Its been a while since I worked with MATLAB and others. Whats up with GNU Octave these days? IIRC thats what folks were championing 10 years ago when anyone was talking about the problems with MATLAB.
In Defense of Matlab Code
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Re: In Defense of Matlab Code
#12As an engineer, I use Matlab (or rather, Octave the free equivalent) all the time. It's really great for numerical computing and plotting. Most things 'just work', there's a sizeable collection of packages, and I personally like how flexible the function inputs are.
Biggest drawback though is that it's over-optimized for matrix math, that it forces you to think about everything as matrices, even if that's not how your data naturally lies. The first thing they teach about performant Matlab code is that simple for-loops will tank performance. And you feel it pretty quickly, I saw a case once of some image processing, with a 1000x speedup from Matlab-optimized syntax.
Other things issues I've run into are string handling (painful), and generally OOP is unnatural. Would love to see something with the convenient math syntax of Matlab, but with broader ease of use of something like JS.
Re: In Defense of Matlab Code
#13Its been a while since I worked with MATLAB and others. Whats up with GNU Octave these days? IIRC thats what folks were championing 10 years ago when anyone was talking about the problems with MATLAB.
I sometimes run my MATLAB code under Octave (generally needs some minor tweaks to catch up with a few new features) and while I don't hit bugs, I find it is much slower.
I think the MATLAB JIT compiler is probably difficult to match.
Re: In Defense of Matlab Code
#14As an engineer, I use Matlab (or rather, Octave the free equivalent) all the time. It's really great for numerical computing and plotting. Most things 'just work', there's a sizeable collection of packages, and I personally like how flexible the function inputs are. Biggest drawback though is that it's over-optimized for matrix math, that it forces you to think about everything as matrices, even if that's not how you…
> Biggest drawback though is that it's over-optimized for matrix math ...
I think this is what inspired the creation of Julia -- they wanted a Matlab clone where for loops were fast because some problems don't fit the matrix mindset.
Re: In Defense of Matlab Code
#15There's also Julia. Earlier in my career, I found that my employers would often not buy Matlab licenses, or would make everyone share even when it was a resource needed daily by everyone. Not having access to the closed-source, proprietary tool hurt my ability to be effective. So I started doing my "whiteboard coding" in Julia and still do.
Precisely; today Julia already solves many of those problems. It also removes many of Matlab's footguns like `[1,2,3] + [4;5;6]`, or also `diag(rand(m,n))` doing two different things depending on whether m or n are 1.
I don't think Julia really solves any problems that aren't already solved by Python. Python is sometimes slower (hot loops), but for that you have Numba. And if something is truly performance critical, it should be written or rewritten in C++ anyway.
But Julia also introduces new problems, such as JIT warmup (so it's not really suitable for scripting) and is still not considered trustworthy:
Re: In Defense of Matlab Code
#16Simulink is great for Rapid Prototyping
Re: In Defense of Matlab Code
#17> # We must reshape X to be a column vector (3,1) > # or rely on broadcasting rules carefully. > Z = Y @ X.reshape(3, 1) Why not use X.transpose()?
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Re: In Defense of Matlab Code
#18> # We must reshape X to be a column vector (3,1) > # or rely on broadcasting rules carefully. > Z = Y @ X.reshape(3, 1) Why not use X.transpose()?
I thought so too, but it doesn't seem to work since X has shape (3,).
This seems to work,
Z = Y @ X[:,np.newaxis]
Re: In Defense of Matlab Code
#19As an engineer, I use Matlab (or rather, Octave the free equivalent) all the time. It's really great for numerical computing and plotting. Most things 'just work', there's a sizeable collection of packages, and I personally like how flexible the function inputs are. Biggest drawback though is that it's over-optimized for matrix math, that it forces you to think about everything as matrices, even if that's not how you…
@mNovak -- super helpful note! Thank you!
Author of RunMat (this project) here --
> The first thing they teach about performant Matlab code is that simple for-loops will tank performance.
Yes! Since in RunMat we're building a computation graph and fusing operations into GPU kernels, we built the foundations to extend this to loop fusion.
That should allow RunMat to take loops as written, and unwrap the matrix math in the computation graph into singular GPU programs -- effectively letting loop written math run super fast too.
Will share more on this soon as we finish loop fusion, but see `docs/fusion/INTERNAL_NOTE_FLOOPS_VM_OPS.md` in the repo if curious (we're also creating VM ops for math idioms where they're advantageous).
> Would love to see something with the convenient math syntax of Matlab, but with broader ease of use of something like JS.
What does "convenient math syntax of Matlab, but with broader ease of use of something like JS" look like to you? What do you wish you could do with Matlab but can't / it doesn't do well with?
Re: In Defense of Matlab Code
#20> # We must reshape X to be a column vector (3,1) > # or rely on broadcasting rules carefully. > Z = Y @ X.reshape(3, 1) Why not use X.transpose()?
Actually, I just tried Y @ X in Numpy and it works just fine. It's because in Python 1-dimensional arrays are actually a thing, unlike in Matlab. That line of code is a non-example; it is easier to make it work in Python than in Matlab.
The result of `Y @ X` has shape (3,), so the next line (concatenate as columns) fails.
To make `Z` a column vector, we would need something like `Z = (Y @ X)[:,np.newaxis]`.
Although, I'm not sure why the author is using `concatenate` when the more idiomatic function would be stack, so the change you suggest works and is pretty clean:
Z = Y @ X
np.stack([Z, Z], axis=1)
# array([[14, 14],
# [32, 32],
# [50, 50]])