Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
By Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe and 7 more. 2019
63 citations recorded by the source corpus.
Probabilistic programming languages (PPLs) are receiving wide-spread 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 mini-batch 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.
Subfields: Artificial Intelligence, Hardware and Architecture, Nuclear and High Energy Physics, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Gilles Louppe
- J. Brehmer
- Kyle Cranmer
- David S. Greenberg
- Atilim Gunes Baydin
- Adam Scibior
- Conor Durkan
- Frank D. Wood
In Hardware and Architecture
In Nuclear and High Energy Physics
- Gilles Louppe
- Kyle Cranmer
- J. Brehmer
- Atilim Gunes Baydin
- Frank D. Wood
- Andreas Munk
- U. Edinburgh
- Atlas Collaboration
In Probabilistic & Bayesian Methods
- Gilles Louppe
- J. Brehmer
- Kyle Cranmer
- David S. Greenberg
- Atilim Gunes Baydin
- Adam Scibior
- Andreas Munk
- Frank D. Wood
Citations in the corpus
- The frontier of simulation-based inference (2019, background): “Previously, this required writing the program in a special-purpose language; however, recent work allows these capabilities to be added to existing simulators with minimal changes to their codebase (42).”
- sbi: A toolkit for simulation-based inference (2020, methodology): “In cases where the simulator likelihood can be evaluated, many methods for approximate Bayesian inference exist (e.g., Metropolis, Rosenbluth, Rosen-bluth, Teller, & Teller, 1953; Baydin et al., 2019; Graham & Storkey, 2017; Le, Baydin, & Wood, 2017; Neal, 2003).”
- Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019, background): “PSN targets universal PPLs, so we focus our discussion here on those.”