I went ahead and ran some stuff on my laptop.

    using DifferentialEquations

    van = @ode_def_noinvhes VanDerPol2 begin
      dy = μ*((1-x^2)*y - x)
      dx = 1*y
    end μ=>1.

    prob = ODEProblem(VanDerPol2(μ=1e6),[0;2.],(0.0,160.0))
  
  
  solve(prob,CVODE_BDF(),reltol=1/10^5,save_everystep=false)
   solve(prob,Rodas5(),reltol=1/10^5,save_everystep=false)


    @time sol1 = solve(prob,CVODE_BDF(),reltol=1/10^5,save_everystep=false)
    @time sol2 = solve(prob,Rodas5(),reltol=1/10^5,save_everystep=false)
    sol3 = solve(prob,CVODE_BDF(),abstol=1/10^10,reltol=1/10^10)

On my laptop I'm getting ~0.1 seconds for both, with Sundials being a little less accurate and OrdinaryDiffEq.jl being a little more accurate against the 10^-10 reference solution than the reported Intel. But "spot checking" like this makes it hard to just timings without issues of more/less error involved, which is why more sophisticated benchmarks use work-precision diagrams. So the timings are in the same ballpark as Intel and I'd assume my (not so fast) laptop doesn't match what you benchmarked with, the point being that MATLAB is just slow here instead of Intel being unbelievably exceptional (though admirable). Still, maybe Intel does pull off some extra efficiency here, but then of course no method is universally good so I wouldn't expect that everywhere. In the end, it's probably a well optimized (probably in the DiffEq.jl/Sundials/FATODE range) single method without event handling or other extra features, but importantly it is abandoned. Probably would've been interesting if development kept up, but without it I think I'm just not going to try to get it installed.

Thanks for pointing me to it though. It's definitely an interesting nugget of ODE history that I didn't know existed.