I’m curious if there is a future in which Stan is able to compute its lp in a GPU accelerated fashion. GPUs are a no-go for smaller models, but for example, large-ish time series models there is enough parallelism, along at least one dimension, to benefit. For example, when fitting a very long nonl…

[image] Stan on the GPU Project Proposals Stan on the GPU using OpenCL/ViennaCL We have started discussing this topic in an e-mail thread with Bob and Steve and we decide to move the discussion to the forums. Quick intro: Me and Erik Štrumbelj from University of Ljublja…

One of the big problems with GPU speedups for us is that we are almost certaintly going to need more than single precision. And it’s hard with our architecture to preload and reuse data. So we’re first looking at low data flow, high compute cycle operations like Cholesky decomposition (quadratic …

in other words, very much the same kind of high-level design as TensorFlow uses as it’s the obvious way to cut a differentiable function apart what about STALINGRAD ? Wouldn’t it require also an optimization compiler of the graph with an “load balancer” to shift the calculation efficiently. Optio…

I don’t know anything about the Pearlmutter and Siskind implementation. If you do reverse mode autodiff, the condition is that all the parents of a node have to have passed gradients down before it can pass gradients down. This is solved in most system by keeping a stack of the expression graph wh…

[image] Bob_Carpenter: the CAR case study Mitzi just did ( http://mc-stan.org/documentation/case-studies/IAR_Stan.html ); she started with Max Joseph’s CAR case study: http://mc-stan.org/documentation/case-studies/mbjoseph-CARStan.html These case studies have moved: [CAR Exact sparse CAR mod…

GPU Stan

Algorithms

bgoodri May 23, 2017, 1:57pm 2

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Integrating GPU support Developers	23	5854	January 31, 2017
Stan on the GPU Project Proposals	16	8745	August 10, 2018
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GPU Stan

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