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f0k avatar f0k commented on May 27, 2024 1
  1. Do you have proper hardware support? On a GP100 chip (Tesla P100) or GV100 (Tesla V100) float16 is faster than float32, but on all other Pascal chips (up to Titan Xp) it's 1/64th of the performance of float32.
  2. If you just need to save memory, you can still use float16 storage and float32 computation.
  3. I guess modifying cudamat won't be the easiest option for you, unless you already have a ton of code using cudamat. There are other libraries that support imperative numpy-like GPU programming now, and are more feature-complete than cudamat. Have a look at cupy or PyTorch, for example.

I'll close this issue for now -- if you think adding fp16 to cudamat would still be the best solution, feel free to reopen and we can have a closer look!

from cudamat.

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