ProbabilityModels.jl

> here is the github: https://github.com/chriselrod/ProbabilityModels.jl. In the example I give there, the logdensity and gradient evaluation was about 25x faster than Stan, and sampling was about 20x faster. that looks pretty cool, though I don't yet know enough Julia to understand all of it. The speedups make sense given that Stan's compiler/math lib doesn't do much in the way of smart data layout. I would still keep in mind that the metric worth using for benchmarking is the number of effective samples per second, and this also depends on the HMC variant you use. > Eigen's fixed size arrays to get about 3-8x worse performance than my Julia library seems unsurprising that Julia can specialize a lot better than verbose C++ templating, no? (still, good job, very worth checking out) > I was getting errors when specifying -DEIGEN_ENABLE_A

I've been using Julia. I've been working on a front end meant to help specify vectorized models and their gradients. It is alpha-quality software (far from production ready), but here is the github: https://github.com/chriselrod/ProbabilityModels.jl In the example I give there, the logdensity and gradient evaluation was about 25x faster than Stan, and sampling was about 20x faster. A simulation fitting many data sets for my dissertation took about 9 hours. 20x is the difference between running overnight, and taking a week. If I understand correctly, one problem Stan has is that it uses a var datatype for its arrays, which interleaves the values (Scalar) with pointers (vi_). https://github.com/stan-dev/math/blob/master/stan/math/rev/c... This interleaving is going to cause problems to an autovectorizer. To get a SIMD vector of the scalars,