The Benchmark That Defined a Memory Frontier

Introduction

In the lifecycle of any high-performance engineering project, there comes a pivotal moment when the intense phase of feature development and optimization gives way to something equally important: systematic characterization. Message 3282 of this opencode session marks exactly that transition for the cuzk engine—a custom Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep). After weeks of implementing a complex split GPU proving API with memory backpressure (Phase 12), the assistant turns from builder to metrologist, launching the first run of a low-memory benchmark sweep designed to map the engine's memory scaling behavior across nine configurations.

This single message, seemingly just another command execution in a long session, is actually the opening move in a campaign that will produce the deployment guidance for the entire system. It represents the moment when raw engineering gives way to production readiness, when the question shifts from "can we make it work?" to "how should it be configured for different hardware?"

The Context: Phase 12 and the Memory Problem

To understand why this message was written, one must appreciate the engineering journey that preceded it. The cuzk engine had been through eleven optimization phases, each targeting a different bottleneck in the Groth16 proof generation pipeline. Phase 9 had identified DDR5 memory bandwidth contention as the primary bottleneck at high concurrency. Phase 10 attempted a two-lock GPU interlock design but was abandoned after discovering fundamental CUDA device-global synchronization conflicts that caused out-of-memory (OOM) errors and performance regressions. Phase 11 then implemented three memory-bandwidth interventions that brought the engine to 36.7s per proof at ~430 GiB peak memory.

Phase 12, the most recent work, introduced a split (async) GPU proving API that decoupled the b_g2_msm computation from the GPU worker loop, hiding its latency behind the critical path. This was accompanied by a memory backpressure system that included early deallocation of NTT evaluation vectors, automatic channel capacity scaling, and a partition semaphore permit-through-send fix. The result was a stable 37.7s per proof at pw=12 with ~400 GiB peak memory—a significant improvement over the 754 GiB that earlier configurations required.

But this optimization work had focused on maximum throughput. The user's question in message 3272—"Run cuzk with lower parallelsms to see how low we can get RSS requirements on smaller systems and the timings"—shifted the focus entirely. Instead of asking "how fast can we go?", the question became "how small can we run?" This is a fundamentally different engineering question, one that requires systematic measurement rather than optimization.

The Message: Launching the Low-Memory Sweep

Message 3282 is the assistant's response to that user request. It begins with the daemon already running at pw=1, gw=1—the most memory-conservative configuration imaginable. The assistant then orchestrates a carefully structured benchmark:

Daemon is up with pw=1 gw=1. Now run 5 proofs with concurrency 5 (j=5) to get steady-state measurements. I'll use a script that monitors RSS in the background:

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```bash # Start RSS monitoring in background DAEMON_PID=$(pgrep -f "cuzk-daemon --config /tmp/cuzk-lowmem-pw1-gw1.toml") echo "Monitoring PID: $DAEMON_PID"

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# Monitor peak RSS every 2 seconds (while kill -0 $DAEMON_PID 2>/dev/null; do rss_kb=$(cat /proc/$DAEMON_PID/status 2>/dev/null | grep VmRSS | awk '{print $2}') if [ -n "$rss_kb" ]; then echo "$(date +%H:%M:%S) RSS: $((rss_kb / 1048576)) GiB ($rss_kb kB)" fi sleep 2 done) > /tmp/cuzk-rss-pw1-gw1.log 2>&1 & RSS_MON_PID=$! echo "RSS monitor PID: $RSS_MON_PID"

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# Run benchmark: 5 proofs, concurrency 5 /home/theuser/curio/extern/cuzk/target/release/cuzk-bench batch --type porep --c1 /data/32gbench/c1.json --count 5 --concurrency 5 2>&1 | tee /tmp/cuzk-bench-pw1-gw1.txt

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# Stop RSS monitor kill $RSS_MON_PID 2>/dev/null

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# Show peak RSS echo "" echo "=== Peak RSS ===" sort -t: -k2 -n /tmp/cuzk-rss-pw1-gw1.log | tail -3 ```

The message then shows the benchmark beginning to stream results. The first proof completes in 295.4s total with a prove time of 33,177ms (approximately 33 seconds). The second completes at 584.4s (prove=32,966ms). The third at 876.1s (prove=33,317ms). The queue times grow from 260ms to 715ms to 1,154ms, indicating increasing serialization pressure.

Why This Message Matters

This message is significant for several reasons. First, it represents the first data point in what will become a comprehensive characterization of the engine's memory scaling behavior. The assistant has chosen pw=1 as the starting point—the absolute minimum number of partition workers—to establish the baseline memory footprint. This is a deliberate experimental design choice: start with the extreme case and work upward.

Second, the message reveals the assistant's methodological approach. The RSS monitoring script runs in the background, sampling every 2 seconds, and logs to a separate file. The benchmark output is captured via tee to both stdout and a file. After the benchmark completes, the monitor is killed and the peak RSS is displayed. This is careful, reproducible measurement—the hallmark of systematic engineering.

Third, the choice of 5 proofs at concurrency 5 is itself an interesting decision. The assistant wants "steady-state measurements," meaning it needs enough proofs to reach thermal equilibrium in the system. With concurrency 5, up to 5 proofs can be in-flight simultaneously. But with only 1 partition worker, the engine can only process one partition at a time, creating a natural serialization bottleneck. The growing queue times confirm this.

Assumptions and Decisions

Several assumptions are embedded in this message. The assistant assumes that pw=1, gw=1 will produce the lowest possible memory footprint—a reasonable starting hypothesis. It assumes that 5 proofs at concurrency 5 is sufficient to reach steady state. It assumes that RSS monitoring via /proc/PID/status provides accurate memory measurements (it does, though it measures total process RSS rather than just the cuzk engine's allocations).

The assistant also assumes that the daemon is properly configured and ready. The earlier message (3281) confirmed that the SRS (Structured Reference String) loaded successfully—a 44 GiB file that must be resident in memory for proof generation. This is a fixed cost that every configuration must pay.

One notable decision is the use of gw=1 (one GPU worker per device) rather than the production default of gw=2. This reduces GPU-side in-flight memory at the cost of potentially lower GPU utilization. For a low-memory characterization, this tradeoff makes sense.

The Results Begin to Speak

The benchmark results streaming in the message tell an immediate story. At pw=1, each proof takes approximately 33 seconds of actual prove time. But the total wall-clock time for 5 proofs at concurrency 5 is 295 seconds for the first proof, 584 for the second, 876 for the third—approximately 290 seconds between completions. This is far slower than the 33-second prove time would suggest.

The reason is clear from the queue times: 260ms, 715ms, 1,154ms. These grow because with only 1 partition worker, the engine serializes the partition processing. Even though multiple proofs can be in-flight (concurrency 5), they all contend for the single partition worker. The effective throughput is approximately 1 proof every 290 seconds, or about 0.0034 proofs per second—far below the theoretical maximum.

This is not a bug; it's a deliberate tradeoff. The user asked to see "how low we can get RSS requirements." The answer, as this first data point shows, is that pw=1 achieves very low memory but at a catastrophic throughput cost. The assistant is systematically mapping this tradeoff curve.

The Thinking Process Visible in the Message

The assistant's reasoning is visible in the structure of the command. It doesn't just run the benchmark; it sets up monitoring first, runs the benchmark, then cleans up and reports. This reveals a methodical mindset: measure before and after, capture all data, and present the summary.

The choice of --concurrency 5 with --count 5 is also revealing. The assistant wants to see how the system behaves under load, not just single-proof performance. By running 5 proofs concurrently, it can observe queuing behavior, memory accumulation, and throughput degradation—all critical for understanding real-world deployment characteristics.

The RSS monitoring at 2-second intervals is a pragmatic choice. Memory allocation in the cuzk engine happens in bursts (partition allocation, SRS loading, GPU buffer allocation), so sampling every 2 seconds provides reasonable coverage without generating excessive data.

Input and Output Knowledge

To fully understand this message, one needs input knowledge of: the cuzk engine architecture (partition workers, GPU workers, the split API), the benchmark tooling (cuzk-bench batch), the configuration system (TOML files, partition_workers, gpu_workers_per_device), and the memory monitoring approach (Linux /proc filesystem).

The message creates output knowledge: the first data point in the low-memory characterization. This single run establishes that pw=1 gw=1 achieves approximately 33s per proof but with severe serialization overhead at concurrency 5. The peak RSS value (not yet shown in the message, as the benchmark is still running) will be captured and analyzed later.

Broader Significance

This message, while seemingly mundane, is the foundation of the deployment guidance that will emerge from this session. The assistant is building a systematic understanding of the engine's behavior across the configuration space. The results from this sweep will directly inform recommendations for system sizing: "For a 128 GiB system, use pw=2 gw=1 achieving 152s/proof. For a 256 GiB system, use pw=7 gw=1 achieving 52s/proof."

Without this message—without this first careful measurement—those recommendations would be guesswork. The assistant is doing the hard work of empirical characterization, running real benchmarks on real hardware, and letting the data speak. This is engineering at its most fundamental: measure, understand, then optimize.

The message also demonstrates a crucial principle of systems engineering: optimization for one metric (throughput) often conflicts with optimization for another (memory). The Phase 12 work optimized for throughput at pw=12, achieving 37.7s/proof at ~400 GiB. But the user's question reveals a different need: smaller systems with less memory. The assistant's response—a systematic sweep across the configuration space—is the correct engineering answer to this question.