Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference

Abstract

AbstractProbabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic 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 probabilisticcheckpointsin PPLs through 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 GPUs. To solve the checkpoint problem, we introduce the concept ofPPL control-flow graphs(PCFGs)—a simple and efficient approach to checkpoints in low-level languages. We use this approach to implementRootPPL: 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 ofcompilinguniversal high-level PPLs to PCFGs and illustrate its application when compilingMiking CorePPL—a high-level universal PPL—to RootPPL. The approach is the first to compile a universal PPL to GPUs with SMC inference. We evaluate RootPPL and the CorePPL compiler through a set of real-world experiments in the domains of phylogenetics and epidemiology, demonstrating up to 6$$\times $$×speedups over state-of-the-art PPLs implementing SMC inference.