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Is Julia the Next Big Programming Language? MIT Thinks So, as Version 1.0 Lands (techrepublic.com)
Julia, the MIT-created programming language for developers "who want it all", hit its milestone 1.0 release this month -- with MIT highlighting its rapid adoption in the six short years since its launch. From a report: Released in 2012, Julia is designed to combine the speed of C with the usability of Python, the dynamism of Ruby, the mathematical prowess of MatLab, and the statistical chops of R. "The release of Julia 1.0 signals that Julia is now ready to change the technical world by combining the high-level productivity and ease of use of Python and R with the lightning-fast speed of C++," says MIT professor Alan Edelman. The breadth of Julia's capabilities and ability to spread workloads across hundreds of thousands of processing cores have led to its use for everything from machine learning to large-scale supercomputer simulation. MIT says Julia is the only high-level dynamic programming language in the "petaflop club," having been used to simulate 188 million stars, galaxies, and other astronomical objects on Cori, the world's 10th-most powerful supercomputer. The simulation ran in just 14.6 minutes, using 650,000 Intel Knights Landing Xeon Phi cores to handle 1.5 petaflops (quadrillion floating-point operations per second).
Obligatory Betteridge's Law Post.
This is my signature. There are many like it, but this one is mine.
There is always going to be argument about which language is the best, which Linux distribution, which web framework, and systemd.
I have been using Crystal and the Lucky Web Framework for a large project, and it's been great. Crystal's handling of types, and the fact that you get all of the error-killing power of tight typing while you often don't have to specify the types at all because they are inferred, has make my code cleaner and easier to write, with fewer bugs and less need for testing. You write it like an interpreted language (it follows Ruby syntax, but treats typing and metaprogramming differently) and it has compiled speed (uses LLVM).
Bruce Perens.
TL; DR: There are a lot of great and nuanced features of Julia, but if you're writing off Julia without understanding the potential power of "multiple dispatch", I'd really encourage you to read up on multiple dispatch and its benefits. Also, if your programming is "laughably mundane", watch out that a machine or machine-learned model doesn't take your job. I'm surprised this got upvoted to a 5. One of the aims of Julia is to 'solve' or 'avoid' the "two-language problem". Interestingly, you just embraced the two-language problem. You can call and use C-code from Python, but that's not necessarily simple. At that point, anything that is "fast" from a computational standpoint now needs someone who's expert at both languages to provide functionality. Most Julia packages provide "fast" code, while allowing any Julia user to help and fix things by learning Julia - i.e. the "one-language satisfaction" ? Julia code is generally "fast" while being very "expressive" - i.e. an aim at trying to avoid the 2-language problem. Crystal looks very interesting and is an "LLVM-language" like Rust and Julia, but I don't see any mention of "N-Dimensional Arrays". To quote another poster, "dealing with ND Arrays with for loops is like pulling teeth". Python has NumPy, but all the good syntax was taken by the time NumPy came along (mid-2000s), so the ND Array syntax can be quite cumbersome with Python, as much as I like the NumPy/SciPy stack. I've been working almost exclusively with Julia over the past 3 months. I'm parsing large-ish (~1GB) binary files generated by arduino boards and a lot of signal analysis work. I'm absolutely loving it, but Julia has been on my radar for a few years now. A few years ago I did a bunch of the Euler challenges with Julia while trying to write as little code as possible and that was a fun exercise to see how powerful some of the syntax is.