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They have recently published a blog series which started with 35,000x and ended with 68,000x [1]. The thing is anyone familiar with performance sensitive code i
by ayhanfuat 3y ago
They have recently published a blog series which started with 35,000x and ended with 68,000x [1]. The thing is anyone familiar with performance sensitive code is not looking at Python vs the other thing. They are looking for whether or not it is in the ballpark of C, Julia, Cython, Numba, etc. Those are the contenders, not Python.
[1] https://www.modular.com/blog/mojo-a-journey-to-68-000x-speedup-over-python-part-3 https://www.modular.com/blog/mojo-a-journey-to-68-000x-speed...
- luma 3y agoIf you read the description, they started with 35,000x, then tripled the number of cores to get 68,000x. If your triple the cores and wind up with less than twice the performance, your scaling isn't very good.
- vorticalbox 3y agoDepends on the task not all tasks scales linearly
- fooker 3y agoIt is very good, especially if you don't have to rewrite your code. Try to do it yourself (say take a merge sort implementation and try to make it scale), it'll be roughly this much after a few hours of work. Now imagine the compiler is doing this for you.
- DiffProg 3y agoKeyword is imagine, because with Mojo you do have to write your code in a new language. You cannot use the Python parts if you want speed. That's writing code in a new language.
- fooker 3y agoThat's the stated goal, it's not mature technology. You can have your python parts source compatible with the new language. Of course they are far off from that. But not that far off if your python code is typical ML framework consumer code.
- ActivePattern 3y agoI think the reason that they focus on pure Python is because a big use case would be pure Python code bases that can be incrementally ported over to Mojo for better performance, without needing to totally abandon the Python language and ecosystem.