Ongoing Efforts to Add CUDA Backend to MLX for Improved Training Speed
By
nsagent
Sesame, salt, and substance. A flagship bake.
Summary
The article discusses ongoing efforts to add a CUDA backend to MLX, with optimizations to improve training speed and challenges in saving operands and temporaries until the kernel finishes.
Key quotes
· 2 pulled"Tried the ideas: switching the implementation of Event from cuda::std::atomic to cudaEvent bumped training speed from 500 it/s to 900; reducing the prefetch calls increased it from 900 it/s to 1100."
"The next optimization is tricky: after evaluating each op, the operands and temporaries are saved until kernel finishes."
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