Empirical Validation: The Memprobe Utility Exposes Hidden Kernel Overhead on a Live Vast.ai Instance

Introduction

In the ongoing battle to stabilize the CuZK proving engine on memory-constrained vast.ai GPU instances, message [msg 4022] represents a pivotal moment of empirical validation. After weeks of chasing OOM (Out of Memory) crashes, budget accounting discrepancies, and mysterious kernel-level overhead, the assistant deploys a custom-built memprobe utility to a live 256 GiB instance and captures, in a single JSON blob, the stark numerical truth of the problem. The message is deceptively simple—a single bash command that copies a binary and executes it—but the data it returns fundamentally reshapes the team's understanding of memory constraints in containerized GPU environments.

Context: The OOM Investigation

The broader investigation, spanning segments 28 and 29 of the coding session, had already uncovered several layers of the OOM problem. The team had discovered that the CUDA pinned memory pool (PinnedPool) was operating outside the MemoryBudget system, meaning pinned buffers returned to the pool after GPU work were never freed from actual RSS. They had implemented cgroup-aware memory detection in detect_system_memory(), replacing the naive /proc/meminfo reading that reported host RAM inside Docker containers. They had fixed bugs in memcheck.sh related to GPU JSON parsing and pinning detection. They had added a safety margin increase to 10 GiB.

Yet the 342 GiB cgroup-limited instance (RTX 5090, 503 GiB host) continued to crash. The 10 GiB safety margin was empirically insufficient. Something was consuming memory invisibly—something that standard tools like free, /proc/meminfo, and even cgroup memory accounting couldn't fully capture.

The assistant and user hypothesized that the missing memory was consumed by kernel/driver overhead: glibc arenas, page tables, GPU driver allocations, and the transient SRS (Structured Reference String) loading spike that simultaneously triggered mmap and cudaHostAlloc. But this was just a hypothesis. They needed data.

The Memprobe Utility

The response to this need was memprobe.c, a tiny C program written in [msg 4000] that allocates 1 GiB chunks via mmap followed by memset until it nears the cgroup limit. The design is elegant in its simplicity: rather than trying to parse kernel memory accounting or interrogate /proc filesystem entries, it simply probes the available memory by allocating until it can't anymore. This is the same approach a stress-testing tool like stress would use, but with careful instrumentation to measure the gap between what the cgroup says is available and what can actually be allocated.

The program reports several key metrics:

The Subject Message: Deployment to a Live Instance

In [msg 4022], the assistant executes:

scp -o StrictHostKeyChecking=no -P 17008 /tmp/memprobe root@ssh6.vast.ai:/usr/local/bin/memprobe 2>&1 && ssh -o StrictHostKeyChecking=no -p 17008 root@ssh6.vast.ai "chmod +x /usr/local/bin/memprobe && /usr/local/bin/memprobe" 2>&1

This command does two things. First, it copies the memprobe binary (extracted from the Docker image in [msg 4021]) to the remote instance at ssh6.vast.ai on port 17008. Second, it makes the binary executable and runs it.

The result is a single JSON object:

{
  "cgroup_limit_bytes": 367003697152,
  "usage_before_bytes": 342817820672,
  "allocated_bytes": 15032385536,
  "allocated_gib": 14,
  "usage_peak_bytes": 364346327040,
  "usage_after_bytes": 350478913536,
  "overhead_bytes": 6496120832,
  "overhead_gib": 6.0,
  "probe_chunks": 14
}

What the Data Reveals

This JSON output is a bombshell. Let's parse what it means.

The cgroup limit is 367,003,697,152 bytes (approximately 342 GiB—the cgroup_limit_bytes value is the raw cgroup v2 memory.max). Before probing, the instance was already using 342,817,820,672 bytes—that's 99.9% of the cgroup limit. The instance was operating with essentially zero headroom.

The probe successfully allocated 14 GiB (15,032,385,536 bytes) before stopping. During the probe, peak usage hit 364,346,327,040 bytes—still within the cgroup limit, but only barely. After freeing the allocations, usage settled at 350,478,913,536 bytes, which is actually higher than before the probe. This is likely because the kernel caches some of the freed pages or because the probe's memory allocations perturbed the system in ways that prevented full reclamation.

The most critical number is overhead_bytes: 6,496,120,832—approximately 6 GiB. This is the gap between what the cgroup limit suggests should be available and what can actually be allocated. It represents kernel overhead that is invisible to standard memory accounting tools but consumes real cgroup budget.

Why This Message Matters

The message is significant for several reasons.

First, it validates the hypothesis. The team had suspected that kernel/driver overhead was consuming memory invisibly. The memprobe output provides direct empirical evidence: 6 GiB of overhead on a 342 GiB cgroup-limited instance. This explains why the 10 GiB safety margin was insufficient—the kernel overhead alone consumed 6 GiB of that margin, leaving only 4 GiB for the actual workload's peak memory needs, which was clearly not enough.

Second, it demonstrates the power of empirical measurement over theoretical analysis. The team could have spent days trying to parse kernel memory accounting structures, interrogate slab allocator statistics, or trace driver memory allocations. Instead, they built a simple tool that asks the question directly: "How much memory can I actually allocate?" The answer is unambiguous.

Third, it provides a data-driven foundation for the adaptive safety margin strategy. The memprobe output becomes the basis for a more intelligent budgeting approach: instead of using a fixed safety margin (which was proven insufficient), the entrypoint script can run memprobe at startup and derive a safety margin that accounts for the actual kernel overhead on that specific instance. This is critical because kernel overhead varies by GPU model, driver version, kernel version, and even workload characteristics.

Fourth, it reveals the severity of the memory pressure. The instance was running at 99% of its cgroup limit before the probe. This means the benchmark was operating on a knife's edge—any transient spike (like the SRS loading that simultaneously triggers mmap and cudaHostAlloc) could push it over the limit and trigger an OOM kill. The 14 GiB of allocatable space was the only buffer, and 6 GiB of that was eaten by kernel overhead before any application memory was even touched.

Assumptions and Their Validation

The message implicitly tests several assumptions that had been made during the investigation.

Assumption 1: Kernel overhead is significant. This was a hypothesis based on the observation that the 10 GiB safety margin was insufficient. The memprobe output confirms it: 6 GiB of overhead is indeed significant.

Assumption 2: The overhead is relatively stable and measurable. The memprobe approach assumes that by allocating memory until near the limit, you can measure the overhead as the difference between the cgroup limit and what can actually be allocated. This assumes that the probe itself doesn't distort the measurement (e.g., by triggering additional kernel allocations that wouldn't normally be present). The output suggests this is reasonable—the overhead measurement is consistent with the observed crash behavior.

Assumption 3: mmap/memset is a valid proxy for real workload memory allocation. The memprobe utility uses mmap followed by memset to allocate and fault in pages. This is a reasonable proxy for the kind of memory allocation the CuZK proving engine performs, but it may not capture all sources of overhead. GPU driver allocations, for example, may use different allocation paths. The 6 GiB overhead figure should be treated as a lower bound—the real overhead during GPU proving could be higher.

Input Knowledge Required

To fully understand this message, one needs:

  1. The cgroup memory model: Understanding that Docker containers run under cgroup v2 (or v1) memory limits, and that /proc/meminfo reports host RAM rather than the container's limit. This was the fundamental bug that led to massive over-allocation.
  2. The vast.ai environment: Vast.ai is a decentralized GPU rental marketplace where instances are Docker containers with cgroup memory limits. The host machine may have much more RAM than the container's limit, making naive memory detection dangerous.
  3. The CuZK proving engine architecture: Understanding that the proving process uses pinned memory (via cudaHostAlloc) for GPU data transfer, and that this memory is managed by a PinnedPool that was not properly integrated with the MemoryBudget system.
  4. The OOM crash symptoms: The 342 GiB instance was crashing with OOM kills during GPU processing, despite the 10 GiB safety margin. The crashes were intermittent and workload-dependent.
  5. The memprobe tool design: Understanding that memprobe uses mmap/memset to allocate 1 GiB chunks, stopping 2 GiB before the cgroup limit to avoid triggering an OOM kill itself.

Output Knowledge Created

This message creates several pieces of critical knowledge:

  1. Empirical overhead measurement: 6 GiB of kernel/driver overhead on a 342 GiB cgroup-limited instance with an RTX 5090 GPU.
  2. Validation of the adaptive safety margin approach: The data confirms that a fixed safety margin is insufficient and that a data-driven approach (using memprobe at startup) is necessary.
  3. Baseline for the OOM recovery loop: The benchmark.sh script (updated in [msg 4009]) implements an OOM recovery loop that reduces the budget by 10% on each retry. The memprobe data provides context for why this loop is necessary and what budget reductions might be effective.
  4. Confirmation of the memory pressure severity: The instance was operating at 99% of its cgroup limit, with only 14 GiB of headroom (of which 6 GiB was kernel overhead). This explains why the benchmark was surviving but with zero safety margin.

The Thinking Process

The message reveals a methodical, data-driven approach to debugging. The assistant does not speculate about the overhead magnitude—it builds a tool to measure it directly. The decision to run memprobe on a live instance (rather than in a test environment) is significant: it captures the real system state, including whatever memory pressure the benchmark had already created.

The choice of scp followed by ssh rather than using the Docker image's built-in memprobe (which was already pushed in [msg 4018]) is pragmatic. The instance is already running a benchmark with the old scripts. Rather than restarting the container (which would interrupt the benchmark), the assistant deploys the binary directly to the running instance's filesystem.

The output format—a single JSON line—is designed for programmatic consumption. The entrypoint.sh script (updated in [msg 4012]) can parse this output and use it for budget calculation. This is consistent with the overall design philosophy of the deployment infrastructure: tools produce machine-readable output that can be composed into larger automation pipelines.

Conclusion

Message [msg 4022] is a masterclass in empirical debugging. Faced with a complex, multi-layered memory management problem involving cgroup limits, GPU driver allocations, kernel overhead, and application-level memory pools, the assistant builds a simple, focused tool that cuts through the complexity and produces a single, unambiguous measurement. The 6 GiB overhead figure becomes the cornerstone of the adaptive safety margin strategy, transforming the OOM recovery loop from a guess-based heuristic into a data-driven algorithm. For anyone debugging memory issues in containerized GPU environments, this message demonstrates the power of asking the right question directly, rather than trying to infer the answer from indirect measurements.