Hi all,

the next release of Stan should have an expanded Stan OpenCL and thus GPU support. We are currently looking for more real world models that we can test and evaluate the performance of the new backend with.

We are looking for models that:

- use one of the below listed distributions with somewhat large inputs (size of vector or array > 5000)
- take a considerable amount of time to fit (at least an hour or more)
- you can can share the model and data for, even if only via e-mail and not on the forums.

List of supported lpdf/lpmf functions:

- bernoulli_lpmf, bernoulli_logit_lpmf, bernoulli_logit_glm_lpmf
- beta_lpdf, beta_proportion_lpdf
- binomial_lpmf
- categorical_logit_glm_lpmf
- cauchy_lpdf
- chi_square_lpdf
- double_exponential_lpdf
- exp_mod_normal_lpdf
- exponential_lpdf
- frechet_lpdf
- gamma_lpdf
- gumbel_lpdf
- inv_chi_square_lpdf
- inv_gamma_lpdf
- logistic_lpdf
- lognormal_lpdf
- neg_binomial_lpmf, neg_binomial_2_lpmf, neg_binomial_2_log_lpmf, neg_binomial_2_log_glm_lpmf
- normal_lpdf, normal_id_glm_lpdf
- ordered_logistic_glm_lpmf
- pareto_lpdf, pareto_type_2_lpdf
- poisson_lpmf, poisson_log_lpmf, poisson_log_glm_lpmf
- rayleigh_lpdf
- scaled_inv_chi_square_lpdf
- skew_normal_lpdf
- student_t_lpdf
- uniform_lpdf
- weibull_lpdf

Thank you!

To give you a taste of what is to come:

A very simple model with the binomial distribution:

```
data {
int N;
int y[N];
int x[N];
vector[N] w;
}
parameters {
vector[2] beta;
}
model {
beta ~ normal(0,1);
y ~ binomial(x , beta[1] + beta[2] * w);
}
```

is faster on a GPU for N > 10k for a single MCMC chain and for large N, fitting using a GPU is up to 60 times faster (tested using AMD Radeon VII and and i7 CPU. The speedup also increases for multiple chains.