GPU and AI Infra: Telemetry and Observability

Overview

Modern AI workloads depend heavily on GPU infrastructure for training and inference. These GPUs are expensive, power-hungry, and critical for production systems. Downtime, overheating, or inefficiency can result in wasted resources and delayed projects. This makes telemetry and observability essential: monitoring GPU health, collecting system metrics, and enabling automated responses.

In this article, we’ll explore:

• Telemetry basics for GPU clusters.
• Observability pillars (metrics, logs, traces).
• Alerts and automation strategies.
• A practical Java example for telemetry collection.
• Interview-focused insights and practice exercises.

By the end, you’ll understand how to design monitoring systems for GPU infrastructure — a frequent topic in FAANG interviews and large-scale system design discussions.

Why Telemetry and Observability Matter

• GPU utilization and cost: Training large models can cost millions of dollars. Underutilized GPUs waste money.
• Reliability: Detect overheating, failing drivers, or kernel errors early.
• Scalability: Monitor across thousands of GPUs in a distributed cluster.
• Optimization: Collect data to improve scheduling, cooling, and performance.

In short: Telemetry keeps the system alive. Observability helps you understand why.

Telemetry in GPU-Based AI Infrastructure

Telemetry is the process of collecting and transmitting metrics from GPUs and nodes.

Common Metrics

• Utilization (%): How busy the GPU cores are.
• Memory usage (MB/GB): Detects OOM conditions.
• Temperature (°C): Prevents hardware failures.
• Power consumption (W): Tracks energy efficiency.
• Error rates: CUDA errors, ECC memory faults.

Tools & Frameworks

• NVIDIA DCGM (Data Center GPU Manager): Low-level telemetry for NVIDIA GPUs.
• Prometheus: Metrics collection and scraping.
• Elasticsearch + Kibana: Storing and visualizing telemetry data.

Example Use Case

Monitor 1,000 GPUs in a data center to:

• Detect overheating GPUs and migrate workloads.
• Spot idle GPUs for better scheduling.
• Provide dashboards for operators.

Observability: Metrics, Logs, and Traces

Telemetry feeds into observability, which goes beyond raw metrics.

1. Metrics

• Quantitative values (utilization, latency, throughput).
• Aggregated and queried over time.
• Example: “Average GPU utilization over last 5 minutes.”

2. Logs

• Unstructured/semi-structured events.
• GPU driver errors, OOM crashes, fan failures.
• Example: “GPU 02 reported driver crash at timestamp X.”

3. Traces

• Distributed traces capture job execution across GPUs.
• Helps debug bottlenecks in multi-GPU training.
• Example: “Training job spent 40% waiting for gradients to sync.”

Putting it Together

• Metrics tell you what’s wrong (utilization dropped).
• Logs explain the event (driver crash).
• Traces show the bigger picture (delayed training step).

Alerts and Automation

Observability isn’t enough without alerts and automated remediation.

Alerts

• Threshold-based: GPU temp > 85°C, memory > 90%.
• Anomaly-based: Sudden utilization drop compared to baseline.
• Composite rules: Multiple metrics (high power + high temp).

Automated Actions

• Auto-scaling: Add GPUs if workload spikes.
• Job rescheduling: Move jobs off failing GPUs.
• Cooling adjustment: Trigger additional fans/AC.

Goal: Detect and fix issues before they impact users.

Code Example: Java Telemetry Collector

Below is a Java program simulating GPU telemetry collection and sending metrics to a monitoring endpoint (e.g., Prometheus Pushgateway, Elasticsearch).

import java.net.HttpURLConnection;
import java.net.URL;
import java.util.HashMap;
import java.util.Map;
import java.util.Random;
import org.json.JSONObject;

public class GPUTelemetryCollector {
    private final String telemetryEndpoint;
    private final Random random;

    public GPUTelemetryCollector(String telemetryEndpoint) {
        this.telemetryEndpoint = telemetryEndpoint;
        this.random = new Random();
    }

    public void collectAndSendMetrics(String gpuId) throws Exception {
        Map<String, Object> metrics = new HashMap<>();
        metrics.put("gpu_id", gpuId);
        metrics.put("timestamp", System.currentTimeMillis());
        metrics.put("utilization_percent", random.nextDouble() * 100);
        metrics.put("temperature_celsius", 50 + random.nextDouble() * 30);
        metrics.put("memory_usage_mb", random.nextInt(16000));

        JSONObject json = new JSONObject(metrics);

        URL url = new URL(telemetryEndpoint);
        HttpURLConnection conn = (HttpURLConnection) url.openConnection();
        conn.setRequestMethod("POST");
        conn.setRequestProperty("Content-Type", "application/json");
        conn.setDoOutput(true);
        conn.getOutputStream().write(json.toString().getBytes());

        int responseCode = conn.getResponseCode();
        if (responseCode == 200 || responseCode == 201) {
            System.out.println("Telemetry sent: " + json);
        } else {
            System.err.println("Failed to send telemetry: HTTP " + responseCode);
        }
    }

    public static void main(String[] args) throws Exception {
        GPUTelemetryCollector collector = new GPUTelemetryCollector("http://localhost:9200/gpu-metrics/_doc");
        collector.collectAndSendMetrics("gpu-001");
    }
}

Explanation

• Collects simulated metrics (utilization, temperature, memory).
• Sends metrics via HTTP POST to a monitoring endpoint.
• Can be extended with real GPU APIs (e.g., NVIDIA DCGM bindings).

Interview Insights

A common FAANG interview prompt: “Design a monitoring system for a GPU cluster.”

How to answer:

1. Collect telemetry (GPU utilization, temperature, memory).
2. Store in Prometheus/Elasticsearch.
3. Build dashboards with Grafana/Kibana.
4. Add alerts (thresholds, anomalies).
5. Automate responses (reschedule jobs, auto-scale).

STAR Example (Amazon - CloudWatch)

• Situation: “Our training cluster needed GPU monitoring.”
• Task: “I was responsible for observability.”
• Action: “I set up telemetry pipelines to CloudWatch with custom metrics.”
• Result: “We reduced GPU idle time by 20% and improved reliability.”

Practice Exercise

Problem: Design a telemetry pipeline for 100 GPUs in a cluster.

1. Define metrics (utilization, memory, temp).
2. Pick a storage backend (Prometheus, Elasticsearch).
3. Add alerts (e.g., temp > 85°C).
4. Describe automation (e.g., job migration).
5. Write a 100–150 word STAR response tailored to Amazon, Google, or Netflix.

Conclusion

Telemetry and observability are the foundation of reliable, scalable GPU-based AI infrastructure. They:

• Detect issues early.
• Improve GPU utilization and cost efficiency.
• Enable automation for resilience.

Mastering these concepts equips you for FAANG interviews and for running production AI systems at scale.

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