JulianJS Posts - Answer Overflow

JulianJS

•Created by JulianJS on 8/30/2024 in #questions

Why not separate Parameters and Generics?

The mojo docs have the following example:

fn repeat[MsgType: Stringable, //, count: Int](msg: MsgType):
    @parameter
    for i in range(count):
        print(msg)

fn repeat[MsgType: Stringable, //, count: Int](msg: MsgType):
    @parameter
    for i in range(count):
        print(msg)

I personally find it a little confusing and "hacky" to mix Parameters and Generics. The docs even say

This is a little confusing, because the parameter value you're passing in this case is a type

What about adding an additional "block" to separate them like (only one idea):

fn repeat[MsgType: Stringable](msg: MsgType)<count: Int>: ...

fn repeat[MsgType: Stringable](msg: MsgType)<count: Int>: ...

With that you have clear separation - [...]: for Generics / Types; - (...): for function arguments (generics/types can be used here) - <>: for parameters; used inside the function Any thoughts on this? 🤔 🤓

2 replies

MModular

•Created by JulianJS on 7/22/2024 in #questions

Matrix Multiplication (matmul): `numpy` hard to beat? even by mojo?

Super interested in mojo and wanted to try out some of the documentation/blog examples. 🤓 🔥 https://docs.modular.com/mojo/notebooks/Matmul Great explanations and the step by step speed improvements are amazing to see! 👍 However, in the end a comparison to a real world alternative is interesting. No one would seriously do matmul in pure python 😆 . So I compared the performance to numpy which is a much better "baseline" for comparison. Results on my machine: - Naive matrix multiplication - 0.854 GFLOP/s - Vectorized matrix multiplication without vectorize - 5.71 GFLOP/s - Vectorized matrix multiplication with vectorize - 5.81 GFLOP/s - Parallelized matrix multiplication - 35.2 GFLOP/s - Tiled parallelized matrix multiplication - 36.8 GFLOP/s - Unrolled tiled parallelized matrix multiplication - 35.3 GFLOP/s - Numpy matrix multiplication - 134.2 GFLOP/s Results - gigantic speedup comparing against naive, pure python 🔥 - still almost 4x SLOWER compared to numpy 😕 Wondering if numpy is so heavily optimised for this operation that there is little way to keep up or improve upon? Does anyone have ideas for further optimisations to get mojo closer to numpy? Is this something that only a framework like MAX or super low level bit manipulation can achive? 🤔

4 replies

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