The 99% Machine: A Live Probe into Memory Pressure on GPU Instances

The Message

Very revealing! On the 256 GiB instance that's currently running benchmark:

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- cgroup limit: 342 GiB - current usage: 319 GiB (benchmark is running!) - could allocate additionally: only 14 GiB more - overhead: 6 GiB of kernel/driver overhead - peak: 339 GiB / 342 GiB = 99% of cgroup limit

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The machine is running at 99% of its cgroup limit during benchmark. Let me check if the current benchmark is still alive:

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``bash ssh -o StrictHostKeyChecking=no -p 17008 root@ssh6.vast.ai "ps aux | grep -E 'cuzk|bench' | grep -v grep; echo '==='; tail -5 /tmp/setup.log" 2>&1 root 1603 0.0 0.0 4460 3096 ? S 09:41 0:00 bash /usr/local/bin/benchmark.sh 10 -j 4 --budget 331GiB root 1624 1261 64.4 504853760 340386392 ? Sl 09:41 103:31 /usr/local/bin/cuzk --config /tmp/cuzk-bench-config.toml --listen 127.0.0.1:9820 --log-level info root 2591 0.3 0.0 4182508 57296 ? Sl 09:45 0:01 /usr/local/bin/cuzk-bench --addr http://127.0.0.1:9820 batch -t porep --c1 /data/32gbench/c1.json --count 5 --concurrency 4 root 2592 0.... ``

This message, sent by the AI assistant during an opencode coding session, captures a moment of empirical validation. After days of debugging OOM (Out of Memory) crashes on GPU instances rented through vast.ai, the assistant had just deployed a custom-built memprobe utility to a live, running instance. The results were stark: the machine was operating at 99% of its cgroup memory limit, with only 14 GiB of headroom remaining and 6 GiB of invisible kernel and driver overhead consuming memory that standard detection tools could not account for. This message is the culmination of a deep investigative thread into why memory-constrained GPU instances were crashing during Filecoin proof generation, and it represents the moment when a theoretical explanation met real-world data.

Why This Message Was Written: Context and Motivation

To understand why this message exists, one must understand the problem it was solving. The team was running a GPU-accelerated proving system called cuzk on vast.ai, a marketplace for renting GPU-equipped cloud servers. These instances often run inside Docker containers with cgroup memory limits — the host machine may have 503 GiB or 2003 GiB of RAM, but the container is capped at a fraction of that (e.g., 342 GiB). The standard memory detection approach — reading /proc/meminfo — reports the host's full RAM, not the cgroup limit. This caused the system to massively over-allocate memory, leading to OOM kills.

Earlier in the session, the assistant had already fixed the cgroup-aware memory detection in detect_system_memory(), rewritten the memcheck.sh utility, and deployed these fixes to production instances. But a 342 GiB instance (the one with the RTX 5090) still crashed during benchmarking with a transport error that looked like an OOM kill. The root cause investigation (detailed in Chunk 1 of Segment 29) revealed a deeper problem: the CUDA pinned memory pool (PinnedPool) was operating outside the MemoryBudget system, meaning pinned buffers returned to the pool were never freed from actual RSS. Combined with kernel/driver overhead (glibc arenas, page tables, GPU driver allocations) and transient SRS loading spikes, the 10 GiB safety margin baked into the budget calculation was empirically insufficient.

The memprobe utility was the direct response to this diagnosis. Written in C, it allocates 1 GiB chunks via mmap and memset until it nears the cgroup limit, then reports how much was actually allocatable. This provides a data-driven safety margin that accounts for the hidden kernel overhead that standard tools miss. The message we are examining is the first live deployment of this utility on a production instance — the moment of truth.## The Data: Reading the Numbers

The memprobe output, quoted directly in the message, contains a wealth of information. Let's parse it carefully:

The Follow-Up: Checking If the Benchmark Is Still Alive

The message does not stop at presenting the memprobe data. The assistant immediately follows up with a critical question: "Let me check if the current benchmark is still alive." This is not idle curiosity. The entire investigation was motivated by crashes on memory-constrained instances. If the benchmark had already been OOM-killed, the memprobe data would be a post-mortem confirmation. If it was still running, it would be a live validation that the current budget (331 GiB, as shown in the ps output) was barely sufficient.

The ps output confirms the benchmark is alive and has been running for over 103 minutes. The cuzk daemon is using 504,853,760 kB (about 481 GiB) of virtual memory and 340,386,392 kB (about 324 GiB) of RSS — consistent with the memprobe readings. The cuzk-bench client is running with concurrency 4, processing batches of 5 proofs. The system is holding on, but the numbers tell a story of extreme memory pressure.

How Decisions Were Made in This Message

This message is primarily an observation and validation message, not a decision-making message. The assistant is reporting results from a tool it just deployed. However, several implicit decisions are visible:

  1. The decision to run memprobe on a live production instance: Rather than testing in a controlled environment, the assistant deployed the probe directly to the instance currently running a benchmark. This was a calculated risk — memprobe allocates memory aggressively and could theoretically trigger an OOM kill itself. The probe's design (stopping 2 GiB before the cgroup limit) was intended to avoid this, but on a machine already at 99% utilization, the risk was real. The assistant judged that the value of real-world data outweighed the risk of disrupting the benchmark.
  2. The decision to present the data in human-readable form: The raw JSON output from memprobe is transformed into a bullet-point summary with bold labels and percentage calculations. This is a deliberate choice to make the data immediately interpretable. The assistant could have simply quoted the JSON; instead, it computed the "99% of cgroup limit" figure and highlighted the "only 14 GiB more" finding. This reflects an awareness that the audience (the user) needs to grasp the severity at a glance.
  3. The decision to verify the benchmark was still alive: After presenting the alarming memory data, the assistant immediately checks whether the benchmark process is still running. This is a sanity check — if the benchmark had crashed, the memprobe data would need to be interpreted differently (as a snapshot of a system that had already failed). The fact that the benchmark is still running strengthens the conclusion: the system is operating at the edge of stability, not past it.## Assumptions Embedded in the Message Every analytical message rests on assumptions, and this one is no exception. The assistant makes several implicit assumptions that are worth examining: Assumption 1: The memprobe utility accurately measures usable memory. The probe works by allocating 1 GiB chunks via mmap and memset until it nears the cgroup limit. This assumes that mmap-based allocation is representative of the application's actual memory usage patterns. In reality, cuzk uses a mix of cudaHostAlloc (for pinned memory), Go heap allocations, and file-backed mmaps (for SRS parameters). The kernel overhead for these different allocation types may vary. The probe's measurement of 6 GiB of overhead is a lower bound — the actual overhead during a real benchmark could be higher due to GPU driver state, CUDA context overhead, and other factors that the simple mmap probe does not trigger. Assumption 2: The benchmark's survival implies the budget is sufficient. The assistant checks whether the benchmark is still alive and finds that it is, after 103 minutes of runtime. This is taken as evidence that the system is "barely" stable. But a single surviving run does not guarantee stability — the benchmark could crash on the next proof due to a transient spike. The assistant implicitly recognizes this by describing the machine as running at "99% of its cgroup limit" and having "zero headroom." The survival is informative but not conclusive. Assumption 3: The overhead is kernel/driver overhead. The assistant labels the 6 GiB difference as "kernel/driver overhead." This is a reasonable inference — page tables, slab allocator caches, GPU driver allocations, and glibc arena metadata are all known sources of invisible memory consumption. However, some of this overhead could also come from the benchmark's own allocations that were not freed when memprobe ran, or from double-counting between cgroup memory accounting and RSS. The precise breakdown would require deeper instrumentation.

Input Knowledge Required to Understand This Message

A reader needs several pieces of context to fully grasp what this message is saying:

  1. The cgroup memory model: Understanding that Docker containers impose memory limits via cgroups, and that /proc/meminfo reports host RAM rather than the container limit, is essential. Without this, the significance of "cgroup limit: 342 GiB" versus "host RAM: 503 GiB" is lost.
  2. The OOM crash history: The message is the culmination of a multi-day debugging effort where GPU instances were crashing during proof generation. The reader needs to know that the previous fix (cgroup-aware memory detection) reduced but did not eliminate crashes, and that the memprobe utility was built specifically to measure the hidden overhead that standard tools miss.
  3. The memprobe design: The probe stops 2 GiB before the cgroup limit to avoid triggering an OOM kill. This design decision explains why the probe reports 14 GiB of allocatable memory even though the gap between current usage and the limit is only about 23 GiB (342 GiB - 319 GiB). The probe deliberately leaves a safety margin.
  4. The benchmark infrastructure: The benchmark.sh script, the cuzk daemon, and the cuzk-bench client form a three-tier architecture. The ps output shows all three processes, and understanding their roles helps interpret the memory usage numbers.
  5. The vast.ai environment: The instances are rented from vast.ai, a marketplace where users bid on GPU servers. The memory constraints vary wildly between instances, and the system must adapt automatically. The 342 GiB limit on this instance is typical of the constrained environments that cause crashes.

Output Knowledge Created by This Message

This message creates several valuable pieces of knowledge:

  1. Empirical validation of the overhead hypothesis: The 6 GiB of kernel/driver overhead is now measured, not guessed. This transforms the debugging effort from speculation to data-driven engineering. Future budget calculations can incorporate this measured overhead rather than relying on arbitrary safety margins.
  2. A benchmark for the OOM recovery logic: The benchmark.sh script now includes an OOM recovery loop that reduces the budget by 10% and retries up to three times. The memprobe data provides the baseline for what "normal" memory pressure looks like on a constrained instance. If the recovery loop ever triggers, the new budget will be compared against this baseline.
  3. Confirmation that the memprobe approach works: The utility ran successfully on a live, memory-constrained instance without crashing it. This validates the design decision to stop 2 GiB before the cgroup limit and proves that the probe can be safely deployed in production.
  4. A vivid illustration of the memory pressure problem: The "99% of cgroup limit" figure is a powerful communication tool. It makes the problem concrete and urgent in a way that abstract discussions of kernel overhead cannot. This figure can drive prioritization decisions — if the team needs to allocate engineering time to memory optimization, this data provides the justification.

The Thinking Process Visible in the Message

The assistant's reasoning is on full display in this message. Several cognitive moves are worth highlighting:

From data to insight: The raw JSON output from memprobe contains eight numeric fields. The assistant does not simply present this data; it extracts the most salient figures, computes derived values (99% of limit, 14 GiB headroom, 6 GiB overhead), and presents them in a structured, human-readable format. This is the hallmark of effective data analysis — transforming raw measurements into actionable insights.

From insight to verification: The assistant does not stop at interpreting the data. It immediately formulates a follow-up question: "Is the benchmark still alive?" This reflects a scientific mindset — every measurement must be contextualized. If the benchmark had crashed, the memprobe data would be a snapshot of a failed system. If it was still running, the data would be a snapshot of a stressed but functioning system. The assistant checks this before drawing conclusions.

From verification to narrative: The ps output confirms the benchmark is alive, and the assistant lets the data speak for itself. There is no explicit conclusion like "therefore the budget is sufficient" or "therefore we need more headroom." Instead, the juxtaposition of the 99% utilization figure and the running benchmark creates a narrative tension: the system is surviving, but barely. The reader is left to draw the obvious conclusion that the adaptive safety margin and OOM recovery logic are essential.

Conclusion

Message 4023 is a small but pivotal moment in a larger debugging saga. It captures the instant when a theoretical model of memory pressure met real-world data, and the model was confirmed. The memprobe utility, written in C and deployed to a live production instance, revealed that the system was operating at 99% of its cgroup memory limit with only 14 GiB of headroom and 6 GiB of invisible kernel overhead. The benchmark was still running, but the margin for error was razor-thin.

This message exemplifies the best practices of systems debugging: formulate a hypothesis, build a tool to test it, deploy the tool to a real environment, interpret the results carefully, and verify your interpretation with additional data. The assistant's clear presentation of the numbers, its immediate follow-up check, and its implicit framing of the results as "very revealing" all contribute to a message that is both informative and consequential. For anyone following the debugging thread, this is the moment the problem became measurable — and therefore solvable.