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>The goal of Julia is to not have to do that sort of boilerplate/arcane tweaking to get really good performance As someone who is new to python (for bioinforma
by acomjean 11y ago
>The goal of Julia is to not have to do that sort of boilerplate/arcane tweaking to get really good performance
As someone who is new to python (for bioinformatics), and find python is a fine language... but..
The do it "this way not that way" method of implementation of the same algorithms to get it to run fast makes writing performant python a tedious exercise in research and profiling. The article cited suggests Cpython, numby and numpy [1] as ways to make it faster.
Why not C using GPU acceleration as the time spent coding would probably be the same? Thats what I love about plain python, its fast to write and has some good data structures.
I haven't tried Julia, but someday its on my list of languages to learn more about.
[1]https://www.ibm.com/developerworks/community/blogs/jfp/entry/Python_Meets_Julia_Micro_Performance?lang=en https://www.ibm.com/developerworks/community/blogs/jfp/entry...
- dagw 11y agoWhy not C using GPU acceleration as the time spent coding would probably be the same? As someone who does that sort of thing I can assure you it isn't. And when I do use C and GPU acceleration, doing so via cython and pyCUDA (and the myriad of libraries that build on cython and pyCUDA) saves massive amounts of time and effort. That being said I do agree that writing fast python is quite different from writing python, probably more so than in most other languages.