I’m preallocating an array for the solution of an ODE at each time step, for post-processing. However, `julia --track-allocation=user foo.jl`

still reports large allocations. To simplify with `@btime`

:

```
using DifferentialEquations
using BenchmarkTools
function f(du,u,p,t)
for i = 1:10
du[i] = -u[i]*sin(i) + 1*im
end
end
u0 = [cos(i)+im*sin(i) for i = 1:10]
tf = 10.0
tspan = (0.0, tf)
prob = ODEProblem(f, u0, tspan)
sol = solve(
prob,
Vern8(),
saveat = 0.0:.01:tf,
)
sol_tmp = similar(u0)
ns = Int(tf/.01)
@btime for sol_i in sol.u
copyto!(sol_tmp, sol_i)
end
@btime for i in 1:length(sol.u)
copyto!(sol_tmp, sol.u[i])
end
@btime copyto!(sol_tmp, sol.u[1])
```

returns

```
66.102 μs (1492 allocations: 38.95 KiB)
110.124 μs (1983 allocations: 46.64 KiB)
65.983 ns (0 allocations: 0 bytes)
```

note that accessing via iterator (1st approach) is ~1.6x faster here than accessing by index (2nd approach); a difficult thing for me to understand is why `@btime copyto!(sol_tmp, sol.u[1])`

does not allocate while it allocates in loop. Note that in loops, normal arrays do not allocate with `copyto!`

:

```
a = [rand(3,3) for i = 1:1000];
b = rand(3,3)
@btime for i in 1:1000
copyto!($b,$a[i])
end
```

gives

```
4.603 μs (0 allocations: 0 bytes)
```