Timezone: »
Implementing inference procedures for each new probabilistic model is time-consuming and error-prone. Probabilistic programming addresses this problem by allowing a user to specify the model and then automatically generating the inference procedure. To make this practical it is important to generate high performance inference code. In turn, on modern architectures, high performance requires parallel execution. In this paper we present Augur, a probabilistic modeling language and compiler for Bayesian networks designed to make effective use of data-parallel architectures such as GPUs. We show that the compiler can generate data-parallel inference code scalable to thousands of GPU cores by making use of the conditional independence relationships in the Bayesian network.
Author Information
Jean-Baptiste Tristan (Oracle Labs)
Daniel Huang (Harvard University)
Joseph Tassarotti (Carnegie Mellon University)
Adam Pocock (Oracle Labs)
Stephen Green (Oracle Labs)
Guy L Steele (Oracle Labs)
Related Events (a corresponding poster, oral, or spotlight)
-
2014 Spotlight: Augur: Data-Parallel Probabilistic Modeling »
Thu. Dec 11th 03:10 -- 03:35 PM Room Level 2, room 210
More from the Same Authors
-
2019 Poster: Unlocking Fairness: a Trade-off Revisited »
Michael Wick · Swetasudha Panda · Jean-Baptiste Tristan -
2017 : Posters and Coffee »
Jean-Baptiste Tristan · Yunseong Lee · Anna Veronika Dorogush · Shohei Hido · Michael Terry · Mennatullah Siam · Hidemoto Nakada · Cody Coleman · Jung-Woo Ha · Hao Zhang · Adam Stooke · Chen Meng · Christopher Kappler · Lane Schwartz · Christopher Olston · Sebastian Schelter · Minmin Sun · Daniel Kang · Waldemar Hummer · Jichan Chung · Tim Kraska · Kannan Ramchandran · Nick Hynes · Christoph Boden · Donghyun Kwak