Performance Tips

General Tips

In Julia, high performance can be achieved by following a small set of principles, such as avoiding global variables, avoding heterogenous containers, and placing performance critical code in a function. The same basic principles apply when using SequentialSamplingModels.jl. See the Julia documentation for more details.

Turing

Turing provides three general recommendations for developing performant code:

1. Ensure types are inferable using principles defined in the Julia documentation
2. Use Multivariate distributions in place of Univariate distributions when applicable.
3. Use forward mode automatic differentiation when your model has a small number of parameters (i.e., 5-10), and use reverse mode automatic differentiation for larger models.

See the Turing documentation for more details. Note that the Turing ecosystem provides a benchmarking package, TuringBenchmarking.jl to aid in the selection of an automatic differentiation backend.