# Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

## TL;DR

This paper introduces Etalumis, a scalable probabilistic programming framework that integrates with scientific simulators, enabling large-scale Bayesian inference using MCMC and deep learning on supercomputers.

## Contribution

It presents a novel PPL framework that couples directly to existing simulators and demonstrates large-scale Bayesian inference with optimized deep learning inference engines.

## Key findings

- Achieved 450 Tflop/s performance on supercomputers.
- Enabled the largest-scale posterior inference in a Turing-complete PPL.
- Successfully applied to LHC physics simulation.

## Abstract

Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticability of rewriting complex scientific simulators in a PPL, the computational cost of inference, and the lack of scalable implementations. To address these, we present a novel PPL framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol and provides Markov chain Monte Carlo (MCMC) and deep-learning-based inference compilation (IC) engines for tractable inference. To guide IC inference, we perform distributed training of a dynamic 3DCNN--LSTM architecture with a PyTorch-MPI-based framework on 1,024 32-core CPU nodes of the Cori supercomputer with a global minibatch size of 128k: achieving a performance of 450 Tflop/s through enhancements to PyTorch. We demonstrate a Large Hadron Collider (LHC) use-case with the C++ Sherpa simulator and achieve the largest-scale posterior inference in a Turing-complete PPL.