# StackOverflowError for large set of Jumps in JumpProcesses.jl with high-dimensional set of ODEs
**URL:** https://discourse.julialang.org/t/stackoverflowerror-for-large-set-of-jumps-in-jumpprocesses-jl-with-high-dimensional-set-of-odes/112969
**Category:** General Usage
**Tags:** question, numerics, differentialequation
**Created:** [April 15, 2024, 3:27pm UTC](https://discourse.julialang.org/t/stackoverflowerror-for-large-set-of-jumps-in-jumpprocesses-jl-with-high-dimensional-set-of-odes/112969 "2024-04-15T15:27:57Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@flmuk](https://discourse.julialang.org/u/flmuk)
#### Post date: [April 15, 2024, 3:27pm UTC](https://discourse.julialang.org/t/stackoverflowerror-for-large-set-of-jumps-in-jumpprocesses-jl-with-high-dimensional-set-of-odes/112969/1 "2024-04-15T15:27:57Z")
Hello,
I have to simulate a large (n=1000) set of simple ODEs, and each ODE gets an independent realisation of Poisson input.
When trying to set up the JumpProblem as in [the documentation](https://docs.sciml.ai/JumpProcesses/stable/tutorials/discrete_stochastic_example/#Adding-Jumps-to-a-Differential-Equation).
However, when the number of dynamical variables is large somehow a StackOverflowError is raised when constructing the problem:
```julia
using DifferentialEquations, JumpProcesses
using StatsBase
# Define the ODE system
function large_ode_system!(du, u, p, t)
n = length(u)
for i in 1:n
du[i] = - u[i]
end
end
# Define initial conditions and parameters
n = 1000 #n=50 works
u0 = ones(n)
tspan = (0.0, 1.0)
rate(u, p, t) = 1. #in Hz
function create_jump_affect(affected_id)
function jump_affect_by_id!(integrator)
refractory = sample(1:n, 10)
if !(affected_id in refractory)
integrator.u[affected_id] += 1.
end
nothing
end
return jump_affect_by_id!
end
# Define the problem
prob = ODEProblem(large_ode_system!, u0, tspan, p)
# Generate individual jumps
jumps = ConstantRateJump[]
for i in 1:n
push!(jumps,ConstantRateJump(rate, create_jump_affect(i)))
end
# Create jump set
try
jumpset_1 = JumpSet(jumps...);#gives StackOverflowError (because of recursion)?
catch error
println(error)
finally
nothing
end
########################################################
#alternative: create jump set 'manually':
function create_jump_set(constant_jumps::Vector{ConstantRateJump})
# Create a JumpSet with the provided constant rate jumps and empty/nil for other jump types
return JumpSet((), tuple(constant_jumps...), nothing, nothing)
end
jumpset_2 = create_jump_set(jumps);#works fine
# Manual handling of multiple jumps (conceptual, adjust based on actual usage)
jump_prob = JumpProblem(prob, Direct(), jumpset_2)
# Solve the problem
sol = solve(jump_prob, ImplicitEuler(), saveat=0.1) #now this gives a StackOverflowError ('Internal error: stack overflow in type inference of solve!')
```
How can one get around this? Any suggestion is more than welcome!
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [April 15, 2024, 4:16pm UTC](https://discourse.julialang.org/t/stackoverflowerror-for-large-set-of-jumps-in-jumpprocesses-jl-with-high-dimensional-set-of-odes/112969/2 "2024-04-15T16:16:41Z")
Try `DirectFW()` for large numbers of jumps.