●Stories
●Firehose
●All
●Popular
●Polls
●Software
●Thought Leadership
Submit
●
Login
●or
●
Sign up
●Topics:
●Devices
●Build
●Entertainment
●Technology
●Open Source
●Science
●YRO
●Follow us:
●RSS
●Facebook
●LinkedIn
●Twitter
●
Youtube
●
Mastodon
●Bluesky
Want to read Slashdot from your mobile device? Point it at m.slashdot.org and keep reading!
Forgot your password?
Close
This discussion has been archived.
No new comments can be posted.
Load All Comments
Full
Abbreviated
Hidden
/Sea
Score:
5
4
3
2
1
0
-1
More
Login
Forgot your password?
Close
Close
Log In/Create an Account
●
All
●
Insightful
●
Informative
●
Interesting
●
Funny
The Fine Print: The following comments are owned by whoever posted them. We are not responsible for them in any way.
bytonique ( 1176513 ) writes:
An anecdote: I wanted to try some maths testing large numbers, using arbitrary precision. The calculation involves a formula for integers (n) for each integer (r=3-1,000,000) and then calculating another value and testing whether that is an integer. Perl could calculate n=1-10,000 in 10 seconds, and Python (under Sage [sagemath.org]) could calculate n=1-10,000 in 12 seconds. Julia, which I certainly don't know the best practices of, could calculate n=1-1,000,000 in 4 seconds. My recommendation is that if you need lots of
byAmbassador Kosh ( 18352 ) writes:
In Python if you need high performance the standard is to use things like numpy, scipy, tensorflow etc. Basically, don't reinvent the wheel. There is even stuff like cython to code some performance critical parts in c which don't already have high performance versions.
byg01d4 ( 888748 ) writes:
Basically, don't reinvent the wheel
Exactly. Scientific programming languages are a collection of built-in routines that easily integrate with those written externally. Some of the more basic languages used for scientific programming were ones that quickly built up a large external collection creating something of a network effect. A lot of code in a scientific application consists of glue that binds these routines together. I think in large part the availability of external routines is what drives popularity rather than the elegance of a language's design.
Parent
twitter
facebook
byAmbassador Kosh ( 18352 ) writes:
Part of the reason I used Python for my PhD work is so many high performance libraries available. You can use numpy and scipy backed by MKL, Tensorflow with CUDA etc. Most of the performance ends up in the low level code and Python essentially ends up for command and control. We even use python on the supercomputers here and see no real performance impact from doing it.
Since the performance ends up pretty much the same as coding it all in C++, C or Fortran but written far faster and more maintainable it is
There may be more comments in this discussion. Without JavaScript enabled, you might want to turn on Classic Discussion System in your preferences instead.
Slashdot
●
●
Submit Story
It is much harder to find a job than to keep one.
●FAQ
●Story Archive
●Hall of Fame
●Advertising
●Terms
●Privacy Statement
●About
●Feedback
●Mobile View
●Blog
Do Not Sell or Share My Personal Information
Copyright © 2026 Slashdot Media. All Rights Reserved.
×
Close
Working...
Working...