98% GPU Utilization Achieved in 1k GPU-Scale AI Training Using Distributed Cache

https://juicefs.com/en/blog/engineering/ai-gpu-utilization-mlperf-benchmark

by LittleCat38 • 2 years ago

22 3 2 years ago

tucnak• 2 years ago

Please stop manipulating the HN frontpage for personal gain. Your account is just juicefs posts sprinkled with cheap news from CNN, NYT, etc. No comments, too.

How do you guys do it?

rthnbgrredf• 2 years ago

Is that against the rules on HN?

poorlyknit• 2 years ago

They have an interesting idea of "achieved":

Currently, only NVIDIA v100 GPU simulation is supported, and all GPUs mentioned later in this post are simulated v100s.

So unless they can reproduce this on a real cluster cluster of V100s, it should probably be taken with a grain of salt. What I'm missing in the simulation is an accurate account of how the host-device barrier is sped up using this system. Reading the headline I was hoping they'd be using NVIDIA GPUDirect for accessing the storage directly from the devices but I believe you'd need very custom CUDA for that...

98% GPU Utilization Achieved in 1k GPU-Scale AI Training Using Distributed Cache

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