Effective GPU Monitoring in Slurm Environments

Monitoring GPU usage within an SSH-accessed Slurm environment requires a combination of Slurm commands and GPU monitoring utilities, which are essential for optimizing performance and resource management. These tools not only help in tracking GPU utilization, memory usage, and temperature but also aid in diagnosing performance bottlenecks that can hinder processing. Here’s a breakdown of the methods and considerations to effectively monitor GPU activity, including the importance of using appropriate commands like squeue to check job status, along with powerful monitoring utilities such as nvidia-smi for real-time insights into GPU performance metrics and other vital statistics. Understanding these elements is crucial for efficiently managing compute resources and ensuring that jobs are executed smoothly in a high-performance computing environment.

Key Tools:

• nvidia-smi (NVIDIA GPUs):
  • This is the standard command-line utility for monitoring NVIDIA GPU activity.¹ It provides real-time information on GPU utilization, memory usage, temperature, and power consumption.
• rocm-smi (AMD GPUs):
  • For AMD GPUs, ROCm-SMI offers similar monitoring capabilities.²
• Slurm Commands:
  • squeue: To check job status and identify the nodes where your job is running.
  • srun: To execute commands on the compute nodes allocated to your Slurm job.
  • sacct: to gather accounting data from slurm jobs, including node lists.

Methods:

1. Using nvidia-smi (or rocm-smi) within a Slurm Job:
   • Identifying the Node:
     • Use squeue to find the node(s) allocated to your running Slurm job.
     • or use sacct to get the node list from a past or present job.
   • Executing nvidia-smi:
     • Use srun to execute nvidia-smi on the compute node. For real-time monitoring, you can use the watch command:
       • srun --overlap --pty --jobid=<job_id> -w <node_name> watch -n 1 nvidia-smi
       • Replace <job_id> and <node_name> with your job ID and the node name.
     • The --overlap option allows you to run commands concurrently with your job.
   • Alternatively, you can include nvidia-smi commands within your slurm job script, to log the gpu utilization over time. This is very useful for post job analysis.
2. Monitoring via Slurm Accounting:
   • Newer versions of Slurm have integrated GPU usage tracking into their accounting.
   • Check with your system administrator to see if this feature is enabled.
   • If enabled, you can use sacct to retrieve GPU usage data associated with your jobs.
3. Third-Party Monitoring Tools:
   • Tools like nvtop and gpustat provide more user-friendly, real-time monitoring interfaces.
   • These tools might need to be installed on the compute nodes.
   • Monitoring systems like Grafana, often combined with data exporters, can provide excellent graphical representations of GPU usage over time.³

Important Considerations:

• Permissions:
  • Access to GPU monitoring tools might be restricted on some HPC systems.
  • Consult your system administrator for appropriate permissions.
• Overhead:
  • Frequent monitoring can introduce some overhead.
  • Adjust the monitoring frequency as needed.
• Slurm Configuration:
  • The availability of certain monitoring features depends on the Slurm configuration.
  • System administrators play a key role in setting up and maintaining monitoring capabilities.

By combining Slurm commands with GPU monitoring utilities, you can effectively track GPU utilization within your SSH-accessed HPC environment.