Memory usage while solving `JumpProblem` increases with run time, even with `save_positions=(false,false)`

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I rewrote my code to not use a Symbolics @reaction_network, similar to your implementation, but I do see the increase in memory allocations still (albeit a bit less)…

Code

(I didn’t want to hide your discussion in my long functions, so here’s a fold:

See code details here 
function catalystplasmiddirect(;T = 100.0)    
    function rate(η,X,Y,K)
        return η*(1-(X+Y)/K)
    end

    r1(u,p,t) = rate(p[1],u[1],u[2],p[2])*u[1]
    r2(u,p,t) = rate(p[1],u[2],u[1],p[2])*u[2]
    r3(u,p,t) = p[3]*u[1]
    r4(u,p,t) = p[3]*u[2]
    r5(u,p,t) = p[4]*u[1]*u[2]
    r6(u,p,t) = p[5]*u[2]
    aff1!(integrator) = integrator.u[1] += 1
    aff2!(integrator) = integrator.u[2] += 1
    aff3!(integrator) = integrator.u[1] -= 1
    aff4!(integrator) = integrator.u[2] -= 1
    function aff5!(integrator)
        integrator.u[1] -= 1
        integrator.u[2] += 1
    end
    function aff6!(integrator)
        integrator.u[1] += 1
        integrator.u[2] -= 1
    end
    #    η    K    μ    γ     ρ
    p = (1.0, 1e4, 0.1, 1e-4, 0.01)
    u0 = [1000,10]
    tspan = (0.0, T)

    dprob = DiscreteProblem(u0, tspan, p)
    jprob = JumpProblem(
        dprob, Direct(),
        ConstantRateJump(r1,aff1!), ConstantRateJump(r2,aff2!), ConstantRateJump(r3,aff3!),
        ConstantRateJump(r4,aff4!), ConstantRateJump(r5,aff5!), ConstantRateJump(r6,aff6!);
        save_positions=(false,false)
    )
    return jprob
end

And the benchmarks:

julia> long_prob = catalystplasmiddirect(T=1e5)
JumpProblem with problem DiscreteProblem with aggregator Direct
Number of jumps with discrete aggregation: 6
Number of jumps with continuous aggregation: 0
Number of mass action jumps: 0


julia> long_sol = solve(long_prob, stepper)
retcode: Success
Interpolation: Piecewise constant interpolation
t: 2-element Vector{Float64}:
      0.0
 100000.0
u: 2-element Vector{Vector{Int64}}:
 [1000, 10]
 [98, 8850]

julia> @btime solve($long_prob, $stepper)
  11.082 s (195816952 allocations: 2.92 GiB)
retcode: Success
Interpolation: Piecewise constant interpolation
t: 2-element Vector{Float64}:
      0.0
 100000.0
u: 2-element Vector{Vector{Int64}}:
 [1000, 10]
 [96, 8951]

julia> short_prob = catalystplasmiddirect(T=1e2)
JumpProblem with problem DiscreteProblem with aggregator Direct
Number of jumps with discrete aggregation: 6
Number of jumps with continuous aggregation: 0
Number of mass action jumps: 0

julia> short_sol = solve(short_prob, stepper)
retcode: Success
Interpolation: Piecewise constant interpolation
t: 2-element Vector{Float64}:
   0.0
 100.0
u: 2-element Vector{Vector{Int64}}:
 [1000, 10]
 [92, 8892]

julia> @btime solve($short_prob, $stepper)
  10.677 ms (204394 allocations: 3.12 MiB)
retcode: Success
Interpolation: Piecewise constant interpolation
t: 2-element Vector{Float64}:
   0.0
 100.0
u: 2-element Vector{Vector{Int64}}:
 [1000, 10]
 [118, 8933]