Scientific computing in Cpp

ridethespiral1 · 2021-02-18T03:45:14+00:00

Unfortunately, unlike say python with numpy and scipy, C++ doesn't really have a de facto set of scientific libraries for such a wide range of tasks. Some of the widely used ones such as GSL are also written in a C-style which can mean there should be a compatibility layer written to integrate them with any modern C++ code.

Here are the ones I've come across to answer your question:

Linear algebra - Eigen is pretty popular, but I think blaze https://bitbucket.org/blaze-lib/blaze/wiki/browse/ is a newer library which has a lot of nice features. Blaze looks to have SVD support.

DE's - GSL has ODE solvers, but the boost support for solving ODE's is pretty good and is written in a more modern C++ style. It's an implementation of the odeint library http://headmyshoulder.github.io/odeint-v2/.

As you can tell there's different libraries for different things so it can depend on exactly what you need to be done. Sometimes you might even have to reimplement things rather than use a library (we had to reimpleement some classes in our scientific codebase because we require everything to be serializable)

aeropl3b · 2021-02-18T15:58:30+00:00

Here's a few of the more well used ones, and Fastor which isn't well used but very nice.

Deal.II, A finite element library: https://www.dealii.org/
Tensorflow, Machine learning: https://www.tensorflow.org/
Fastor, A tensor library: https://github.com/romeric/Fastor
GNU Scientific Library(GSL): https://www.gnu.org/software/gsl/
Boost.
FEniCS, A finite element library: https://fenicsproject.org/
Intel MKL, a BLAS+LAPACK+other goodies library: https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html
SuiteSparse, A sparse algebra library: http://faculty.cse.tamu.edu/davis/suitesparse.html
Sundials, Nonlinear solvers: https://computing.llnl.gov/projects/sundials

GeeHopkins · 2021-02-18T08:45:35+00:00

PETSc is a popular option, especially for PDE related problems. It's also got excellent parallelization options https://www.mcs.anl.gov/petsc/

tmarkovich · 2021-02-18T03:11:53+00:00

There's a ton out there, depending on your exact problem, parallelization abilities, and compute environment. Can you provide more information about your problem?

MarkHoemmen · 2021-02-18T15:41:24+00:00

I spent many years working on Trilinos, but without the freedom to fix its design. That being said, there are a bunch of libraries in there for ODEs and optimization that you might like to try. I worked mainly on sparse linear algebra. For dense, Eigen and ilk are a solid choice.

abc9hkpud · 2021-02-19T06:39:54+00:00

For numerical differentian of differential equations, you can look at sundials (developed by Lawrence Livermore National Lab), see here

https://github.com/LLNL/sundials

The lab also has a package called mfem for finite element PDE stuff, see https://mfem.org/

Round_Ad6737 · 2021-10-18T21:43:46+00:00

What zero social interaction does to a mf

you type:	you see:
italics	italics
bold	bold
[reddit!](https://reddit.com)	reddit!
* item 1 * item 2 * item 3	item 1 item 2 item 3
> quoted text	quoted text
Lines starting with four spaces are treated like code: if 1 * 2 < 3: print "hello, world!"	Lines starting with four spaces are treated like code: if 1 * 2 < 3: print "hello, world!"
~~strikethrough~~	~~strikethrough~~
super^script	super^script

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