Monitoring Systems Implementation in HPC is about turning a "Black Box" into a "Glass Box."

In a supercomputer, "It works" is not enough. A job might be running, but if it is only using 10% of the CPU or waiting 50% of the time for the network, you are wasting millions of dollars in potential science.

HPC Monitoring differs from standard IT monitoring because it focuses on Performance Efficiency (FLOPS/Watt) and Straggler Detection (finding the one slow node that is holding back 1,000 others).

Here is the detailed breakdown of the monitoring architecture, the key metrics to track, and the recommended toolset, followed by the downloadable Word file.

1. The Monitoring Architecture: The Observability Stack

Because HPC clusters generate millions of metrics per second, traditional monitoring tools often crash. We implement a modern, high-performance stack:

1. Exporters (The Sensors):
• Small agents running on every compute node.
• Node Exporter: Collects CPU, RAM, and Disk stats.
• DCGM Exporter: Collects NVIDIA GPU stats (Temperature, Power, Utilization).
• IPMI Exporter: Reads physical sensors (Fan speed, Voltage) directly from the motherboard.
2. Time-Series Database (The Memory):
• Prometheus: The industry standard. It "scrapes" (pulls) metrics from the exporters every 15-30 seconds and stores them efficiently.
3. Visualization (The Face):
• Grafana: Connects to Prometheus to display beautiful, real-time heatmaps. You can instantly see a "hot spot" in Rack 4 where temperatures are 10°C higher than the rest.

2. Key Metrics: What to Watch

A. The "Straggler" Metric (CPU Wait)

• The Problem: User says "My job is slow."
• The Metric: Look at CPU_IOWAIT.
• Meaning: If this is high, the CPU is idle because it is waiting for the storage to send data. The bottleneck is the Disk, not the Processor.

B. Interconnect Health (InfiniBand Errors)

• The Problem: The network is fast, but unstable.
• The Metric: SymbolErrors or LinkDowned.
• Meaning: Even one symbol error indicates a bad cable. In HPC, a packet drop forces a "Retry," which stops the entire parallel simulation for milliseconds. This ruins performance.

C. GPU Efficiency

• The Problem: AI Training is expensive.
• The Metric: GPU_UTIL vs. GPU_MEM.
• Meaning: If Utilization is 0% but Memory is 100%, the code is crashed or hung. If Utilization is 20%, the code is poorly optimized and wasting money.

3. Implementation Strategy

Phase	Action
1. Baseline	Deploy Ganglia (Legacy) or Prometheus (Modern) to get simple "Up/Down" and Load Average stats.
2. Deep Dive	Enable Job-Level Monitoring. Integrate the scheduler (Slurm) with the monitoring tool so you can tag metrics by Job ID. (e.g., "Show me the power usage of Job #12345").
3. Alerting	Configure Alertmanager. Don't alert on "High Load" (HPC is supposed to be high load). Alert on "Low Load" (Idle nodes) or "High Temp".