Performance tips

By using this package in favor of the naive way of performing Monte-Carlo propagation, you are already likely to to see a performance increase. Nevertheless, there are some things that can increase your performance further. Some of these tips are discussed in greater detail in the paper, "MonteCarloMeasurements.jl: Nonlinear Propagation of Arbitrary Multivariate Distributions by means of Method Overloading".

Consider using StaticParticles and/or sigmapoints

If you want to propagate a small number of samples, less than about 300, StaticParticles are much faster than regular Particles. Above 300 samples, the compilation time starts exploding.

Using Sigma points is a way to reduce the number of samples required, but they come with some caveats and also reduce the fidelity of the propagated distribution significantly.

Use a smaller float type

While performing Monte-Carlo propagation, it is very likely that the numerical precision offered by Float64 is overkill, and that Float32 will do just as well. All forms of AbstractParticles are generic with respect to the inner float type, and thus allow you to construct, e.g., Particles{Float32,N}. Since a large contributing factor to the speedup offered by this package comes from the utilization of SIMD instructions, switching to Float32 will almost always give you a clean 2x performance improvement. Some examples on how to create Float32 particles:

julia> 1.0f0 ∓ 0.1f0
StaticParticles{Float32, 100}
 1.0 ± 0.0999

julia> Particles(Normal(1f0, 0.1f0))
Particles{Float32, 2000}
 1.0 ± 0.1

julia> Particles(randn(Float32, 500))
Particles{Float32, 500}
 0.0532524 ± 1.01

Try GPU particles

If you are propagating a very large number of samples, say 10⁵-10⁷, you may want to try utilizing a GPU. Unfortunately, supporting GPU particles on the main branch has proven difficult since the CuArrays package is a heavy dependency and very hard to test on available CI infrastructure. We thus maintain a separate branch with this functionality. To install it, do

using Pkg
pkg"add MonteCarloMeasurements#gpu"

after which you will have access to a new particle type, CuParticles. These act just like ordinary particles, but perform all computations on the GPU.