Ferran, I can totally understand Fortran is still the king of fast and efficient numerical calculation in terms of computation time and memory usage. As to "fast", I am comparing Julia main to high-level languages like Python, R, and Matlab which offers similar level of fast prototyping.
Since we are on this topic, I'd like to expand a little. Different users have different needs. As someone trained to be an engineer who can write some code, my desired go-to programming language should have the following: (1) Fast prototyping: I can get a one-time calculation done or try an idea in very short time - most probably I won't ever repeat it so I don't want to spend much time optimizing it. This requires the language to be simple, expressive, yet flexible and powerful. (2) Production ready: However, if something I tried is shown to be useful, I can go back and spend some time to improve it such that the code is good for production. "Improve" mainly means making it fast enough, efficient enough, reliable enough, and user friendly enough. Note the "enough" in every criteria - I won't try my best to make it as fast, efficient, reliable, and user friendly as possible, but only enough to satisfy the use case. (3) Decent speed for both vectorized calculations and brute-force loops in its native form: Some algos are easier to write and understand in vectorized form, others are easier in loops. I'd like to write an algo in the most natural way without frequently stopping to try fitting a square peg in a round hole. Also, even for fast prototyping, the data can be large, and I don't want to wait for hours or even days to see the result. (4) Vast collection of free, high-quality libraries/modules: I think everyone wants this. :-) (5) Open source, most libraries preferably in the same language: Though most of the time I trust other's library, once in a while I may bump into a problem that can only be solved if I can peek under the hood and even tweak a thing or two. It's not just about source code - I want to be able to run my application and trace into the library for debugging, as many subtle bugs can only be revealed when you watch things running. (6) Easy to collaborate: This requires the language to be simple to pick up - someone can learn enough to start using it in a couple days while learning more advanced features. Code written in it should be easy to read, and most of the time there shouldn't bee too many (obscure) ways to do the same thing. Also it needs to be free: it's hard enough to persuade someone else to learn a new programming language, not to mention persuading anyone to pay hundreds of dollars for using it. (7) Can scale up to a bigger project with many contributors: The language has enough protections so: (a) Compiler can check for mistakes as much as possible (with some help from the programmer), as run-time errors are annoying and costly; (b) Things can be modularized and collaborators won't easily step on each other's toes (style guide is nice but reinforcement is sometimes necessary). (8) Cross-platform: Windows and Linux support at the minimum. (9) Talk to other main-stream languages easily: There will always be a time when one really needs to use a library in C/C++/Fortran/Python/R or even Java/.NET that one desires to use. The wishlist is long and demanding. :-) For many of the above reasons I was a huge fan of Python. However, Python is way too slow at loops (Cython is not convenient as I often need to access objects and other libraries inside the loops); many errors can only show up in run-time (imagine running an algo for hours and then stop due to one type mismatch - ); and it's scarily easy to change class definition (Programmer A and B can both decide to add a member variable "n" to the object of the same class at run time! And sometimes this is done without the programmer even knowing it, as he/she may think the member variable already exists.); also a lot of powerful Python libraries are written in C/C++, so though they are open source they are effectively "black box" to me unless I am willing to invest a lot of time to dig into the gear box. So far I see Julia solving several major pain points in Python for me. I'd love to learn about it and use it everyday, and possibly persuade some friends to pick it up, too. :-) My main worry is would Julia is still a very young and somewhat specialized language with limited user base. I hope it will grow to surpass the critical mass for it to become main stream. Thanks, -Zhong On Tuesday, July 12, 2016 at 2:45:33 AM UTC-5, Ferran Mazzanti wrote: > > ...but then is Julia *really* fast ? I guess that depends on what you > compare with. I've been doing lots of numerics for the last 20+ years and > for that I use fortran. This is *damn* fast, even more if you use something > like the Intel compiler. Fortran many times performs better than C, and the > benchmarks says that Julia is 1.6 times slower than C whichm froma > performance point of view, is not a minor factor. So should I understand > that Julia is about1.6-1.8 times slower than fortran? Then depending on > what you call fast, Julia is fast :) >
