This automated approach reduces the search scope from thousands of nodes to a handful and cuts the search time from days to minutes. Once a straggler is identified, the service flags it so you can take action, such as cordoning the node and rescheduling the workload onto healthy infrastructure.
Real-world impact: Accelerating AI innovation
This automated approach is already delivering a significant impact for companies training frontier models on Google Cloud.
One of these companies is Magic, which partnered with Google to develop their frontier LLM with 100M token context windows trained across thousands of GPUs. Prior to Google’s automated straggler detection algorithm, the process for identifying straggler nodes required a lot of manual observability and troubleshooting.
Eric Steinberger, Magic’s Co-founder and CEO, described a challenging situation they faced: “Magic’s hero workload running across 8,000 GPUs was experiencing severe performance degradation over a period of 40 hours. Debugging this required access to precise low-level device and networking performance statistics.” Eric explained how the automated service provided a swift resolution. “Google’s automated straggler detection was able to identify straggler nodes, and their team was available 24/7 until the issue was resolved. We now have straggler detection enabled by default.”
The Allen Institute for AI (Ai2), whose open-sourced language models were trained on Google Cloud, saw a similar boost in research productivity. Sophie Lebrecht, Ai2 COO, highlighted the challenge their teams faced. “Previously, our teams wasted precious cycles trying to pinpoint the exact source of a node and/or GPU failure during our lengthy training runs.” Sophie explained how the new capability changed their workflow. “Straggler detection on GCP was a big productivity boost for our ML research team… We’ve been able to increase our development velocity significantly with straggler detection.”
Get started: Using automated straggler detection
Automated straggler detection is an always-on service offered within Google Cloud’s Cluster Director, passively monitoring your GPU clusters and automatically flagging potential stragglers that might be slowing down your workload.
When you experience a performance slowdown, you can quickly check the results in your dashboard:
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Navigate to the Dashboards page in the Google Cloud console.
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Find and click on the Cluster Director Health Monitoring dashboard.
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Review the Straggler Detection section to see the Suspected Straggler Instances table.
This table provides a simple, actionable list of nodes that are likely causing the slowdown, allowing you to take immediate action. For a detailed guide on interpreting the results and troubleshooting steps, visit our documentation on how to troubleshoot slow performance.
Source Credit: https://cloud.google.com/blog/products/compute/stragglers-in-ai-a-guide-to-automated-straggler-detection/
