UFM system diagnostics: Worked Example — Troubleshooting Tools (NVIDIA-Certified Professional: AI Networking)
UFM System Diagnostics: Worked Example In the context of the NVIDIA-Certified Professional: AI Networking certification, understanding how to...
UFM System Diagnostics: Worked Example
In the context of the NVIDIA-Certified Professional: AI Networking certification, understanding how to effectively utilize UFM (Unified Fabric Manager) system diagnostics is crucial for troubleshooting networking issues. This worked example will guide you through a realistic scenario where you will apply UFM diagnostics to resolve a network performance issue.
Scenario
Imagine you are managing a data center where multiple GPUs are connected through a high-speed InfiniBand network. Recently, users have reported latency issues during high-load periods. Your task is to diagnose the problem using UFM system diagnostics.
Step 1: Access UFM Dashboard
Begin by logging into the UFM web interface. Ensure you have the necessary credentials and access rights to view the network topology and diagnostics tools.
Step 2: Check Resource Allocation
Use the cl-resource-query command to check the current resource allocation for your GPUs and network interfaces. This command provides insights into how resources are distributed and whether any bottlenecks exist.
Command:
cl-resource-query
Expected Outcome: A list of resources allocated to each GPU, including bandwidth usage and memory allocation.
Step 3: Analyze Real-Time Events
Next, utilize the wJH (Web Job Handler) for real-time event analysis. This tool helps identify any anomalies or errors occurring in the network.
Command:
wjh -analyze
Expected Outcome: A report detailing any recent errors, including timestamps and affected devices.
Step 4: Verify Low-Latency Performance
To ensure low-latency performance across the GPU, CPU, and storage, run diagnostics using the ibping command. This command tests the latency between nodes in the network.
Command:
ibping -s
Expected Outcome: Latency measurements between nodes, indicating whether any nodes are experiencing higher latency than expected.
Step 5: Conduct UFM System Diagnostics
Finally, run the UFM system diagnostics to check for any underlying issues within the network fabric. Use the ibdiagnet command for a comprehensive diagnostic report.
Command:
ibdiagnet
Expected Outcome: A detailed report on the health of the InfiniBand fabric, including any detected faults or misconfigurations.
Step 6: Analyze Results and Take Action
Review the outputs from the commands executed. If latency issues are identified, consider reconfiguring resource allocations or addressing any hardware faults indicated in the diagnostics report. Document your findings and any actions taken for future reference.
Conclusion
By following these steps, you can effectively utilize UFM system diagnostics to troubleshoot and resolve network performance issues in an NVIDIA AI Networking environment. Mastery of these tools is essential for success in the NVIDIA-Certified Professional: AI Networking certification.