Understanding GPU Metrics: nvidia-smi vs. Guest OS Insights

The discrepancy between GPU usage reported by nvidia-smi on the host system and the metrics seen within the guest OS (such as a virtual machine or container) can arise due to several reasons. Understanding these differences requires a look into how GPU metrics are collected and reported in both environments. Here are some common causes and explanations:

Common Causes of Discrepancies:

1. Virtualization Overhead:

• Host vs. Guest Perspective: In virtualized environments, the host system (nvidia-smi on the host) has a broader view of GPU usage, including all resources shared across different virtual machines (VMs) or containers. Conversely, the guest OS sees only its allocated slice of GPU resources.
• Resource Allocation: The GPU usage reported in the guest OS is typically limited to the resources allocated to that VM or container. If the host allocates more or less GPU power dynamically, the metrics will differ.

2. NVIDIA Virtual GPU (vGPU) Configuration:

• vGPU Profiles: When using vGPU technology, different profiles can be assigned to different VMs. These profiles dictate how much GPU resources each VM gets. The host’s nvidia-smi command will show overall usage, while the guest OS will show usage limited to its assigned profile.
• Driver and API Differences: Different versions of drivers or APIs between host and guest can lead to different interpretations of GPU usage.

3. Docker Container Isolation:

• NVIDIA Docker Runtime: When using Docker with the NVIDIA runtime, the container might only have visibility into the GPU resources assigned to it. The nvidia-smi command inside the container reflects usage limited to what the container can access.
• Shared vs. Exclusive Mode: Depending on how GPUs are shared among containers, the metrics might show aggregate usage differently.

4. Monitoring Tools and APIs:

• NVIDIA Management Library (NVML): nvidia-smi relies on NVML to report GPU metrics. If the guest OS uses different tools or APIs to gather GPU metrics, they might interpret the data differently.
• Custom Metrics Collection: Some environments might use custom scripts or software to monitor GPU usage, which can differ from what nvidia-smi reports.

5. Scheduling and Job Management:

• GPU Task Scheduling: The host system might schedule tasks differently across GPUs, which can cause momentary discrepancies in reported usage.
• Idle vs. Active States: If the GPU is idle in the host but active in the guest or vice versa, the reported usage metrics will differ.

Steps to Investigate and Align Metrics:

1. Check vGPU Profile and Allocation:

• Verify the vGPU profile assigned to the VM and ensure it aligns with the expectations. Profiles can limit the available GPU resources to a guest.
• Example: On the host, use nvidia-smi vgpu to check the profiles and their allocations.

2. Synchronize Driver and API Versions:

• Ensure that the host and guest OS use compatible versions of NVIDIA drivers and CUDA toolkit. Mismatched versions can lead to reporting inconsistencies.
• Example: Check driver versions using nvidia-smi on both host and guest, and ensure they are compatible.

3. Inspect Docker GPU Configuration:

• If using Docker, check how GPUs are allocated to containers and if they are shared among multiple containers.
• Example: Use docker inspect to see the GPU configuration for running containers.

4. Use Consistent Monitoring Tools:

• Compare GPU usage metrics using the same tool or API across both host and guest. Preferably, use nvidia-smi in both environments for consistent reporting.
• Example: Run nvidia-smi inside the container or VM and compare with the host’s output.

5. Monitor GPU Utilization Over Time:

• Collect GPU usage data over a period to understand patterns and discrepancies. Temporary spikes or drops can cause short-term differences in reported usage.
• Example: Use nvidia-smi with logging options to monitor usage over time.

6. Understand Resource Scheduling:

• Investigate how the host schedules GPU tasks among different guests. Resource scheduling policies can impact how usage is reported.
• Example: Review the GPU task scheduling settings and logs on the host system.

Practical Example of Comparison:

To compare the GPU metrics between host and guest, you can use the following commands:

• On the Host System:

  nvidia-smi

• Inside the Guest OS (VM or Container):

  nvidia-smi

Collect and compare the output of these commands to understand how each environment sees GPU usage.

By following these steps and understanding the underlying reasons for discrepancies, you can better align GPU usage metrics between the host and guest environments.

Conclusion

After comparing GPU metrics such as nvidia-smi vs. Guest OS Insights, it is concluded that administrators can identify resource shortages, optimize resource allocation, and ensure efficient utilization of GPU resources across virtualized environments. Follow all these steps to check the GPU metrics that help in maintaining performance, diagnose the issue, and make the quick decision to boost the overall efficiency of the best GPU dedicated server for GPU-intensive workloads. By following the overall steps, you can better align the differences between the GPU host and guest environments.