41–50 of 114 posts
BLAS and LAPACK were originally written for FORTRAN, and FORTRAN is particularly suited to numerical computing and array programming. https://modelingguru.nasa.gov/docs/DOC-1762
BLAS and LAPACK were originally written for FORTRAN, and FORTRAN is particularly suited to numerical computing and array programming. https://modelingguru.nasa.gov/docs/DOC-1762
Aren't BLAS and LAPACK just for the linear algebra bits? Numpy is so much more than linear algebra.
For instance, the interface and array/matrix types make vector operations really natural and efficient in python.
Earlier quoted context omitted.
Well that could have been said (and was said) about Numpy/Scipy when it started, "oh R has so many more packages, what numpy can do I can do in MATLAB ...", yet here we are.
You probably don't realize it, you are agreeing with me :-)
I do disagree strongly with the opinion that Numpy is no better than MATLAB :). MATLAB has adopted some Numpy features after Numpy came out (broadcasting for example) but Numpy offered some genuine and unique advantages, both technical (broadcasting, no need for a MEX compiler that I have to pay through my nose for, not restricted to weird naming conventions, nature of parameter passing, ...) and legal.
Earlier quoted context omitted.
Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. But it's growing incredibly fast and there have already been libraries available for a few years that would be really impractical to write and maintain at such a level in C++ for Python. I assume that for science at least Julia will catch on a lot.
> Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. That's not a small issue. The ecosystem is probably the reason people choose NumPy over MATLAB, for example. NumPy is not inherently superior to MATLAB, and most academicians that adopted NumPy in the 2000's already had a MATLAB license, so cost was not a concern either.
For more pure research and prototyping things both can do, I still think matlab is better though I rarely use it. I just like the idea of being able to easily deploy the code later somehow. Kind of an entrepreneurial feature.
Earlier quoted context omitted.
Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. But it's growing incredibly fast and there have already been libraries available for a few years that would be really impractical to write and maintain at such a level in C++ for Python. I assume that for science at least Julia will catch on a lot.
> Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. That's not a small issue. The ecosystem is probably the reason people choose NumPy over MATLAB, for example. NumPy is not inherently superior to MATLAB, and most academicians that adopted NumPy in the 2000's already had a MATLAB license, so cost was not a concern either.
Don't underestimate the impact this has on getting funding or even just tenure/etc recognition for working on numpy. I'm in industry these days, but coming from the academic side, it's _really_ hard to get recognized for building the underlying infrastructure that tons of people use. I've built and maintained libraries that are used in a ton of publications, but was always told my work was "utterly and completely use…
At a talk of his it lead to a very heated discussion where an older professor accused him of wasting government money on such nonsense.
Don't underestimate the impact this has on getting funding or even just tenure/etc recognition for working on numpy. I'm in industry these days, but coming from the academic side, it's _really_ hard to get recognized for building the underlying infrastructure that tons of people use. I've built and maintained libraries that are used in a ton of publications, but was always told my work was "utterly and completely use…
I recall a story where a friend was unable to publish a paper in which he wrote an alternative to a very commonly used commercial tool (that virtually everybody used) with roughly 10 times better performance. He open sourced it and all, it was extremely useful, but there was no new methodology, it was simply very well implemented. At a talk of his it lead to a very heated discussion where an older professor accused h…
By the way, those of that opinion are all professors who wanted me on their labs, but I turned them down...
Earlier quoted context omitted.
> Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. That's not a small issue. The ecosystem is probably the reason people choose NumPy over MATLAB, for example. NumPy is not inherently superior to MATLAB, and most academicians that adopted NumPy in the 2000's already had a MATLAB license, so cost was not a concern either.
I started using python and numpy/scipy back then because it was vastly easier to deploy on a server or supercomputer. The matlab compiler meanwhile is clunky and adds new bugs and additional steps. Julia doesn't really match python in this regard either. For more pure research and prototyping things both can do, I still think matlab is better though I rarely use it. I just like the idea of being able to easily deploy…
Earlier quoted context omitted.
> Theoretically Julia is better for scientific computing, the only issue is its package ecosystem isn't as mature as Python's. That's not a small issue. The ecosystem is probably the reason people choose NumPy over MATLAB, for example. NumPy is not inherently superior to MATLAB, and most academicians that adopted NumPy in the 2000's already had a MATLAB license, so cost was not a concern either.
Well that could have been said (and was said) about Numpy/Scipy when it started, "oh R has so many more packages, what numpy can do I can do in MATLAB ...", yet here we are.
Don't underestimate the impact this has on getting funding or even just tenure/etc recognition for working on numpy. I'm in industry these days, but coming from the academic side, it's _really_ hard to get recognized for building the underlying infrastructure that tons of people use. I've built and maintained libraries that are used in a ton of publications, but was always told my work was "utterly and completely use…
> for over a decade
Probably two even if NumPy came out in 2005