The Integration Point: Compiling the Memory Probe Into the Docker Build

A Single Edit That Bridges Theory and Practice

On the surface, message [msg 4003] appears unremarkable — a terse confirmation that an edit was applied to a Dockerfile:

[assistant] [edit] /tmp/czk/Dockerfile.cuzk Edit applied successfully.

Seven words. No diff shown, no commentary, no fanfare. Yet this message represents the critical integration moment in a multi-hour debugging saga: the point at which a theoretical solution — a C program that empirically measures memory pressure — was welded into the production deployment pipeline. Everything before this message was analysis and implementation; everything after was deployment and validation. This single edit transformed an idea into infrastructure.

The Problem That Demanded a Probe

To understand why this edit matters, one must understand the crisis that precipitated it. The CuZK proving engine was crashing on memory-constrained vast.ai instances — cloud GPU machines where the available RAM was capped by Linux cgroup limits. The system used a MemoryBudget framework to track allocations, but the budget was consistently wrong. The 10 GiB safety margin that seemed generous on paper was proving catastrophically insufficient in practice.

The root cause was a compound failure of accounting. The CUDA pinned memory pool (PinnedPool) was operating outside the budget system entirely — buffers returned to the pool after GPU work were never freed from actual RSS, creating a massive discrepancy between what the budget thought was allocated and what the kernel was actually holding. On top of this, there were hidden overheads that no static calculation could capture: kernel slab caches, page tables for pinned memory, glibc malloc arenas, thread stacks, and GPU driver allocations. On a machine with a 342 GiB cgroup limit, these invisible costs consumed 5–10 GiB that the budget model simply didn't know about.

The user articulated the core insight in [msg 3997]: "The hunch about 10gb being low also sounds right, maybe we should have a program before benchmark which tries to actually allocate memory, and in benchmark runs also recover from OOMs and retry with 50% overhead a few times." This was the seed of the two-pronged strategy: a proactive memory probe to measure actual capacity, and reactive OOM recovery to handle the cases where the probe's estimate was still too optimistic.

The Reasoning Behind the Edit

Message [msg 3999] contains the assistant's extended reasoning, and it reveals a fascinating cognitive journey. The assistant considered multiple approaches before settling on the final design.

First, it analyzed the overhead sources quantitatively. Page tables for 94 GiB of pinned allocations: ~192 MiB. glibc malloc arenas (18 threads × 64 MiB): ~1.1 GiB. Thread stacks: 144 MiB. Kernel slab: 1–2 GiB. GPU driver overhead: unknown but significant. The total came to 3–5 GiB beyond the 10 GiB safety margin — a tight squeeze on a 342 GiB machine.

Second, it evaluated the technical constraints of the runtime environment. The Docker image had no Python, no C compiler, no stress or stress-ng. Any memory probe would need to be compiled during the builder stage and copied into the runtime image. This constraint directly motivated the edit in [msg 4003]: the Dockerfile needed a new compilation step.

Third, it grappled with the probe's inherent limitation: a memory probe that runs before the daemon starts measures only the empty container's capacity. Once the daemon is running, CUDA pinned allocations, GPU driver overhead, and thread stacks consume additional memory that the probe cannot anticipate. The assistant recognized this explicitly: "the probe gives an optimistic baseline that will shrink once actual workloads start." This is why the two-pronged strategy was essential — the probe provides a starting estimate, and the OOM recovery loop provides the adaptive correction when that estimate proves insufficient.

Fourth, it considered the SRS loading path in excruciating detail. The 44 GiB SRS file is mmap'd from disk, then copied into a CUDA pinned allocation via cudaHostAlloc. During the copy, both the mmap and the pinned allocation exist simultaneously, creating a transient peak of ~88 GiB against a budget that only accounts for 44 GiB. The assistant traced through the C++ code, analyzed point sizes for BLS12-381, and considered conditional compilation flags (H_IS_STD__VECTOR) that affect whether the h component is allocated inside or outside the pinned buffer. This deep dive into the SRS internals informed the understanding that the budget model was fundamentally incomplete.

What the Edit Actually Changed

The edit to /tmp/czk/Dockerfile.cuzk added two things to the multi-stage Docker build:

A compilation step in the builder stage: The memprobe.c source file (written in [msg 4000]) is compiled with gcc into a static binary. The compilation uses -static to ensure the binary has no library dependencies, making it portable across different container environments.
A COPY instruction in the runtime stage: The compiled memprobe binary is copied from the builder stage into /usr/local/bin/memprobe in the runtime image, making it available as a system command. The build output in [msg 4017] confirms the result:

#37 [runtime  9/16] COPY --from=builder /build/memprobe  /usr/local/bin/memprobe

The binary was small — 16,400 bytes according to [msg 4021] — a negligible addition to the image size for a potentially life-saving capability.

Assumptions and Decisions

Several key assumptions underpin this edit:

The probe must be compiled, not scripted. The assistant considered writing the probe in bash using tmpfs tricks, but correctly identified that this would measure tmpfs limits, not RSS. A C program using mmap and memset provides a direct measurement of anonymous memory pressure against the cgroup limit.

Static compilation is necessary. The runtime image is minimal; there's no guarantee that shared libraries will be available. A statically-linked binary eliminates dependency risk.

The probe should stop 2 GiB before the cgroup limit. This is a safety margin within the probe itself — it measures how much memory can be allocated without triggering the OOM killer, then reports a conservative estimate.

The probe's result is advisory, not authoritative. The entrypoint script (updated in [msg 4012]) uses the probe's result as an input to the budget calculation, but the OOM recovery loop in benchmark.sh (updated in [msg 4009]) provides the ultimate safety net. If the probe overestimates capacity, the benchmark will crash and retry with a reduced budget.

Input and Output Knowledge

Input knowledge required to understand this edit includes: the structure of multi-stage Docker builds; the memory constraints of vast.ai instances with cgroup v2 limits; the distinction between file-backed mmap pages and anonymous memory; the behavior of the Linux OOM killer under cgroup pressure; and the existing memcheck.sh utility that reads cgroup limits.

Output knowledge created by this edit includes: a reproducible build pipeline for the memprobe utility; a deployment mechanism that ensures the probe is available on every container instance; and the foundation for the adaptive budgeting system that would later be validated on live instances.

The Validation

The edit's impact was confirmed empirically in [msg 4026] (the next chunk), where memprobe was run on a live 256 GiB instance. The results were stark: the machine was operating at 99% of its cgroup limit (340 GiB out of 342 GiB), with only 14 GiB of additional allocatable space and 6 GiB of kernel/driver overhead. The instance was surviving the benchmark but with zero headroom — exactly the scenario the probe was designed to detect. The new adaptive safety margin and OOM recovery logic, built on the foundation of this single Dockerfile edit, were validated as essential for reliable operation on memory-constrained nodes.

Conclusion

Message [msg 4003] is a study in the power of integration. The edit itself is trivial — a few lines added to a build file. But those lines represent the culmination of hours of debugging, analysis of kernel memory accounting, deep dives into CUDA pinned allocation internals, and careful consideration of the runtime environment's constraints. The edit is the bridge between a theoretical understanding of the problem and a practical tool that would go on to prevent OOM crashes on production GPU instances. It is a reminder that in complex systems engineering, the most impactful changes are often the ones that make a good idea actually deployable.