I have a reasonably optimized ODE problem where I used callback for terminating the integrator when some values get too large. Without callback I have (43 allocations: 16.23 KiB), and with callback I have (42,065,928 allocations: 3.13 GiB). The callback takes more than 40% of the time (see below).
Here’s a minimal working example using the lorenz problem:
function lorenz!(du,u,p,t)
du[1] = 10.0*(u[2]-u[1])
du[2] = u[1]*(28.0-u[3]) - u[2]
du[3] = u[1]*u[2] - (8/3)*u[3]
end
u0 = [1.0;0.0;0.0]
tspan = (0.0,100.0)
prob = ODEProblem(lorenz!,u0,tspan)
function condition(out, u, t, integrator)
out[1] = u[1] - 25000;
out[2] = u[2] - 0.2;
out[3] = u[3] - 1.2;
end
function affect!(integrator, index)
if (index == 1)
# ...
# terminate!(integrator);
elseif (index == 2)
# ...
# terminate!(integrator);
elseif (index == 3)
# ...
# terminate!(integrator);
end
end
cellFate = VectorContinuousCallback(condition, affect!, nothing, 3, save_positions=(false, false));
@btime solve(prob,Tsit5()) # no callback
@btime solve(prob,Tsit5(), callback=cellFate) # with callback
In this example, without callback it takes 689.839 μs (11748 allocations: 1.37 MiB), and with callback it takes 2.161 ms (24628 allocations: 2.34 MiB). I don’t understand why the callback function could have so many allocations.
