The Zombie Process and the 100-GiB Memory Drain: A Pivotal Status Check in GPU Utilization Debugging

The Message

At a critical juncture in a GPU utilization investigation, the assistant executed the following remote status check:

ssh -p 40612 root@141.0.85.211 'ps aux | grep cuzk | grep -v grep; free -g'

The response revealed a system in transition:

root       73072 1025  0.0      0     0 ?        Zl   17:43 319:16 [cuzk-priodisp] <defunct>
               total        used        free      shared  buff/cache   available
Mem:             755         534         148         119         198         221
Swap:              7           0           7

This single message, indexed as &lt;msg id=2997&gt;, is deceptively simple. It contains one bash command and its output. Yet it sits at the fulcrum of a multi-hour investigation into why a CUDA-based zero-knowledge proving daemon (cuzk) was achieving only ~50% GPU utilization despite having a large backlog of work. The message captures the moment a running daemon was killed, its process became a zombie, and 100 GiB of pinned CUDA memory began to drain from the system — a necessary precondition for deploying an instrumented binary that would finally pinpoint the root cause of the performance bottleneck.

Context and Motivation: Why This Message Was Written

To understand why this message exists, one must trace back through the investigation that led to it. The cuzk proving daemon is a high-performance GPU-accelerated system for generating zero-knowledge proofs on Filecoin. It runs on a remote machine with 755 GiB of RAM and multiple NVIDIA GPUs. The team had observed a troubling pattern: despite a deep queue of synthesized partitions waiting for GPU proving, the GPU compute utilization hovered around 50%, with multi-second idle gaps visible at 200ms resolution.

The assistant had spent the prior hour instrumenting the codebase with precise timing logs. Two potential bottlenecks were hypothesized: (1) contention on a tracker lock, exacerbated by malloc_trim(0) calls in the finalizer that walk the entire 400+ GiB heap, and (2) a C++ mutex inside the two-phase GPU prove flow (prove_start/prove_finish) that might serialize GPU access. Rather than guessing, the assistant added GPU_TIMING and FIN_TIMING log instrumentation to measure every step of the GPU worker loop and finalizer with microsecond precision.

But instrumentation alone is useless without deployment. The instrumented binary needed to replace the running daemon on the remote machine. This required a careful sequence: build the binary with Docker, extract it, upload it via SCP, kill the running daemon, wait for its 400+ GiB of pinned CUDA memory to be released, and then start the new binary. Message &lt;msg id=2997&gt; is the status check that occurs immediately after the kill command was issued in &lt;msg id=2996&gt;.

The Deployment Workflow and Its Decisions

The deployment workflow reveals several deliberate decisions. First, the assistant chose to build the instrumented binary inside Docker using DOCKER_BUILDKIT=1 and a custom Dockerfile.cuzk-rebuild, producing a 27 MB statically-linked binary. This binary was extracted from a temporary container and uploaded to the remote machine via scp to /data/cuzk-timing. The choice of /data/ rather than /usr/local/bin/ was itself a lesson learned from an earlier deployment failure — the remote machine uses an overlay filesystem where /usr/local/bin/ cannot be modified.

Second, the assistant decided to kill the running daemon rather than perform a graceful restart. The daemon was mid-work, with 5 active synthesis tasks and multiple partitions queued for GPU processing. The status check in &lt;msg id=2995&gt; showed a pipeline that had been running for 1852 seconds with zero partitions completed — suggesting either a very large proof or a stuck pipeline. The kill was issued with kill $(pgrep -f cuzk-priodisp), a blunt signal (SIGTERM by default) that would terminate all processes matching the pattern.

Third, the assistant chose to wait before checking the result. The command in &lt;msg id=2996&gt; included a sleep 5 before running free -g, anticipating that memory release from pinned CUDA allocations would not be instantaneous. This turned out to be an underestimate.

What the Output Reveals

The output of message &lt;msg id=2997&gt; tells a nuanced story. The process table shows [cuzk-priodisp] &lt;defunct&gt; with state code Zl — a zombie process that has terminated but whose parent has not yet called wait() to reap its exit status. The l suffix indicates the process is threaded (it had many worker threads). The process had accumulated 319 minutes of CPU time over its 5+ hour runtime, consistent with heavy GPU compute work.

The memory statistics reveal the beginning of a slow drain. Before the kill, the system was using 633 GiB of 755 GiB total (from &lt;msg id=2995&gt;). Now, approximately 5-10 seconds after the kill, usage has dropped to 534 GiB — a release of about 100 GiB. But 534 GiB is still enormous. The daemon had allocated approximately 400 GiB of pinned CUDA memory via cudaHostAlloc, which is not freed instantaneously upon process termination. The kernel must tear down the CUDA context, unmap the pinned memory regions, and return the pages to the system's free pool. This process can take many seconds or even minutes on a system under memory pressure.

The available field of 221 GiB is particularly informative. In Linux memory accounting, available is an estimate of how much memory is available for starting new applications without swapping. It includes reclaimable page cache and slab objects. The fact that available (221 GiB) is higher than free (148 GiB) suggests a significant amount of page cache (198 GiB in buff/cache) that can be reclaimed if needed. This is consistent with a system that has been doing heavy file I/O — likely reading proof parameters and circuit data from disk.

Assumptions and Their Validity

This message and its surrounding workflow rest on several assumptions, some explicit and some implicit.

Assumption 1: The instrumented binary will reveal the bottleneck. The entire deployment hinges on the belief that the GPU_TIMING and FIN_TIMING logs will contain sufficient signal to identify why GPU utilization is only 50%. This is a reasonable assumption given that the instrumentation covers every step of the GPU worker loop and finalizer, but it assumes the bottleneck is in one of the instrumented sections. If the bottleneck were elsewhere — for example, in the CUDA driver itself, or in PCIe bandwidth contention from other processes — the instrumentation would miss it. In fact, as later chunks reveal, the root cause turned out to be H2D (Host-to-Device) transfer bandwidth, which was not directly instrumented in this round. The instrumentation would only rule out the tracker lock and malloc_trim hypotheses, forcing further investigation deeper into the C++ gpu_prove_start function.

Assumption 2: Killing the daemon is safe. The assistant assumed that killing the mid-work daemon would not corrupt any persistent state. The cuzk daemon processes proofs submitted via its HTTP API; killing it mid-pipeline means those proofs are lost and must be resubmitted. The assistant implicitly assumed this was acceptable — that the investigation's value outweighed the cost of losing in-flight work. This is a judgment call that prioritizes debugging over throughput.

Assumption 3: Memory will free quickly enough. The sleep 5 before the status check assumed that 5 seconds would be sufficient for the OS to reclaim the daemon's memory. The output shows this was insufficient — 534 GiB remained in use, and the process was still a zombie. The assistant's next message (&lt;msg id=2998&gt;) acknowledges this and begins a loop of 12×10-second waits, indicating the assumption was recognized and corrected.

Assumption 4: The SSH connection is reliable. The assistant uses a non-standard SSH port (40612) and connects as root with password-based authentication (implied by the lack of -i key flag). This assumes the remote machine's SSH daemon is configured to accept root logins and that the network path is stable. For a remote machine in a data center, this is generally safe, but it introduces a failure mode: if the SSH connection drops mid-deployment, the daemon would be killed with no replacement started.

Input Knowledge Required

To fully understand this message, one needs knowledge spanning several domains:

Linux process management: The zombie process state (Zl) and the need for a parent to reap children. The ps output format showing PID, CPU%, memory, and command.

Linux memory accounting: The difference between used, free, buff/cache, and available in free -g. Understanding that available includes reclaimable cache and is a better indicator of usable memory than free.

CUDA memory model: Pinned memory (cudaHostAlloc) is not regular heap memory. It is registered with the CUDA driver for DMA transfers and requires explicit tear-down when the allocating process exits. This tear-down is asynchronous and can take significant time for large allocations.

The cuzk architecture: The daemon uses a two-phase GPU prove flow (prove_start for H2D transfers and kernel launches, prove_finish for result collection). It uses a MemoryBudget system to gate concurrent synthesis and proving. The tracker lock protects shared state between GPU workers and finalizers.

The investigation context: The prior rounds of instrumentation, the GPU_TIMING/FIN_TIMING log format, the hypothesis about malloc_trim contention, and the decision to deploy an instrumented binary rather than guess at the bottleneck.

Output Knowledge Created

This message creates several pieces of actionable knowledge:

  1. The daemon has been successfully killed. The process is no longer running (it's a zombie), meaning the old binary is no longer consuming GPU resources. The new binary can be started once memory is freed.
  2. Memory release is in progress but incomplete. 534 GiB still in use vs. 633 GiB before the kill means ~100 GiB was freed in 5 seconds. At this rate, the remaining ~400 GiB of pinned memory will take ~20 more seconds to release. This informs the wait loop in the next message.
  3. The system has 221 GiB available memory. This is enough to start the new binary, which needs approximately 400 GiB of pinned memory for its budget. However, the new binary will need to allocate this memory fresh, which will trigger further page reclaim and potentially cause OOM if the old allocations aren't fully released first.
  4. No other cuzk processes are running. The grep cuzk only shows the zombie, confirming there are no stray daemon instances that could conflict with the new deployment.
  5. The system is not under swap pressure. Only 7 GiB of swap is used out of 7 GiB total, and the 0 in the free column for swap indicates no swap activity. This is good — it means the system isn't thrashing.

The Thinking Process Visible in the Reasoning

The assistant's reasoning, visible in the surrounding messages, follows a careful evidence-gathering methodology. The key insight is the decision to instrument before fixing. Rather than guessing which of the two hypothesized bottlenecks (tracker lock contention or C++ mutex serialization) is the real cause, the assistant adds precise timing to both and deploys the instrumented binary to gather real-world data. This is a scientific approach: formulate hypotheses, design measurements, collect data, then conclude.

The deployment sequence itself shows systems-thinking. Each step is verified: cargo check to ensure compilation, Docker build to produce the binary, docker cp to extract it, md5sum to verify integrity, scp to upload, and then the kill-and-wait sequence. The assistant checks the status API before killing to understand what work is in flight. It checks memory before and after to track the release. It anticipates the need to wait for pinned memory to drain.

The one assumption that proves slightly off — that 5 seconds would be enough for memory release — is corrected immediately in the next message with a 120-second polling loop. This demonstrates adaptive reasoning: when the data contradicts the model, update the model and retry.

Conclusion

Message &lt;msg id=2997&gt; is a single status check in a longer investigation, but it captures the essence of disciplined systems debugging. It shows the assistant: (1) forming hypotheses about a performance bottleneck, (2) instrumenting the code to collect evidence, (3) deploying the instrumented binary through a careful workflow, (4) verifying the deployment's effects, and (5) adapting when reality diverges from expectations. The zombie process and the slow memory drain are not bugs — they are features of the CUDA memory model that must be understood and worked with. The message is a reminder that in performance engineering, the most important tool is not intuition but measurement, and the most important skill is not guessing but knowing how to set up the experiment that will reveal the truth.