Tracing the CUDA Initialization Failure: A Diagnostic Deep Dive

In the midst of a complex deployment of the GLM-5-NVFP4 large language model across eight NVIDIA RTX PRO 6000 Blackwell GPUs running inside a Proxmox LXC container, the team encountered a frustrating and stubborn blocker: CUDA refused to initialize. Message [msg 553] captures a pivotal moment in the diagnostic process — a shift from high-level investigation into low-level system call tracing that would ultimately lead to the root cause. This message, though brief, represents the critical transition between observing a failure and understanding its mechanism.

The Context: A Stubborn CUDA Blocker

The broader session had been an exercise in overcoming hardware and software barriers. The team had successfully set up an ML environment on Ubuntu 24.04 with NVIDIA driver 590.48.01 and CUDA Toolkit 13.1, resolved flash-attn build issues, and deployed eight RTX PRO 6000 Blackwell GPUs. However, when they attempted to run CUDA workloads inside an LXC container on Proxmox — a setup chosen to bypass the PCIe P2P limitations of the KVM-based virtual machine — cuInit() began behaving erratically.

In the messages immediately preceding [msg 553], the assistant had been running a series of diagnostic tests. Earlier attempts showed cuInit() hanging indefinitely, timing out after 10 seconds without returning any error code. The assistant investigated GSP firmware loading, checked for Blackwell-specific firmware files, examined kernel versions, and even ran strace — which confusingly showed cuInit completing successfully with exit code 0. Then, in [msg 552], a direct Python call to cuInit(0) returned error code 3 (CUDA_ERROR_NOT_INITIALIZED) and reported zero devices. This was progress: the call no longer hung, but it still failed.

The Message: Processing a New Data Point

Message [msg 553] opens with the assistant synthesizing the new information:

So cuInit returns 3 (CUDA_ERROR_NOT_INITIALIZED), but this time it returned quickly (didn't hang). The strace showed it exiting cleanly too. Earlier the hang may have been something else. Let me now see the actual ioctl calls to understand what's failing.

This brief paragraph reveals the assistant's reasoning process in real time. It notes three key observations: (1) the error code is 3, meaning CUDA could not initialize; (2) the call completed quickly rather than hanging; and (3) the earlier strace showed a clean exit. The assistant then draws a tentative conclusion — that the earlier hang "may have been something else" — before pivoting to the next diagnostic step: tracing the actual ioctl calls.

The assistant then executes a strace command:

strace -f -e trace=ioctl,open,openat -t python3 -c 'import ctypes; lib=ctypes.CDLL("libcuda.so.1"); print(lib.cuInit(0))' 2>&1 | grep -E 'nvidia|cuda|error|ENOENT|EPERM|EACCES|0x00|nvidiactl' | tail -40

The command is carefully constructed. The -f flag follows child processes (since CUDA's userspace library may spawn threads). The -e trace=ioctl,open,openat limits tracing to just the system calls most likely to reveal the failure — file operations and device control operations. The grep filter targets NVIDIA-specific patterns and common error codes. The tail -40 captures the most relevant portion of what could be a very long trace.

The Reasoning: Why strace?

The choice of strace is itself a statement about the assistant's mental model of the problem. The assistant has ruled out several high-level causes: GSP firmware is present and loaded (nvidia-smi reports GSP Firmware Version 590.48.01), the kernel module is loaded (all eight /dev/nvidia devices exist), and the driver version is current. The failure must therefore be occurring somewhere in the interaction between the CUDA userspace library (libcuda.so.1) and the kernel driver — at the system call boundary.

By tracing ioctl calls specifically, the assistant is targeting the primary mechanism by which the CUDA driver communicates with the kernel module. The NVIDIA driver uses ioctl on the /dev/nvidia* device files to perform GPU initialization, memory allocation, and context creation. If cuInit is returning error 3, something in that ioctl chain is failing — perhaps a particular command is returning an unexpected status, or the driver is hitting a resource limit or permission check.

The inclusion of open and openat in the trace is also strategic. The assistant wants to verify that the CUDA library can successfully open the NVIDIA device files. If openat returns an error like ENOENT (file not found) or EPERM (permission denied), that would explain the initialization failure. The fact that the earlier strace (in [msg 551]) showed openat succeeding for /dev/nvidia3 and /proc/driver/nvidia/params suggests the issue lies deeper, in the ioctl exchange itself.

Input Knowledge Required

To fully understand this message, the reader needs familiarity with several domains. First, knowledge of the CUDA runtime architecture: cuInit is the entry point that initializes the CUDA driver API, discovers available devices, and establishes communication with the kernel driver. Error code 3 (CUDA_ERROR_NOT_INITIALIZED) is a generic failure that can have many causes — from missing devices to driver incompatibility to resource exhaustion.

Second, understanding of Linux system call tracing: strace intercepts and records system calls made by a process. The ioctl system call is the standard interface for device-specific operations on Unix-like systems. The NVIDIA driver defines a large set of ioctl commands for GPU management, and understanding which specific ioctl is failing would require knowledge of the driver's internal protocol.

Third, context about the deployment environment: the assistant is working inside an LXC container on Proxmox VE, with eight Blackwell GPUs passed through to the container. The Proxmox kernel (6.8.12-9-pve) is a customized version with virtualization and container support, which may interact differently with the NVIDIA driver than a standard kernel.

Output Knowledge Created

The strace output shown in the message reveals the initial sequence of operations: the CUDA library opens /proc/driver/nvidia/params (to read driver parameters), then opens /dev/nvidia3 multiple times (file descriptors 28-31). The output is truncated, so the critical ioctl calls that follow are not visible in this message. However, the fact that the opens succeed is itself valuable information — it rules out permission issues or missing device files.

The full strace output (which the assistant would see in the next message) would show the ioctl calls and their return values. This data becomes the foundation for the next round of investigation, ultimately leading to the discovery that the nvidia_uvm module's Heterogeneous Memory Management (HMM) feature is incompatible with the Proxmox kernel, causing the UVM (Unified Virtual Memory) initialization to fail silently.

Assumptions and Potential Missteps

The assistant makes one notable assumption in this message: that the earlier hang "may have been something else" — i.e., a different problem from the current error 3. In reality, both symptoms (the hang and the error code) were likely manifestations of the same root cause: the HMM incompatibility causing the UVM initialization path to behave unpredictably. The hang may have occurred when the kernel module attempted to interact with the HMM subsystem and blocked on a lock or wait queue; the error 3 may have occurred when the driver detected the HMM incompatibility and returned a failure code instead of hanging.

This assumption is understandable and even productive — it prevents the assistant from anchoring on a single explanation and keeps the investigation open to new possibilities. The assistant is not committing to a theory; it's simply noting the discrepancy and moving forward with more data collection.

The Significance of This Diagnostic Step

Message [msg 553] represents a classic debugging pattern: when high-level diagnostics fail to reveal the cause, drop to a lower level of abstraction. The assistant has exhausted the information available from nvidia-smi, /proc/driver/nvidia/, and simple Python tests. The next logical step is to observe the actual interaction between the CUDA library and the kernel driver at the system call level.

This approach is particularly important in containerized environments, where the kernel interface may behave differently than on bare metal. The LXC container shares the host kernel but has its own namespace and cgroup restrictions. A system call that works on the host might fail inside the container due to seccomp profiles, device cgroup policies, or other container isolation mechanisms. By tracing the actual system calls, the assistant can determine whether the failure is at the kernel boundary (a blocked ioctl) or within the driver itself (a valid ioctl that returns an error status).

The Path Forward

The strace output from this message, combined with further investigation in subsequent messages, would reveal that the ioctl calls to the NVIDIA devices succeed but that the UVM (Unified Virtual Memory) initialization fails. This would lead the assistant to investigate the nvidia_uvm module parameters, ultimately discovering the uvm_disable_hmm=1 workaround that resolves the issue entirely.

In the larger narrative of the session, message [msg 553] is the moment when the investigation transitions from "what is failing" to "how is it failing." The assistant stops asking which component is broken and starts asking what sequence of operations leads to the failure. This shift in framing — from symptom to mechanism — is what enables the eventual discovery of the HMM incompatibility and the fix that gets the deployment back on track.

The message also illustrates a broader principle of debugging complex systems: when faced with an opaque failure, instrument the boundary between components. The boundary between the CUDA userspace library and the kernel driver — the system call interface — is where the abstraction breaks down and the raw mechanics of the failure become visible. By placing a probe at this boundary, the assistant transforms an inscrutable error code into a traceable sequence of operations, each of which can be examined and understood.