# Speed of EnsembleProblem
**URL:** https://discourse.julialang.org/t/speed-of-ensembleproblem/101929
**Category:** General Usage
**Created:** [July 22, 2023, 2:49pm UTC](https://discourse.julialang.org/t/speed-of-ensembleproblem/101929 "2023-07-22T14:49:53Z")
**Posts on this page:** 5
**Page:** 1
### Author:  [@George\_Stepaniants](https://discourse.julialang.org/u/George_Stepaniants)
#### Post date: [July 22, 2023, 2:49pm UTC](https://discourse.julialang.org/t/speed-of-ensembleproblem/101929/1 "2023-07-22T14:49:53Z")
Here I’ve written a simple MRE that generates 100 simulations of a linear ODE with 100 different controls/forcings. I find that simply writing a for-loop to generate this 100 simulations is 10x faster than generating these simulations as an EnsembleProblem. Is there a reason for this or perhaps my code is not optimized?
```julia
cd(@ __DIR__ )
using Pkg
Pkg.activate(".")
using DifferentialEquations
using Random
# Simulate Data
function true_ode(du, u, control, t)
x, y = u
du[1] = -y
du[2] = x
du .+= control(t)
end
tspan = (0.0, 10.0)
Nt = 100
saveat = LinRange(tspan[1], tspan[2], Nt)
u0 = zeros(2)
# number of controlled trajectories
Nc = 100
rng = MersenneTwister(1111)
disc_controls = randn(rng, Nc, Nt)
controls = [linear_interpolation(saveat, disc_controls[i, :]) for i = 1:Nc]
@time begin
sols = zeros(2, Nt, Nc)
prob = ODEProblem(true_ode, u0, tspan, controls[1])
for i = 1:Nc
prob = remake(prob, p=controls[i])
sols[:, :, i] = Array(solve(prob, Tsit5(); saveat=saveat))
end
end
function prob_func(prob, i, repeat)
remake(prob, p=controls[i])
end
ensemble_prob = EnsembleProblem(prob, prob_func=prob_func)
@time begin
sols2 = Array(solve(ensemble_prob, Tsit5(), trajectories=Nc; saveat=saveat))
nothing
end
```
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [July 22, 2023, 3:15pm UTC](https://discourse.julialang.org/t/speed-of-ensembleproblem/101929/2 "2023-07-22T15:15:11Z")
> [@George\_Stepaniants](#):
>
> `ensemble_prob = EnsembleProblem(prob, prob_func=prob_func)`
`ensemble_prob = EnsembleProblem(prob, prob_func=prob_func, safetycopy=false)`
---
### Author:  [@George\_Stepaniants](https://discourse.julialang.org/u/George_Stepaniants)
#### Post date: [July 22, 2023, 3:26pm UTC](https://discourse.julialang.org/t/speed-of-ensembleproblem/101929/3 "2023-07-22T15:26:42Z")
Great! Now they both take approximately 11ms.
---
### Author:  [@George\_Stepaniants](https://discourse.julialang.org/u/George_Stepaniants)
#### Post date: [July 22, 2023, 3:32pm UTC](https://discourse.julialang.org/t/speed-of-ensembleproblem/101929/4 "2023-07-22T15:32:35Z")
@ChrisRackauckas I had a quick follow-up question. What is a good way of combining ODE parameters together with a control input function?
One idea I had was to pass both the parameters and the control input through the ODE parameters argument as a Tuple p = (params, control). Here “control” is a function of time while “params” is a vector. But this causes an issue if I want to then optimize params because a Tuple parameter vector cannot be optimized with sensitivity methods.
Another option is to pass in p = [params; control] as a vector where here “control” is a set of values of the continuous control function saved at discrete time points. But this causes yet another problem, I now need to interpolate the discrete saved values of the control vector inside my ODE function.
Are there any other options to resolve this problem?
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [July 23, 2023, 1:26am UTC](https://discourse.julialang.org/t/speed-of-ensembleproblem/101929/5 "2023-07-23T01:26:10Z")
> [@George\_Stepaniants](#):
>
> One idea I had was to pass both the parameters and the control input through the ODE parameters argument as a Tuple p = (params, control). Here “control” is a function of time while “params” is a vector. But this causes an issue if I want to then optimize params because a Tuple parameter vector cannot be optimized with sensitivity methods.
Indeed that’s a fine way to do it, but it’ll only work with forward mode AD for now. The fix for this should be coming in about a month.