Pushing a Band-Aid: The Docker Deploy That Exposed a Deeper Memory Accounting Problem

On the surface, message <msg id=4172> is unremarkable — a routine Docker push command that uploads a freshly built image to a registry. The assistant runs:

docker push theuser/curio-cuzk:latest 2>&1 | tail -10

and receives back a list of layer digests, three of which were newly pushed, culminating in a final digest: sha256:2c8b5507eb2caaaa446c6f7da5633e0200ce8c35e55253646293575e666cbc9f. The image is now available for deployment. But this mundane command is the culmination of a much deeper story — one that reveals the tension between shipping a quick fix and properly solving a fundamental architectural flaw in a high-performance GPU proving engine.

The Crash That Started It All

The immediate context for this Docker push is a catastrophic OOM (Out of Memory) crash on an RTX 5090 vast.ai instance (C.32897009). During a benchmark's timed phase, the entire container was killed by the cgroup OOM killer. The initial investigation was complicated by a subtle bash script bug in benchmark.sh (line 346) where a set -euo pipefail interaction with an if ! cmd | tee pipeline pattern caused exit codes to be masked. The assistant had already fixed that bug in a previous round, but the underlying memory pressure remained.

The root cause was unbounded growth in the pinned memory pool — a pool of CUDA-pinned buffers used for fast host-to-device transfers during GPU proving. Each synthesis partition allocates three large buffers (a, b, c) totaling roughly 11.6 GiB. These buffers are checked out from the pool, used during synthesis and GPU proving, then checked back in. The problem is that once checked in, they remain in the pool indefinitely, accumulating over time. On a machine with a 342 GiB cgroup limit, enough concurrent partitions could cause the pool to grow until the system ran out of memory entirely.

The User's Principled Objection

The assistant's first instinct was a quick fix: cap the pool at 30 buffers (~116 GiB). But the user rejected this approach, pointing out that it was "unprincipled" and would "catastrophically harm performance on memory-constrained systems where pinned memory is the dominant operational memory." The user insisted that the pinned pool and memory manager must be properly integrated to collaborate on memory management, avoiding thrashing while maximizing parallelism.

This forced the assistant to abandon the ad-hoc cap and undertake a deeper architectural analysis.

From Buffer Count to Byte-Based Budget Cap

The assistant redesigned the approach in several iterations. First, the pool's cap was changed from a buffer count to a byte-based max_bytes limit (<msg id=4157> through <msg id=4161>). This required updating the PinnedPool struct, the checkout and checkin methods, and the status reporting in status.rs (<msg id=4162>–<msg id=4163>).

Then the critical question arose: how to compute max_bytes? The assistant's initial proposal was 60% of the memory budget, but a detailed analysis in <msg id=4169> revealed the deeper problem. The assistant traced through the memory accounting for a 342 GiB machine:

"The issue was that pinned buffers PERSIST in the pool after the partition completes and its budget reservation is released. So: 1. Partition starts → budget reserves 14 GiB → pinned pool allocates 11.6 GiB (within that 14 GiB). 2. Partition completes → budget releases 14 GiB → but pinned pool holds 11.6 GiB. 3. Net: budget says 14 GiB freed, but only 2.4 GiB actually freed."

This is the fundamental accounting mismatch: the pinned pool's cudaHostAlloc buffers are invisible to the MemoryBudget. When a partition completes, its per-partition budget reservation is released, but the pool retains the physical pinned memory. This blind spot causes the budget to systematically over-commit, eventually triggering the cgroup OOM killer.

The 40% Heuristic

After running the numbers and finding that 60% could still lead to over-commit, the assistant settled on 40% of the budget as the pool cap. For the 342 GiB machine, this meant a 132 GiB pool cap, which the assistant calculated would allow roughly 11 concurrent pinned partitions before falling back to heap allocation. The detailed trace showed peak real memory of ~318 GiB — under the 342 GiB cgroup limit, but with only 24 GiB of margin.

The assistant also added an escape hatch: on machines with a budget of 500 GiB or more, the pool is left unlimited, preserving current behavior for the large production instances.

What This Push Actually Deploys

The Docker image pushed in <msg id=4172> contains this 40% budget cap. It is a meaningful improvement over the unbounded pool that caused the crash. But it is still a heuristic — a percentage chosen to balance performance and safety across a range of machine sizes. The assistant's own analysis acknowledged that the fix doesn't address the root cause: the budget and the pinned pool remain decoupled, with no mechanism for the pool to communicate its memory usage back to the budget.

The Unresolved Problem

The deeper issue — that the pinned pool's memory is invisible to the budget — remains unsolved. The 40% cap prevents the worst case (unbounded growth), but it creates a new problem: the budget can still allocate up to 261 GiB of "working memory" on top of the 132 GiB pool cap, potentially reaching 393 GiB total — well over the 342 GiB cgroup limit. In practice, the budget's per-partition reservations prevent this because each partition's 14 GiB reservation includes the a/b/c buffers that the pool covers. But the two systems don't know about each other, and the safety margin depends on the specific workload mix.

A proper solution would require the pinned pool to participate in the budget system — either by holding a permanent reservation equal to its current size, or by routing all pool allocations through the budget's reservation mechanism. But that would be a significant refactor, not something to rush into a Docker image at 2 AM after a crash.

Conclusion

Message <msg id=4172> is a Docker push, but it represents a moment of decision in a complex engineering process. The assistant chose to deploy an imperfect but functional fix rather than hold out for a perfect solution. The 40% cap is a compromise — it prevents the immediate crash on the RTX 5090 while preserving performance on large machines, but it leaves the fundamental accounting mismatch unresolved. The image digest sha256:2c8b5507eb2caaaa446c6f7da5633e0200ce8c35e55253646293575e666cbc9f encodes this tension: a fix that works today, knowing that the real solution requires deeper architectural changes tomorrow.