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

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 28 Jan 2022 English Publisher:Zenodo
Authors: Daniel Lundén; Joey Öhman; Jan Kudlicka; Viktor Senderov; Fredrik Ronquist; David Broman;

doi: 10.5281/zenodo.5914164 , 10.5281/zenodo.6004987 , 10.5281/zenodo.5914165

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

Abstract

Artifact accompanying the paper Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference (https://doi.org/10.1007/978-3-030-99336-8_2) published at the European Symposium on Programming (ESOP) 2022. The file README.txt contains instructions on how to work with the artifact in Docker. The file 86.zip contains the artifact itself (ppl-smc-gpu.tar.gz) and source code for building the artifact (under docker/).

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Keywords

Probabilistic Programming Languages, Compilers, GPU Compilation, Sequential Monte Carlo

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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