research-article

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Etalumis: bringing probabilistic programming to scientific simulators at scale

Authors:

Atilim Güneş Baydin,

Lukas Heinrich,

Lawrence Meadows,

Saeid Naderiparizi,

Bradley Gram-Hansen,

Frank WoodAuthors Info & Claims

SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 29, Pages 1 - 24

https://doi.org/10.1145/3295500.3356180

Published: 17 November 2019 Publication History

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 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.

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cover image ACM Conferences

SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2019

1921 pages

ISBN:9781450362290

DOI:10.1145/3295500

General Chair:
Michela Taufer,
Program Chairs:
Pavan Balaji,
Antonio J. Peña

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SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis

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Lew AGhavamizadeh MRinard MMansinghka V(2023)Probabilistic Programming with Stochastic ProbabilitiesProceedings of the ACM on Programming Languages10.1145/35912907:PLDI(1708-1732)Online publication date: 6-Jun-2023
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