Universal Probabilistic Programming Language Compilation with Parallel Efficient Sequential Monte Carlo Inference

12/01/2021
by   Daniel Lundén, et al.
0

Probabilistic programming languages (PPLs) allow for natural encoding of arbitrary inference problems, and PPL implementations can provide automatic general-purpose inference for these problems. However, constructing inference implementations that are efficient enough is challenging for many real-world problems. Often, this is due to PPLs not fully exploiting available parallelization and optimization opportunities. For example, handling of probabilistic checkpoints in PPLs through the use of continuation-passing style transformations or non-preemptive multitasking – as is done in many popular PPLs – often disallows compilation to low-level languages required for high-performance platforms such as graphics processing units (GPUs). As a solution to this checkpoint problem, we introduce the concept of PPL control-flow graphs (PCFGs), providing a simple and efficient approach that can be used for handling checkpoints in such languages. We use this approach to implement RootPPL: a low-level PPL built on CUDA and C++ with OpenMP, providing highly efficient and massively parallel SMC inference. We also introduce a general method of compiling universal high-level PPLs to PCFGs, and illustrate its application when compiling Miking CorePPL – a high-level universal PPL – to RootPPL. This is the first time a universal PPL has been compiled to GPUs with SMC inference. Both RootPPL and the CorePPL compiler are evaluated through a set of real-world experiments in the domains of phylogenetics and epidemiology, demonstrating up to 6x speedups over state-of-the-art PPLs implementing SMC inference.

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