Oracle International Corporation

Joseph Tassarotti

Guy Lewis Steele, Jr

Jean-Baptiste Tristan

Daniel E Huang

The present invention relates to a probabilistic programming compiler that (a) generates data-parallel inference code to sample from probability distributions in models provided to the compiler; and (b) utilizes a modular framework to allow addition and removal of inference algorithm information based on which the compiler generates the inference code. For a given model, the described compiler can generate inference code that implements any one or more of the inference algorithms that are available to the compiler. The modular compiler framework utilizes an intermediate representation (IR) that symbolically represents features of probability distributions. The compiler then uses the IR as a basis for emitting inference code to sample from the one or more probability distributions represented in the IR. Further, the compiler produces parallelized inference code that facilitates efficient parallel processing of inference computations in order to take advantage of highly data-parallel architectures, such as GPUs.

Data-parallel probabilistic inference

Herein is described a data-parallel and sparse algorithm for topic modeling. This algorithm is based on a highly parallel algorithm for a Greedy Gibbs sampler. The Greedy Gibbs sampler is a Markov-Chain Monte Carlo algorithm that estimates topics, in an unsupervised fashion, by estimating the parameters of the topic model Latent Dirichlet Allocation (LDA). The Greedy Gibbs sampler is a data-parallel algorithm for topic modeling, and is configured to be implemented on a highly-parallel architecture, such as a GPU. The Greedy Gibbs sampler is modified to take advantage of data sparsity while maintaining the parallelism. Furthermore, in an embodiment, implementation of the Greedy Gibbs sampler uses both densely-represented and sparsely-represented matrices to reduce the amount of computation while maintaining fast accesses to memory for implementation on a GPU.

Growing community of inventors

Pittsburgh, PA, United States of America

Joseph Tassarotti