by per-partition PCE copying or failure to deduplicate — or was it a benchmark artifact? The answer would determine whether the PCE approach was viable for multi-GPU deployments where memory is a precious resource.

The assistant had traced the 375 GB peak to a benchmark artifact: the pce-bench subcommand held both the old-path baseline (~163 GiB) and the PCE path (~125 GiB) results simultaneously for validation comparison. In production, only one path would run at a time. The real PCE memory overhead was just 25.7 GiB of static CSR matrix data, shared across all pipelines via a OnceLock, with per-pipeline working set remaining at ~21 GiB — essentially unchanged from the old path.

But this analysis was theoretical. It relied on reading source code and calculating allocations. The user wanted proof — a benchmark that demonstrates lower memory use and heavier pipelining, maximizing GPU utilization while keeping memory under control.

The Message: A Script is Born

Message [msg 1514] is the assistant's response to this challenge. Having spent the previous messages designing and implementing the pce-pipeline subcommand — a new benchmark mode that runs multiple sequential proofs with RSS tracking, malloc_trim calls to aggressively release memory between phases, and a --parallel flag to simulate concurrent pipeline execution — the assistant now needs to actually run it.

The message opens with "Clean," referring to the successful compilation of the cuzk-bench binary after fixing a type annotation error. This single word signals that the tool-building phase is complete. The next step is empirical: "Now let me start the memmon script in the background and run the benchmark."

But there's a problem. The existing memory monitoring script, /tmp/cuzk-memmon.sh, was designed to track cuzk-daemon — the persistent proving server process. The new benchmark runs as cuzk-bench, a different binary. The monitoring script needs to be adapted.

The Reasoning Process

The assistant's reasoning is visible in the structure of the new script. Rather than modifying the original /tmp/cuzk-memmon.sh, the assistant creates a parallel variant: /tmp/cuzk-benchmon.sh. This is a deliberate design choice — it preserves the original script for daemon monitoring while creating a specialized version for the benchmark tool. The two scripts can run simultaneously if needed, and each produces its own output file.

The script itself reveals several design decisions:

Process identification via pgrep: The script uses pgrep -f 'target/release/cuzk-bench' to find the benchmark process. The -f flag matches against the full command line, which is important because the process might be launched with various arguments. However, this pattern match is deliberately specific — it matches target/release/cuzk-bench rather than just cuzk-bench, avoiding false matches from development builds in target/debug/ or from grep itself.

Polling with graceful degradation: The script polls every 0.5 seconds (the INTERVAL default). If no matching process is found, it sleeps and retries. If a process is found but its /proc/PID/status file is unreadable (perhaps because the process exited between the pgrep and the awk call), it also sleeps and retries. This makes the script robust to the natural lifecycle of a benchmark process that starts, runs, and exits.

Peak tracking: The script tracks peak RSS in addition to logging every sample. This is critical for the memory analysis — the peak value represents the worst-case memory pressure, which is what matters for capacity planning in multi-GPU deployments.

Unit conversion: RSS is logged in both kilobytes and gibibytes (using bc for floating-point division). The GiB column makes the output human-readable for the ~100+ GiB ranges the benchmark operates in.

Output format: The CSV format (timestamp_ms,rss_kb,rss_gib) is designed for easy import into spreadsheet software or plotting tools. The epoch millisecond timestamp enables precise correlation with benchmark log output.

Assumptions and Their Implications

The assistant makes several assumptions in this message:

That the benchmark process will be identifiable by process name: The pgrep -f 'target/release/cuzk-bench' pattern assumes the binary is launched from the target/release/ directory. If run from a different path (e.g., ./cuzk-bench or an installed location), the script won't find it. This is a reasonable assumption for a development environment but would need adjustment for production deployment.

That /proc/PID/status is available and contains VmRSS: This assumption holds on Linux systems with procfs mounted, which is standard. However, it would fail on containers with restricted /proc access, on macOS (which uses a different memory reporting mechanism), or if the process has exited before the read.

That 0.5-second polling is sufficient: The script samples every half second. For a benchmark that runs for minutes, this provides ~120+ data points — enough to reconstruct the memory timeline. However, rapid memory allocation/deallocation events within sub-second windows could be missed. The assistant implicitly assumes that memory changes at the scale of GiB happen over seconds, not milliseconds, which is reasonable for CPU-side synthesis workloads.

That bc is available for floating-point arithmetic: The script uses bc for the GiB conversion. This is standard on Linux but not guaranteed on minimal systems. The assistant could have used awk for the calculation, but bc is more readable.

Input Knowledge Required

To fully understand this message, one needs:

Knowledge of the Linux proc filesystem: The /proc/PID/status file and the VmRSS field are standard Linux interfaces for querying process memory. Understanding that VmRSS reports resident set size in kilobytes is essential.

Knowledge of the cuzk project architecture: The distinction between cuzk-daemon (the persistent server) and cuzk-bench (the benchmarking tool) is crucial. The original memmon script tracked the daemon; the new one tracks the benchmark.

Knowledge of the Phase 5 memory analysis: The 375 GB peak, the 25.7 GiB static PCE overhead, and the ~21 GiB per-pipeline working set are the context that makes this benchmark meaningful. Without this background, the script appears to be a generic memory monitor.

Knowledge of the pce-pipeline subcommand: The assistant is about to run this new subcommand, which was built in the preceding messages. The script is the measurement instrument for that experiment.

Output Knowledge Created

This message creates:

A reusable memory monitoring tool: /tmp/cuzk-benchmon.sh can be used for any future cuzk-bench run, not just the immediate benchmark. Its design (CSV output, peak tracking, configurable interval) makes it suitable for regression testing and performance validation.

A foundation for empirical validation: The script enables the critical experiment that will either confirm or refute the assistant's memory model. The results (which appear in subsequent messages) show 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 are dropped — confirming no memory leak and validating the model for multi-GPU scaling.

A pattern for process-specific monitoring: By creating a variant of the existing daemon monitor, the assistant establishes a pattern: one monitoring script per process type, each with appropriate process matching. This pattern could be extended to monitor other components of the proving pipeline.

The Broader Significance

This message, for all its apparent simplicity, represents a commitment to empirical rigor. The entire Phase 5 effort — the PCE design, the CSR matrix infrastructure, the multi-threaded evaluation, the integration into the pipeline — rests on a memory model that was, until this moment, theoretical. The assistant could have declared victory based on the 1.42× speedup and moved on. Instead, the user's pointed question about the 375 GB peak forced a deeper investigation, and the assistant responded by building the tools needed to answer it definitively.

The cuzk-benchmon.sh script is not elegant. It is a bash script with a polling loop, a pgrep hack, and a dependency on bc. But it is fit for purpose. It answers the question: "What is the actual peak memory usage of the PCE pipeline, and does it scale gracefully across multiple concurrent proofs?" The answer — confirmed by the subsequent benchmark run — is that the PCE's memory overhead is a one-time static cost, shared across all pipelines, and the per-pipeline working set is unchanged from the old path.

In the broader narrative of the cuzk optimization campaign, this message is the moment when theory meets measurement. The assistant has built the instrument, calibrated it, and is about to take the reading. The results will shape the architecture of the multi-GPU proving system, influence deployment decisions, and determine whether the PCE approach is viable for production.

Conclusion

Message [msg 1514] is a bridge between two phases of engineering: the construction phase, where tools and optimizations are built, and the validation phase, where they are tested against reality. The bash script it contains is a small piece of work — 30 lines of shell code — but it carries the weight of the entire memory model investigation. By adapting an existing monitoring script to track the benchmark process, the assistant demonstrates a pragmatic approach to instrumentation: use what exists, adapt it to the task at hand, and get the data you need. The script's design choices — process matching, polling interval, peak tracking, CSV output — all reflect a deep understanding of both the Linux process model and the specific memory characteristics of the proving pipeline. And the results it enables will determine the future direction of the cuzk project.