DiffEqGPU.jl
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SciML
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
See [here](https://github.com/SciML/DiffEqGPU.jl/blob/master/src/DiffEqGPU.jl#L410). It is trying to get a field `f` from a `DynamicalODEFunction` (at least in my case) and this does not exist. Looks like this particular object has `f1`...
- [ ] Requires https://github.com/SciML/SciMLBase.jl/pull/72 - [ ] Needs a SciMLBase release and to then increment the SciMLBase version. - [ ] Can't test locally, so then needs tests to...
With ROCKernels being merged into KernelAbstractions, we should soon be able to integrate AMDGPU support into this package! @ChrisRackauckas if this is desired, how would you like me to do...
@FerencHegedus @nnagyd have been putting a lot of work into tuning ensemble GPU parallel CUDA kernel explicit solvers for non-stiff ODEs so we should probably should make use of that....
It would be helpful to apply GPUs for ensemble studies with the delay and stochastic delay problems.
We will need to think about this one. Actually terminating will cause the others not to finish. I think that terminate should just set an array to remember the time,...
Solving a matrix differential equation yields a matrix solution (as expected) on a CPU, but on a GPU a vector solution is returned. Suppose that the following is executed on...
```julia using DiffEqGPU, OrdinaryDiffEq pa = [1.0] u0 = [3.0] function f(du,u,p,t) du[1] = 1.01 * u[1] * p[1] end function f2(du,u,p,t) du .= 1.01 .* u .* p end...
Full MWE ``` using OrdinaryDiffEq using DifferentialEquations.EnsembleAnalysis using DiffEqGPU using CuArrays Base.@propagate_inbounds function lotka_volterra2(du,u,p,t) x,y = u du[1] = p[1]*x - p[2]*x*y du[2] = -p[3]*y + p[4]*x*y return nothing end...
```julia using OrdinaryDiffEq, DiffEqGPU, Test, Unitful function lorenz(du,u,p,t) @inbounds begin du[1] = p[1]*(u[2]-u[1]) du[2] = u[1]*(p[2]-u[3]) - u[2] du[3] = u[1]*u[2] - p[3]*u[3] end nothing end u0 = [1f0u"m";0u"m";0u"m"] tspan...