On Wednesday, 24 April 2019 at 17:31:03 UTC, jmh530 wrote:
On Wednesday, 24 April 2019 at 16:33:00 UTC, Shigeki Karita
wrote:
[snip]
I see. I'm interested in Stan that is the best library for
probabilistic models but it lacks of GPU computation.
Therefore, I plan to add some probabilistic programming
paradigm into grain like pytorch (pyro) and tensorflow (tf
probability).
Conveniently enough, they just incorporated some GPU support in
the release in March [1]. Here's an earlier status update [2].
The initial work was focused on cholesky decomposition because
that was a big source of slowdown for some types of models.
Probably still has a ways to go before reaching tensorflows
maturity on the GPU.
[1] https://github.com/stan-dev/math/releases/tag/v2.19.0
[2]
https://discourse.mc-stan.org/t/gpu-update-whats-up-and-where-we-are-going/6015
I haven't know that GPU support in Stan. That's Cool! Cholesky
decomposition always suffers me when I use covariance matrix or
something. If you are interested in GPU acceleration in
probabilistic programming, see also this paper (Table 2) of
Edward (previous name of Tensorflow Probability)
https://arxiv.org/pdf/1701.03757.pdf
