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Top 7 Julia High Performance Projects
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OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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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.
There has been a lot of research in Runge Kutta methods in the last couple decades which resulted in all kind of specialized Runge Kutta methods. You have high order ones, RK methods for stiff problems, embedded RK methods which benefit from adaprive step size control, RK-Nystrom methods for second order Problems, symplectic RK methods which preserve energy (eg. hamiltonian) ando so on. If you are interested in the numerics and the use cases I highly recommend checking out the Julia Libary OrdinaryDiffEq (https://github.com/SciML/OrdinaryDiffEq.jl). If you look into the documentation you find A LOT of implemented RK methods for all kind of use cases.
Julia is great https://github.com/JuliaData or https://github.com/sl-solution/DLMReader.jl might be a good startingpoint
Julia High Performance related posts
- Symbolics.jl
- What packages would you like Julia to have?
- Converting Symbolics.jl Objects to SymPy.jl Objects
- Error With StaticArrays Module & Symbolics.jl
- ModelingToolkit over Modelica
- Parallélisation distribuée presque triviale d’applications GPU et CPU basées sur des Stencils avec…
- IA et Calcul scientifique dans Kubernetes avec le langage Julia, K8sClusterManagers.jl
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A note from our sponsor - SaaSHub
www.saashub.com | 20 Apr 2024
Index
What are some of the best open-source High Performance projects in Julia? This list will help you:
Project | Stars | |
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1 | Symbolics.jl | 1,285 |
2 | OrdinaryDiffEq.jl | 498 |
3 | Transducers.jl | 423 |
4 | ThreadsX.jl | 308 |
5 | FLoops.jl | 302 |
6 | FoldsCUDA.jl | 54 |
7 | DLMReader.jl | 26 |
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