The Moment of Truth: Running the PCE Pipeline Benchmark

A Message of Validation and Discovery

In the course of a months-long optimization campaign for the Filecoin PoRep Groth16 proving pipeline, one message stands out as a decisive turning point — a moment when months of design, debugging, and implementation crystallized into a single, high-stakes execution. Message [msg 1515] is that moment. It is the assistant's first run of the newly constructed pce-pipeline benchmark, designed to empirically validate the memory model of the Pre-Compiled Constraint Evaluator (PCE) and to address the user's pointed concern about whether the PCE's memory overhead would scale gracefully across multi-GPU deployments.

This article examines that message in depth: the reasoning that motivated it, the decisions embedded in its execution, the assumptions it carried, the knowledge it required as input, and the critical knowledge it produced as output. It is a story about how engineering confidence is built — not through argument or analysis alone, but through carefully designed experiments that confront theoretical models with real hardware.

The Context: A Crisis of Confidence

The message arrives at the end of a long debugging and optimization arc. The PCE — a mechanism that pre-compiles the constraint system of a Filecoin PoRep circuit into a static CSR (Compressed Sparse Row) matrix representation — had been implemented, debugged, and shown to produce bit-identical results to the old synthesis path while achieving a 1.42× speedup (see [chunk 17.0]). But a critical question remained unanswered.

The user had noticed that the earlier pce-bench benchmark showed a peak memory usage of approximately 375 GiB. This number was alarming. The PCE was supposed to reduce memory overhead by sharing static constraint data across proofs, yet the benchmark suggested it might be doing the opposite — consuming nearly 400 GiB of RAM. The user's question, paraphrased from the conversation, was pointed: "Is the 375 GB peak coming from per-partition PCE copying rather than deduplication?" In other words, was the PCE actually duplicating its data across circuits instead of sharing it?

The assistant had already traced the 375 GiB peak to a benchmark artifact — the old pce-bench held both the old-path baseline results (~163 GiB) and the PCE path results (~125 GiB) simultaneously for validation comparison. But the user was not fully satisfied. The question demanded an empirical answer, not a code review. The assistant needed to build a benchmark that could demonstrate, with real memory measurements, that the PCE's static data lived in a single OnceLock shared across all pipelines, and that the per-pipeline working set remained essentially unchanged from the old path.

This is the context in which message [msg 1515] is written. It is not a message of design or analysis. It is a message of execution — the moment the assistant commits to running the experiment that will either confirm or refute the memory model.

What the Message Actually Says

The message is a bash command block that orchestrates a multi-step benchmark execution. It begins by launching a memory monitor script in the background:

/tmp/cuzk-benchmon.sh /tmp/pce-pipeline-mem.csv 0.5 &
MONPID=$!
echo "Monitor PID: $MONPID"

This monitor polls /proc/<pid>/status every 500 milliseconds, recording the VmRSS value of any process matching target/release/cuzk-bench. It writes a CSV with timestamps, RSS in KB, and RSS in GiB.

Then the actual benchmark invocation:

FIL_PROOFS_PARAMETER_CACHE=/data/zk/params /usr/bin/time -v ./target/release/cuzk-bench pce-pipeline --c1 /data/32gbench/c1.json --num-proofs 3 --compare-old 2>&1

Key parameters:

The Reasoning: Why This Specific Experiment?

The assistant's reasoning in designing this benchmark reveals a sophisticated understanding of what constitutes a convincing validation. Several design choices are worth examining.

First, the choice of --compare-old. The assistant could have run the PCE path alone, which would have been simpler and faster. But the user's concern was specifically about whether the PCE path introduced new memory overhead compared to the old path. By running both paths in sequence — first the old path, then the PCE path — the benchmark could directly compare peak RSS. The old-path results are held in memory briefly, then explicitly dropped (with malloc_trim to force the allocator to release memory back to the OS), and then the PCE path runs. This creates a clear before-and-after picture.

Second, the choice of three proofs. A single proof would not demonstrate amortization — the PCE extraction cost is paid once, and subsequent proofs reuse the cached CSR matrices. Three proofs show that the first proof pays the extraction cost (visible as a one-time spike), while the second and third proofs reuse the cached data and show a lower, stable memory profile. This directly addresses the production scenario where the proving daemon runs continuously, extracting the PCE once at startup and then serving proofs indefinitely.

Third, the 500ms sampling interval. The assistant chose a relatively aggressive sampling rate for the memory monitor. The PCE synthesis takes approximately 35 seconds (26.5s witness generation + 8.8s MatVec), so a 500ms interval provides roughly 70 samples per synthesis phase. This is enough to capture the peak RSS with reasonable accuracy without creating an unwieldy CSV file.

Fourth, the use of /usr/bin/time -v. This provides an independent, kernel-level measurement of peak RSS. The inline RSS tracking (via /proc/self/status reads in the benchmark code) and the external monitor (via /proc/<pid>/status reads from a shell script) give three independent measurements of memory usage. Triangulating between them increases confidence in the results.

Assumptions Embedded in the Experiment

Every experiment carries assumptions, and this one is no exception. The assistant made several implicit assumptions that are worth examining.

Assumption 1: The memory monitor would correctly identify the cuzk-bench process. The monitor script uses pgrep -f 'target/release/cuzk-bench' to find the process. This assumes that the process name matches that pattern and that only one such process exists at a time. In practice, this assumption turned out to be partially wrong — as shown in the subsequent message ([msg 1516]), the monitor captured 0 values because it was monitoring its own shell process (5912 KB) instead of the benchmark process. The pgrep pattern matched the shell script itself (which contained the string "target/release/cuzk-bench" in its invocation). This is a subtle but important failure: the external monitor was useless for this run. Fortunately, the inline RSS tracking built into the benchmark code provided the necessary data.

Assumption 2: malloc_trim would reliably release memory. The benchmark code calls malloc_trim(0) after dropping large data structures to encourage the allocator to return memory to the OS. This assumes that the glibc allocator will actually release memory when asked. In practice, malloc_trim is known to be unreliable for releasing memory from all arenas, especially in multi-threaded programs. The assistant was aware of this limitation and used it as a best-effort mechanism rather than a guaranteed one.

Assumption 3: The benchmark environment is representative of production. The benchmark runs on a machine with 512 GiB of RAM and an RTX 5070 Ti GPU. The assistant assumes that the memory patterns observed here — the PCE static data of 25.7 GiB, the per-pipeline working set of ~156 GiB — will generalize to other hardware configurations. This is a reasonable assumption for the same circuit (32 GiB PoRep C2), but it would not hold for different circuit sizes or different proving parameters.

Assumption 4: Sequential proof execution is a valid proxy for pipelined execution. The benchmark runs proofs sequentially: it completes proof 1, drops the results, then starts proof 2. In production, the proving daemon would pipeline: while the GPU is processing proof N, the CPU is synthesizing proof N+1. The sequential benchmark captures the steady-state memory of a single pipeline, but it does not capture the peak memory of overlapping pipelines. The user immediately recognized this limitation, asking in the next message ([msg 1517]): "Couldn't this run parallel?" This led the assistant to add a --parallel flag in subsequent messages ([msg 1518] through [msg 1523]), enabling concurrent synthesis of multiple proofs to simulate multi-GPU deployments.

The Thinking Process: What the Assistant Was Considering

The assistant's thinking process, visible in the trajectory of messages leading up to [msg 1515], reveals a careful weighing of options. In message [msg 1470], the assistant outlined the benchmark design goals:

  1. Drop baseline results before running PCE path (lower peak memory)
  2. Run multiple sequential proofs to show PCE amortization
  3. Measure memory at each stage The assistant considered whether to add RSS tracking as a separate script or inline in the benchmark code. It initially searched for an existing RSS measuring script (messages [msg 1486] through [msg 1497]), finding /tmp/cuzk-memmon.sh which monitored the daemon process. Rather than modifying that script, the assistant created a new variant (cuzk-benchmon.sh) that monitors cuzk-bench instead. This decision to create a parallel monitoring infrastructure rather than reuse the existing one reflects a desire for clean separation of concerns — the daemon monitor and the benchmark monitor have different lifetimes and targets. The assistant also made a deliberate choice about the benchmark's internal architecture. Rather than calling the daemon's gRPC API, the pce-pipeline subcommand runs the synthesis inline in the same process. This allows precise RSS tracking at each phase boundary (before extraction, after extraction, after synthesis, after drop) because the benchmark code can call malloc_trim and read /proc/self/status directly. A daemon-based approach would have required parsing daemon logs or adding IPC for memory reporting.

Input Knowledge Required

To understand this message, several pieces of prior knowledge are necessary.

The PCE architecture: The Pre-Compiled Constraint Evaluator stores the constraint system as static CSR matrices in a OnceLock, shared across all proof pipelines. This is the key design decision that the benchmark aims to validate.

The memory model: The old synthesis path creates a fresh ConstraintSystem for each proof, consuming ~156 GiB for the working set (10 circuits × ~130M constraints each). The PCE path adds 25.7 GiB of static CSR data but keeps the per-pipeline working set unchanged. The total peak memory with PCE is ~181.6 GiB (25.7 static + ~156 working set), compared to ~155.7 GiB for the old path — a modest increase of ~26 GiB.

The benchmark infrastructure: The cuzk-bench crate has a pce-bench feature flag that enables PCE-related subcommands. The pce-pipeline subcommand was added in messages [msg 1501] through [msg 1507], building on the existing pce-bench subcommand structure.

The hardware context: The machine has 512 GiB of RAM, so the ~181 GiB peak of the PCE path is comfortably within budget. On a machine with 256 GiB of RAM, the same workload would be tight but feasible. On a machine with 128 GiB, it would not fit.

The /proc/self/status interface: The benchmark reads VmRSS from /proc/self/status to get current resident set size. This is a Linux-specific interface; the benchmark would not work on other operating systems without modification.

Output Knowledge Created

This message, and the benchmark run it initiates, produces several pieces of critical knowledge.

First, it empirically validates the PCE memory model. The subsequent messages ([msg 1516] and [msg 1518]) report the actual numbers: the old path peaks at 155.7 GiB, the PCE extraction leaves 25.8 GiB of static data, the PCE synthesis peaks at 181.6 GiB, and after dropping results the RSS falls back to 25.9 GiB. This clean sawtooth pattern confirms that the PCE static data is shared and not duplicated, and that there is no memory leak across multiple proofs.

Second, it identifies a bug in the external memory monitor. The monitor script captured 0 values because pgrep matched the shell script itself rather than the benchmark process. This is a valuable operational insight: process monitoring via pattern matching is fragile, and inline measurements are more reliable.

Third, it establishes a benchmarking methodology. The combination of inline RSS tracking, malloc_trim calls, /usr/bin/time -v for independent verification, and multiple proof iterations creates a template for future memory characterization work. The methodology is documented in the benchmark code and can be reused for other optimization experiments.

Fourth, it reveals the need for parallel benchmarking. The user's immediate question — "Couldn't this run parallel?" — shows that the sequential benchmark, while useful, does not fully address the multi-GPU deployment scenario. This insight drives the addition of the --parallel flag in subsequent messages, leading to a second round of benchmarking that validates the memory model for concurrent pipelines.

Mistakes and Incorrect Assumptions

The most visible mistake in this message is the assumption that the external memory monitor would work correctly. The monitor script used pgrep -f 'target/release/cuzk-bench' which matched the shell script itself (since the script's command line contained that string). This is a classic "grep matching itself" problem. The result was a CSV file full of zeros — the monitor was reading the RSS of its own shell process (5912 KB) rather than the benchmark process.

This mistake is instructive for several reasons. First, it demonstrates the importance of independent verification: the inline RSS tracking in the benchmark code provided the real data, so the monitor failure was not catastrophic. Second, it shows that even carefully designed experiments can have subtle instrumentation errors. The assistant's decision to include three independent measurement methods (inline, external monitor, and /usr/bin/time -v) meant that the failure of one method did not compromise the overall experiment.

A more subtle issue is the assumption that malloc_trim(0) is sufficient to release memory. In glibc, malloc_trim only releases memory from the main arena, not from per-thread arenas. In a multi-threaded Rust program with heavy allocation patterns, significant amounts of memory may remain in thread-local arenas even after malloc_trim. The benchmark's clean RSS drops (155.7 GiB → 0.1 GiB after dropping old-path results) suggest that malloc_trim was effective in this case, but this may not generalize to all workloads.

The Broader Significance

Message [msg 1515] is significant not because of what it says, but because of what it does. It is the moment when a theoretical model — the PCE's shared static data, the 25.7 GiB overhead, the clean amortization across proofs — confronts real hardware. The assistant could have continued analyzing code, tracing paths, and building arguments. Instead, it chose to run an experiment.

This choice reflects a deeper engineering philosophy: that confidence comes from measurement, not reasoning. The assistant had already traced the 375 GiB peak to a benchmark artifact through code analysis (see [chunk 17.1]). But rather than presenting this analysis as the final answer, the assistant built a new benchmark specifically designed to demonstrate the correct memory behavior. The message is the execution of that benchmark — the moment of empirical validation.

The message also illustrates the iterative nature of engineering experimentation. The benchmark was not perfect on the first try: the memory monitor failed, the sequential-only design was immediately identified as insufficient by the user, and the results required careful interpretation. But each iteration produced new knowledge. The parallel benchmark that followed ([msg 1524]) confirmed that 2 concurrent pipelines peak at 310.9 GiB (2 × ~156 GiB working set + 25.7 GiB static), validating the linear scaling model. The final results, documented in the project file, showed that for an 8-GPU system with 2 pipelines per GPU, the PCE adds only ~3.6% total memory overhead (~738 GiB vs ~712 GiB).

Conclusion

Message [msg 1515] is a message of action — the moment when months of design, debugging, and optimization culminated in a decisive experiment. It carries the weight of the assistant's reasoning about what constitutes a convincing validation, the assumptions about instrumentation and measurement, and the humility to let data speak. The external monitor failure, the user's immediate request for parallel benchmarking, and the subsequent iteration all demonstrate that engineering knowledge is built through cycles of experimentation, not through single definitive tests.

For a reader unfamiliar with the conversation, this message represents the transition from theory to practice. It is the point at which the PCE's memory model stops being a claim about code and becomes a measured property of a running system. The numbers that emerged from this benchmark — 25.8 GiB static, 181.6 GiB peak, clean drops, no leaks — became the foundation for the multi-GPU deployment analysis that followed. In the end, the PCE's memory overhead was confirmed to be a one-time static cost that scales gracefully with the number of concurrent pipelines, directly answering the user's core concern.