# Potentially Getting Started With DifferentialEquations.jl

**URL:** <https://discourse.julialang.org/t/potentially-getting-started-with-differentialequations-jl/84772>\
**Category:** New to Julia\
**Tags:** gpu\
**Created:** [July 25, 2022, 6:59pm UTC](https://discourse.julialang.org/t/potentially-getting-started-with-differentialequations-jl/84772 "2022-07-25T18:59:00Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![yoshi](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/yoshi/32/38259_2.png) [@yoshi](https://discourse.julialang.org/u/yoshi)\
**Post date:** [July 25, 2022, 6:59pm UTC](https://discourse.julialang.org/t/potentially-getting-started-with-differentialequations-jl/84772/1 "2022-07-25T18:59:00Z")

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I’m trying to assess whether Julia can be used for my use case. I have a ~25 dimensional differential equation whose solutions I’d to analyze for a variety of initial conditions and choices of parameters. As such, I have quite a few simulations to run. Is there a way to use Julia on GPUs to parallelize this task?

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**Author:** ![gbaraldi](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gbaraldi/32/22101_2.png) [@gbaraldi](https://discourse.julialang.org/u/gbaraldi)\
**Post date:** [July 25, 2022, 7:05pm UTC](https://discourse.julialang.org/t/potentially-getting-started-with-differentialequations-jl/84772/2 "2022-07-25T19:05:49Z")

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Yes there is a way, I don’t the exact details but I believe [GitHub - SciML/DiffEqGPU.jl: GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem](https://github.com/SciML/DiffEqGPU.jl) is the way to do it :). Also here for some examples [Parallel Ensemble Simulations · DifferentialEquations.jl](https://diffeq.sciml.ai/stable/features/ensemble/)