Our great sponsors
-
ClimateMachine.jl
Discontinued Climate Machine: an Earth System Model that automatically learns from data
-
BlockBandedMatrices.jl
A Julia package for representing block-banded matrices and banded-block-banded matrices
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
SciMLBenchmarks.jl
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
-
ParallelKMeans.jl
Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm
There are some big projects using all of this. The CLIMA climate model is a good example, and you can find 20 other fluid dynamics simulators. There's differentiable N-body simulators, rigid body and robotics simulators, DFT libraries, etc.
Of course, (GPU-based) numerical linear algebra, complex numbers, etc. is all there. There's a bunch of fancy special matrix libraries for faster PDE calculations that can be mixed with all sorts of solvers, etc.
Most of the SciML organization is dedicated to research and production level scientific computing for domains like physical systems, chemical reactions, and systems biology (and more of course). The differential equation benchmarks are quite good in comparison to a lot of C++ and Fortran libraries, there's modern neural PDE solvers, pervasive automatic differentiation, automated GPU and distributed parallelism, SDE solvers, DDE solvers, DAE solvers, ModelingToolkit.jl for Modelica-like symbolic transformations for higher index DAEs, Bayesian differential equations, etc. All of that then ties into big PDE solving. You get the picture.
Once upon a time we implemented kmeans algorithm in our Julia and it outperform c implementations by a large amount. The reason for that is not that Julia is faster (which is not), but mainly because we were able to better utilize resources that we have. One can rewrite our Julia solution in c and get better timings, but I guess this solution is not as obvious from the c perspective. Package in question is https://github.com/PyDataBlog/ParallelKMeans.jl