Gosl – Go scientific library
Gosl is a set of tools for developing scientific simulations using the Go language. We mainly consider the development of numerical methods and solvers for differential equations but also present some functions for fast Fourier transforms, the generation of random numbers, probability distributions, and computational geometry.
This library contains essential functions for linear algebra computations (operations between all combinations of vectors and matrices, eigenvalues and eigenvectors, linear solvers) and the development of numerical methods (e.g. numerical quadrature).
We link Gosl with existent libraries written in C and Fortran, such as OpenBLAS, LAPACK, UMFPACK, MUMPS, QUADPACK and FFTW3. These existent libraries have been fundamental for the development of high-performant simulations over many years. We believe that it is nearly impossible to rewrite these libraries in native Go and at the same time achieve the same speed delivered by them. Just for reference, a naive implementation of matrix-matrix multiplication in Go is more than 100 times slower than OpenBLAS.
Installation
TLDR
- Install Docker
- Install Visual Studio Code
- Install the Remote Development extension for VS Code
- Clone https://github.com/cpmech/hello-gosl
- Create your own application within a container (see gif below)
Done. And your system will remain "clean."
Long version
Because we use CGO for linking Gosl with these many libraries, we cannot use the so convenient "go get" functionality for installing Gosl. Moreover, we view Gosl as the most basic set of libraries for high-performance computing and therefore prefer to install Gosl directly alongside Go. In other words, Gosl extends Go with powerful tools for scientific simulations.
Gosl is then copied inside the "/usr/local/go/src" directory right where the Go code itself is located. We have experimented with GOPATH and the newer Go Modules approach, but both do not work well with CGO (and hence Gosl).
Again because of CGO and the other libraries, the easiest way to work with Gosl is via Docker. Having Docker and VS Code installed, you can start developing powerful numerical simulations using Gosl in a matter of seconds. Furthermore, the best part of it is that it works on Windows, Linux, and macOS out of the box.
Nonetheless, if you are using a Debian-based Linux distribution, you basically just need the following (apt) packages: gcc gfortran libopenmpi-dev libhwloc-dev liblapacke-dev libopenblas-dev libmetis-dev libsuitesparse-dev libmumps-dev libfftw3-dev libfftw3-mpi-dev. After the installation of these dependencies, run bash all.bash. However, we do not recommend this approach because with Docker your system will remain "clean."
Documentation
Gosl includes the following essential packages:
- chk. To check numerical results and for unit testing
- io. Input/output including printing to the terminal and handling files
- utl. To generate series (e.g. linspace) and other functions as in pylab/matlab/octave
Gosl includes the following main packages:
- fun. Special functions, DFT, FFT, Bessel, elliptical integrals, orthogonal polynomials, interpolators
- gm. Geometry algorithms and structures
- la. Linear Algebra: vector, matrix, efficient sparse solvers, eigenvalues, decompositions
- mpi. Message Passing Interface for parallel computing
- num. Fundamental numerical methods such as root solvers, non-linear solvers, numerical derivatives and quadrature
- ode. Solvers for ordinary differential equations
- opt. Numerical optimization: Interior Point, Conjugate Gradients, Powell, Grad Descent
- pde. Solvers for partial differential equations (FDM, Spectral, FEM)
- rnd. Random numbers and probability distributions
(see each sub directory for more information)
Examples
Pelase check out https://github.com/cpmech/gosl-examples
Benchmarks
Pelase check out https://github.com/cpmech/gosl-benchmarks