Are Van Der Waals Forces the Similar to Van der Waal Equation? Also, are the things that are being said of Julia generating efficient, platform-specific code things that can also be said of, for instance, Rust and Go? 2. for well-typed julia code (and there’s a number of changes yet in the pipeline). Whereas in Matlab/R/Python it is simply not possible to port low-level kernels from Fortran and get speedups unless you find truly fantastic algorithmic improvements [like going from O(n²) to O(n)] or find a new library routine (usually written in C or Fortran or similar!) Python, Julia, Java, Scala, IDL, Matlab, R, C, Fortran _____ Authors: Jules Kouatchou (jules.kouatchou@nasa.gov)Alexander Medema (alexander.medema@gmail.com)NOTICE: This project is now Open-Source. Anything you do in Julia can be done as fast or faster in Fortran. Why is “1000000000000000 in range(1000000000000001)” so fast in Python 3? Some of the available library code was a bit dodgy, like GARCH estimation which had convergence issues, and there was no code for multivariate G… What am I doing wrong? I would point out that the “1.3 times faster than Fortran” isn’t a claim by Julia Computing, but a blurb from a bank that uses Julia, and is presumably referring to a specific use case. For example, the newest methods for SDEs that I recently published gets about 100x over the “simple standard methods” in easy problems, and I give a real-world example (problem which someone was trying to solve in my lab) where the adaptivity algorithm gives about a 1e6 speedup. That’s huge! here is the Julia wrapper for ARPARK. code. (I included Float64 in quite a few places as I tried Float32 to see if that helped, it didn't most of the time). Julia RPC or MPI) or use threads etc. I would absolutely not be surprised to see a carefully optimized julia application I know nothing about the source of the quote in question, but let me give you a real-world example in which I’ve often found that Julia code can be faster than comparable algorithms in production-quality Fortran: special-function implementations. comes off a bit “fast and loose” and context-free. Figuring out from a map which direction is downstream for a river? There is no need to declare types on functions in Julia, unless you want to use it for multiple dispatch. Update Julia now deprecates [:] (version 0.4.0). Julia is much slower (~44 times slow) than Fortran, the gap narrows but is still significant with 10x more time steps( 0.50s vs 15.24s). Sure, if someone wrote a very large software in Fortran which is perfectly optimal, Julia probably can’t optimize that as well as Fortran because it can alias pointers among other things which can disable some compiler optimizations. Julia is lightning fast. Would be cool to see how Cuba.jl performs in that case. It does not read well to developers because it’s obviously missing details. Julia (Julia-lang) Performance Compared to Fortran and Python, How to write an effective developer resume: Advice from a hiring manager, Podcast 290: This computer science degree is brought to you by Big Tech, “Question closed” notifications experiment results and graduation, MAINTENANCE WARNING: Possible downtime early morning Dec 2/4/9 UTC (8:30PM…, Congratulations VonC for reaching a million reputation. Every performance comparison on stackoverflow I read gets slammed for not being comprehensive/correct/well written/relevant etc. Stack Overflow for Teams is a private, secure spot for you and I can say from experience that the biggest problem in porting R, and before that S, to new architectures was often the need to find a freely available Fortran compiler that was compatible with the local C compiler so that the BLAS/LAPACK code could be compiled. There are ideas about having a cache for this process so that this process becomes instantaneous, but it is not finished yet. I don't know. Why are most helipads in Sao Paulo blue coated and identified by a "P"?