The First Proof: Validating Cgroup-Aware Memory Budgeting Under Real-World Load

Message Overview

The subject message (msg id=3960) is a brief but momentous status report from the assistant, delivered after a long chain of debugging, coding, and deployment. It reads:

First proof completed successfully: - Total: 201s (3.3 min) — first proof is always slow (SRS loading, PCE extraction) - Status: COMPLETED with valid proof bytes - RSS: 666 GiB — well within 961 GiB cgroup limit - No OOM

The benchmark has moved to phase 1 warmup (5 warmup proofs at concurrency 4). The system is stable and running correctly with cgroup-aware memory budgeting.

[todowrite] {"todos":[{"content":"Commit outstanding changes (docker scripts, memcheck, vast-manager, config.rs)","priority":"high","status":"completed"},{"content":"Fix detect_system_memory() in Rust to be cgroup-aware natively","priority":"high","status":"completed"},{"content":"Build new Docker image with cgroup-aware detect_system_memory()","priority":"high","status":"completed"},{"content":"Fix memcheck.sh GPU JSON parsing (nvidia-smi split on spaces)","priority":"high","status":"completed"},{"content":"Fix memcheck.sh pinning detection (ulimit -l vs nvidia-smi)","priority":"high","status":"completed"},{"content":"Fix entrypoint.sh jq parse error handling","priority":"high","status":"completed"},{"content":"Deploy and verify on vast.ai instances","priority":"high","status":"completed"}]}

On the surface, this is a simple success notification. But in the full context of the session — spanning multiple segments of debugging memory-constrained GPU proving on vast.ai instances — this message represents the first concrete evidence that an entire stack of fixes is working correctly under real production load. The 201-second proof time, the 666 GiB RSS figure, and the absence of an OOM kill are not just metrics; they are the culmination of a deep investigation into cgroup memory limits, CUDA pinned memory pool accounting, kernel/driver overhead, and the subtle ways that Docker containers report memory differently than the host.

Why This Message Was Written: The Culmination of a Debugging Odyssey

To understand why this message exists, one must trace the arc of the preceding work. The broader session (segment 29) was focused on solving a persistent problem: GPU proving instances running inside Docker containers on vast.ai were being killed by the Out-of-Memory (OOM) killer, even though the host machine appeared to have abundant RAM. The root cause was subtle. Inside a Docker container, /proc/meminfo reports the host's total RAM, not the container's cgroup-imposed limit. The Rust detect_system_memory() function was reading this file, obtaining (for example) 2003 GiB on a host whose container was actually limited to 961 GiB by the cgroup. The memory budget system would then allocate as though nearly 2 TiB were available, and the kernel would summarily OOM-kill the process when it exceeded the actual 961 GiB limit.

The assistant and user had already implemented a multi-layered solution. The memcheck.sh shell script was rewritten to detect cgroup v1 and v2 limits and report the effective (minimum of host RAM and cgroup limit) total. The entrypoint.sh was hardened to pass this budget to the cuzk daemon. The Rust detect_system_memory() function was itself made cgroup-aware as a native fallback. Two bugs in memcheck.sh were fixed during live testing: GPU names like "NVIDIA GeForce RTX 4090" were being split on spaces by an overly aggressive IFS=', ' in a bash loop, producing broken JSON; and the pinning detection was incorrectly flagging low ulimit -l as a failure, even though CUDA's cudaHostAlloc bypasses RLIMIT_MEMLOCK via the NVIDIA kernel driver.

The Docker image was rebuilt and pushed, then deployed to two real vast.ai instances. One of them — a 961 GiB cgroup-limited machine with an RTX 4090 — had successfully started its benchmark after downloading ~100 GiB of proof parameters. The subject message is the first status report after that benchmark began producing proofs. It answers the single most important question: does it actually work?

The Significance of the Metrics

The three numbers in the message — 201 seconds, 666 GiB RSS, and "No OOM" — each tell a different part of the story.

201 seconds (3.3 minutes) for the first proof is described as "always slow" due to SRS loading and PCE extraction. This is an important piece of domain knowledge. The Structured Reference String (SRS) is a large cryptographic parameter file (44 GiB in this deployment) that must be loaded into memory before any proving can begin. Pre-Compiled Constraint Evaluator (PCE) extraction is a one-time circuit analysis step. The fact that the first proof completed in 201 seconds, rather than crashing or hanging, confirms that these initialization steps completed successfully within the memory budget. In earlier failed runs, the process would be OOM-killed during SRS loading because it tried to allocate more than the cgroup limit.

666 GiB RSS is the most revealing number. The cgroup limit was 961 GiB (1032768716800 bytes), and the memory budget was set to 951 GiB (961 GiB minus a 10 GiB safety margin). An RSS of 666 GiB means the process is using about 70% of its budget — comfortably within limits, with 285 GiB of headroom. This is a stark contrast to the previous behavior, where the system would attempt to allocate based on the host's 2003 GiB and quickly exceed the 961 GiB cgroup limit. The 666 GiB figure also validates that the PinnedPool (CUDA pinned memory pool) is operating within budget, one of the key concerns from earlier debugging.

No OOM is the bottom-line validation. The entire multi-day effort — from the cgroup-aware memory detection in Rust, to the memcheck.sh GPU JSON fix, to the entrypoint hardening, to the Docker rebuild — was aimed at preventing OOM kills. This single message confirms that the stack works.

Assumptions and Decisions Embedded in the Message

The message makes several implicit assumptions that are worth examining. First, it assumes that the first proof completing successfully is a reliable indicator that subsequent proofs will also succeed. This is a reasonable assumption for a deterministic proving system, but the assistant hedges by noting that the benchmark has moved to "phase 1 warmup (5 warmup proofs at concurrency 4)." The warmup phase is explicitly designed to stress-test the system before the timed benchmark phase begins.

Second, the message assumes that the 10 GiB safety margin (951 GiB budget vs 961 GiB cgroup limit) is sufficient. This assumption was empirically tested in the companion investigation on the 342 GiB instance (chunk 1 of this segment), where a memprobe utility was developed to measure kernel/driver overhead empirically. The 10 GiB margin was found to be tight but adequate on the 961 GiB instance, though the 342 GiB instance required a more adaptive approach with OOM recovery loops and budget reduction.

Third, the message treats "RSS: 666 GiB" as a static measurement, but RSS is inherently dynamic in a GPU proving system. The PinnedPool returns buffers to a pool after GPU work completes, but those buffers remain in RSS until the pool decides to release them. The assistant's confidence that "the system is stable" relies on the PinnedPool being integrated with the MemoryBudget system — a fix that was itself the subject of extensive debugging in earlier segments.

Input Knowledge Required

To fully understand this message, a reader needs knowledge of several domains:

Cgroup memory accounting: Docker containers use Linux cgroups to limit memory. Inside the container, /proc/meminfo shows host RAM, not the cgroup limit. The effective memory available is min(host_ram, cgroup_limit). This is the core insight that drove the entire fix.
CUDA pinned memory: cudaHostAlloc allocates page-locked (pinned) host memory that the GPU can access directly via DMA. This memory is not subject to RLIMIT_MEMLOCK because it goes through the NVIDIA kernel driver, which is why the ulimit-based pinning detection was wrong.
Filecoin proving pipeline: The proof system uses large parameters (SRS, PCE) that must be loaded into memory. The first proof is slow because it includes one-time initialization steps.
The benchmark structure: The benchmark runs in phases — warmup (5 proofs), timed (multiple proofs), and cooldown. The message reports entering phase 1 (warmup).

Output Knowledge Created

This message creates several important pieces of knowledge:

Empirical validation that the cgroup-aware memory budgeting fix works on a real 961 GiB vast.ai instance with an RTX 4090. This is not a unit test or a simulation — it is a production-like benchmark run.
A baseline for first-proof time (201 seconds) on this hardware configuration. This can be used for performance regression testing and capacity planning.
Confirmation that the 10 GiB safety margin is adequate for this instance size. The 666 GiB RSS leaves 285 GiB of headroom, suggesting the margin could even be reduced.
A reference RSS figure (666 GiB) for a 10-proof benchmark at concurrency 4 on a 961 GiB instance. This helps predict memory requirements for similar deployments.
Validation of the entire deployment pipeline: memcheck.sh → entrypoint.sh → cuzk-daemon with --budget → benchmark.sh. Each component passed its output correctly to the next.

The Thinking Process Visible in the Message

The message itself is terse — it is a status report, not a reasoning trace. But the thinking process is visible in what is not said. The assistant does not express surprise or relief; it reports the results as expected outcomes. This indicates that the assistant had high confidence that the fixes would work, based on the systematic debugging that preceded this moment.

The inclusion of the todowrite block with all items marked "completed" is a deliberate organizational choice. It signals that this message is a milestone — the point at which the assistant considers the current workstream finished and is ready to move on. The todos cover the entire chain: committing changes, fixing the Rust code, building the Docker image, fixing memcheck.sh bugs, fixing the entrypoint, and deploying. All are checked off.

The assistant's choice to report RSS alongside the cgroup limit (666 GiB vs 961 GiB) reveals an awareness that the reader needs to see the margin. It is not enough to say "proof completed" — the assistant knows that the critical question is whether the memory budget is being respected, and it provides the evidence directly.

Mistakes and Limitations

While the message is a success report, it is worth noting what it does not address. The 342 GiB instance (the other deployment target) was still experiencing issues — its benchmark failed with a transport error, and the memprobe investigation revealed that it was operating at 99% of its cgroup limit with only 6 GiB of kernel/driver overhead. The 10 GiB safety margin that works on the 961 GiB instance would be catastrophically insufficient on the 342 GiB instance. The assistant does not mention this in the subject message, but the broader segment context shows that an OOM recovery loop and adaptive budget reduction were being developed precisely for this scenario.

Additionally, the message assumes that the first proof completing is representative of all proofs. In practice, different proof types (WinningPoSt, WindowPoSt, SnapDeals) have different memory profiles, and the benchmark may not exercise all of them. The assistant's earlier work on PCE extraction (segment 0) showed that enabling PCE for WindowPoSt caused a crash due to is_extensible() mismatches — a class of bug that could reappear under different proof types.

Conclusion

Message 3960 is the payoff for a long debugging arc. It is the moment when theory meets practice — when the cgroup-aware memory budget, the fixed memcheck.sh, the hardened entrypoint, and the rebuilt Docker image all converge to produce a correct proof on a real machine under real constraints. The 201-second proof time, the 666 GiB RSS, and the absence of OOM are not just numbers; they are evidence that a complex, multi-layered fix is working end-to-end. For anyone who has followed the session from the beginning, this message is the answer to the question that motivated all the work: can we make this system run reliably on memory-constrained vast.ai instances? The answer, at least for this 961 GiB instance, is yes.