Array Programming with NumPy • Hacker News Board

Re: Array Programming with NumPy

#111

Post by haihaibye » Sat, Sep 19, 2020, 12:18 AM UTC

Mikhail_K wrote: post #99

teorema wrote: post #3

For some reason this struck me as inappropriate for the outlet. It's a nice piece as an introduction to array programming with numpy, but seemed out of place to me.

It also leads the readers down the dead-end. Python is incapable of parallelism, the only way to badly emulate it is to launch several runtimes. Writing any scientific software in a badly design language when better alternatives exist is wasted time and effort.

It would be nice to have a faster, parallel Python but the best scientific ecosystems are Python and R.

For most scientists, programming productivity matters the most, and plenty of programs are embarassingly parallel.

For instance it's no trouble at all just launching a single threaded Python program once per sequencing sample, and it plays nicely with the supercomputer queuing system.

Re: Array Programming with NumPy

#112

Post by teleforce » Sat, Sep 19, 2020, 8:28 AM UTC

mumblemumble wrote: post #100

enriquto wrote: post #66

Young programmers are slowly but steadily reaching the power that Fortran programmers had 40 years ago. That's good news!

That statement only feels true to me if you interpret the word "power" as an exact synonym for "performance." Which is a definition that is valid, but also just about perfect for leading someone to miss the point. Numpy approaches the performance you could get with Fortran. Mostly because its core is written in Fortran. What Numpy offers that Fortran never did, though, is leverage . The article mentions, but doesn't…

Try D language, with its numerical library you can get both productivity and performance that is even better than the OpenBLAS (Numpy and Julia library are based on) [1].

In D you get the consistency of a single unified language semantic unlike the impedance mismatched approach that is inherent in Python and Numpy programming combination.

[1]http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/...

Re: Array Programming with NumPy

#113

Post by Mikhail_K » Sat, Sep 19, 2020, 1:17 PM UTC

haihaibye wrote: post #111

Mikhail_K wrote: post #99

Earlier quoted context omitted.

It also leads the readers down the dead-end. Python is incapable of parallelism, the only way to badly emulate it is to launch several runtimes. Writing any scientific software in a badly design language when better alternatives exist is wasted time and effort.

It would be nice to have a faster, parallel Python but the best scientific ecosystems are Python and R. For most scientists, programming productivity matters the most, and plenty of programs are embarassingly parallel. For instance it's no trouble at all just launching a single threaded Python program once per sequencing sample, and it plays nicely with the supercomputer queuing system.

> the best scientific ecosystems are Python and R.

That is debatable

Re: Array Programming with NumPy

#114

Post by jofer » Sat, Sep 19, 2020, 10:46 PM UTC

beambot wrote: post #39

teej wrote: post #30

Earlier quoted context omitted.

> my work was "utterly and completely useless I can never understand the arrogance of folks who would say something like this

Academic administrators judge performance based on publication counts, journal impact factors, citations/h-index, and fundraising. Working on tooling doesn't fit in those buckets, so it's broadly-speaking "useless" to a researcher vying for promotions (eg tenure). It's an imperfect method of measuring true impact.

And to be fair, that was the context of the comment. It was meant to be harsh but true advice. It was quite arguably, at the time, worthless in the context of advancing my career. It turned into my career, eventually, but that wasn't the goal then.