In the race to build bigger and more powerful AI systems, organizations are discovering that simply adding more GPUs isn’t the golden ticket to faster results. While GPU clusters with thousands—or even hundreds of thousands—of chips offer unparalleled computational power, they also introduce a formidable challenge: synchronization and checkpointing.
This blog explores why checkpointing is critical for AI training, why it becomes exponentially more challenging as GPU clusters grow, and how Clockwork’s innovative solution transforms this bottleneck into an opportunity for efficiency and cost savings.
Why is Checkpointing a Requirement for AI Training?
Training a modern AI model relies on a process called backpropagation, where the model learns by making predictions, calculating errors, and adjusting its parameters. In distributed GPU clusters, where each GPU works on a portion of the data, checkpointing ensures these updates are synchronized across the entire system. Without synchronization, GPUs would train isolated models rather than contribute to a single, unified model.
Checkpointing involves two critical steps:
- Sharing Gradients: Each GPU must communicate the changes it has made (gradients) to the rest of the cluster.
- Synchronizing Updates: This ensures all GPUs operate with the same version of the model, preventing divergence.
While vital, this process is not without its costs, especially as the scale of GPU clusters grows.
The Exponential Growth of the Checkpointing Problem
The logistical burden of synchronization grows exponentially as GPU clusters scale. Here’s why:
- Networking Complexity
- With two GPUs, there’s a single communication link.
- For 20 GPUs, the number of links grows to 190.
- For 200,000 GPUs, there are almost 20 billion links.
- Communication Overhead
- Synchronization time increases commensurately with the number of connections, often consuming up to 50% of training time for large clusters.
- According to AMD, 40% of AI training and inference time is spent on network communications rather than computation.
- Impact on Time-to-Failure
- In a newly deployed 10,000-GPU cluster, failures in synchronization can lead to a time-to-first-job-failure as low as 157 minutes.
