Measurements.jl VS ITensors.jl

Aside from the language features, some of the libraries in Julia make it really useful for statistical computing. One really cool library I am trying to use more and more in Julia is the Measurements library [1]. With the multiple dispatch system in Julia its super easy to integrate into most problems and can let you estimate error bounds on values programs produce. Super important for scientific applications.

I am hoping in the future that I can mix this in with some auto-diff problems to get uncertainty bounds on estimation problems with minimal fiddling with covariance matrices. Right now the performance is the only problem in integrating the library into pretty much any problem :(

[1] https://github.com/JuliaPhysics/Measurements.jl

DMRG packages are available in Julia and C++ and Python. (Don't use Fortran. But here is a Fortran library if you insist.)

As one example, one of the leading libraries for tensor network simulations (https://itensor.org) has recently been rewritten in Julia (previously was c++) and the flatiron institute who develops it (which is certainly one of the leading Computational physics institutions in the world) is advising new users to use the Julia version. I also know some other computational groups which use Julia, even for things like quantum Monte Carlo (where I personally would have believed c++ to have an edge but people tell me different)! I think when even leading computational groups switch, Julia is almost always the much better option for the average user if you write your code from scratch (a situation not so rare in condensed matter). If you need to use some libraries or legacy code, this obviously changes the situation.

> One thing that supports this view is that there are several Julia packages that are wrappers around existing C/Fortran/C++ libraries, and basically no examples (that I know) of people porting existing libraries to Julia.

As with the others, I'll strongly disagree and chime in with a few examples off the top of my head:

* ITensors.jl : They started moving from a C++ to Julia a couple years ago and now their webpage doesn't even mention their original C++ implementation on its homepage anymore https://itensor.org/

* DifferentialEquations.jl : This has many state of the art differentiatial equation solving facilities in it, many of which are improvements over old Fortran libraries.

* SpecialFunctions.jl, Julia's own libm, Bessels.jl, SLEEFPirates.jl : Many core math functions have ancient Fortran or C implementations from OpenLibm or whatever, and they're being progressively replaced with better, faster versions written in pure julia that outperform the old versions.

Otherwise, maybe check ITensors.jl or look for packages that want to do the same thing?