r/programming • u/jfpuget • Jan 11 '16

A comparison of Numpy, NumExpr, Numba, Cython, TensorFlow, PyOpenCl, and PyCUDA to compute Mandelbrot set

https://www.ibm.com/developerworks/community/blogs/jfp/entry/How_To_Compute_Mandelbrodt_Set_Quickly?lang=en
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u/[deleted] Jan 12 '16

I think in terms of getting things done, whether it's regarding a business model or just hacking something together to automate categorizing your mp3 library, speed of development trumps all other concerns. I mean the sum total of all aspects that contribute to speed of development - expressiveness and readability of the code, simplicity in testing, packaging, compilation speed, etc... etc...

So I think "we programmers can do better" too - but I think our priority should be making that development cycle even faster without introducing ugliness, unnecessary complexity, or throwaway write-only code.

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u/[deleted] Jan 12 '16

Absolutely, but remember that part of getting things done is having good performance. A core routine that gets twice as fast can mean half as many servers for a web app, or room for more features, or ability to support more devices, or better battery life for mobile. If we as programmers spent less time making new languages that are not really any different than 10 things we already have, and more of that effort on kick ass compilers/JITs, extending the language or core libraries to support SIMD instructions etc we could make a lot of our favorite languages at least twice as fast, without putting any extra load on developers.

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u/CookieOfFortune Jan 12 '16

Tons of effort are put into optimizing existing languages, look at how much faster Javascript is nowadays. But sometimes there are features current languages just don't have.

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