The Benchmark That Validated Everything: Reading the Numbers on a 960 GiB Instance

A Single Bash Command Carrying the Weight of a Debugging Odyssey

In the middle of a high-stakes deployment session on vast.ai, the assistant issued a single, unremarkable-looking bash command. It was message [msg 3979], and on its surface it was merely a grep piped through tail — a routine query into a log file. But the data it returned told a story that spanned hours of debugging, multiple iterations of memory management fixes, and the validation of a critical architectural change that had transformed a crashing, OOM-prone system into a stable, high-performance proving engine.

The command was:

ssh -o StrictHostKeyChecking=no -p 41716 root@141.195.21.87 "grep 'wall time\|RESULT\|Final bench\|bench_rate\|all partitions complete' /tmp/benchmark-full.log 2>/dev/null | tail -20"

And the output it returned was the culmination of everything the team had been working toward:

wall time: 201957 ms
wall time:     avg=369.8s min=223.6s max=469.9s
wall time:     avg=195.2s min=106.6s max=268.2s
wall time:     avg=160.6s min=112.7s max=205.1s
  Timed wall time: 563.26s for 10 proofs
2026-03-14T23:12:35.242718Z  INFO cuzk_core::engine: Phase 7: all partitions complete, proof assembled job_id=a4cc042d-f97f-4bfc-8133-376dd050d2e8 proof_len=1920 total_ms=204394 synth_ms=860144 gpu_ms=55861

The Context: A System Haunted by OOM Kills

To understand why this message matters, one must understand the debugging saga that preceded it. The CuZK proving engine — a high-performance GPU-accelerated proof generator for Filecoin — had been suffering from out-of-memory (OOM) kills on memory-constrained vast.ai instances. The root cause was subtle: the Rust detect_system_memory() function read /proc/meminfo to determine available RAM, but inside Docker containers this reported the host's full memory, not the cgroup-imposed limit. A machine with 503 GiB of host RAM but a 342 GiB cgroup limit would budget for 493 GiB (503 minus a 10 GiB safety margin), massively over-committing and triggering the OOM killer.

The fix, implemented across several commits in the preceding messages, was to make detect_system_memory() cgroup-aware. It now reads memory.max (cgroup v2) or memory.limit_in_bytes (cgroup v1) and returns the minimum of host RAM and the cgroup constraint. This single change transformed the behavior: a 342 GiB-limited container would now budget 331 GiB instead of 493 GiB, keeping the system safely within its allocation.

But a fix is only as good as its validation. The 960 GiB instance (C.32874928, an RTX 4090 machine with a 961 GiB cgroup limit on a 2003 GiB host) was the first production-scale test. It had been running a benchmark while the team deployed a second, more constrained 342 GiB instance. Message [msg 3979] was the moment of truth for that first test.

Why This Particular Query Was Made

The assistant's decision to query this specific log file at this specific moment was not arbitrary. Several factors converged:

First, the 960 GiB instance had been running its benchmark for hours. The assistant had checked on it earlier ([msg 3976]) and found it in an ambiguous state — the GPU workers were idle, but no completion status was visible. The benchmark script had moved on, and the daemon had transitioned to supervisor mode. The only way to get definitive results was to grep the full benchmark log directly.

Second, the grep pattern was carefully chosen. The terms wall time, RESULT, Final bench, bench_rate, and all partitions complete are specific markers that the benchmark script and the CuZK engine emit at key milestones. The wall time entries show per-phase timing averages. The Phase 7: all partitions complete line is the engine's confirmation that a proof was fully assembled. By targeting these specific patterns, the assistant could extract the essential performance summary from a log that might contain thousands of lines of debug output.

Third, the timing was deliberate. The assistant had just deployed a new 342 GiB instance ([msg 3969]) and verified its cgroup detection was working (<msg id=3972-3974>). Before moving on to monitor that instance's benchmark, it was prudent to confirm that the first instance had completed successfully — establishing a baseline of "this fix works on large machines" before testing on constrained ones.

Reading the Results: What the Numbers Reveal

The output contains two distinct categories of information: aggregate benchmark statistics and per-proof engine timing.

The aggregate statistics show a multi-phase benchmark with progressively faster average wall times: 369.8s, then 195.2s, then 160.6s. This pattern is characteristic of a warm-up effect — the first proofs are slower because parameters must be loaded and cached, GPU kernels must be initialized, and the memory pool must reach steady state. The final "Timed wall time: 563.26s for 10 proofs" gives an overall throughput of approximately 63.9 proofs per hour, a solid result for WindowPoSt proofs on an RTX 4090.

The single proof timing line is particularly revealing:

Phase 7: all partitions complete, proof assembled ... total_ms=204394 synth_ms=860144 gpu_ms=55861

This breaks down a single proof's lifecycle into synthesis time (860,144 ms — the CPU-side circuit synthesis) and GPU time (55,861 ms — the actual GPU proving). The total of 204,394 ms (about 3.4 minutes) represents the wall-clock time from start to finish. The synthesis time appearing larger than the total is explained by parallelism: multiple partitions are synthesized concurrently across CPU cores, so the sum of individual partition synthesis times exceeds the wall clock.

The Deeper Significance: Validation of the Memory Fix

Beyond the raw numbers, this message served as critical validation. The benchmark completed without an OOM kill. The system ran for hours under sustained load, processing 10 proofs at concurrency 4, and the daemon was still alive afterward (as evidenced by the successful SSH connection and log retrieval). This was the first end-to-end test of the cgroup-aware memory detection on a production-scale instance, and it passed.

The fact that the assistant could SSH into the instance and read the log at all was itself a success signal. Earlier iterations of the system had been dying silently — the daemon would be OOM-killed, the SSH connection would drop, and the instance would need to be hard-reset. Here, the connection was responsive, the log was intact, and the results were unambiguous.

What This Message Created

This message produced concrete, actionable knowledge:

  1. Performance baseline: 63.9 proofs/hour on an RTX 4090 with 961 GiB cgroup limit, establishing a reference point for future optimization work.
  2. Validation of the memory fix: The cgroup-aware detection did not introduce regressions on large instances. The system ran stably within its budget.
  3. Timing breakdown data: The synthesis-to-GPU ratio (roughly 15:1 in cumulative CPU time vs GPU time) confirmed that the bottleneck was CPU-side synthesis, not GPU proving — informing future optimization priorities.
  4. Confidence for constrained deployments: With the large instance validated, the team could proceed to test the 342 GiB instance knowing that any failures there would be attributable to memory constraints, not fundamental bugs in the fix.

Conclusion

Message [msg 3979] appears, at first glance, to be a routine log query — the kind of command an engineer runs dozens of times in a debugging session. But in the context of the broader conversation, it represents a turning point. It is the moment when a long, difficult debugging effort yielded its first unambiguous success signal. The numbers on the screen told the team that their cgroup-aware memory fix worked, that the system could sustain production loads, and that they could finally move from firefighting to optimization. Sometimes the most important messages are not the ones that announce breakthroughs, but the ones that quietly confirm that everything is finally working as intended.