The Importance of Check Pointing in GPU Clusters

Check pointing is essential in GPU clusters due to the high frequency of GPU failures and the need for coherence among GPUs. Each GPU's loss results in the loss of progress since the last checkpoint, making frequent check points vital. The risks of failure in GPUs, stemming from hardware issues and various points of failure in the network, storage, and memory connections, emphasize the necessity of reliable check pointing. As GPUs have outpaced the performance of other components in data centers, investment in improved networking, storage, and memory technologies is crucial. By enhancing reliability and reducing failure rates, the need for frequent check pointing can be minimized, leading to better utilization rates. The interplay of compute efficiency (SFU), PUE (power utilization efficiency), and check pointing frequency plays a pivotal role in determining performance versus costs in GPU clusters. A balance between these factors can lead to significant competitive advantages in operational costs and efficiency, ultimately impacting the resource expenditures for advanced AI models and inference processes.

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