Automatic Differentiation of Parallel Loops with Formal Methods - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2022

Automatic Differentiation of Parallel Loops with Formal Methods

Résumé

This paper presents a novel combination of reverse mode automatic differentiation and formal methods, to enable efficient differentiation of (or backpropagation through) shared-memory parallel loops. Compared to the state of the art, our approach can reduce the need for atomic updates or private data copies during the parallel derivative computation, even in the presence of unstructured or data-dependent data access patterns. This is achieved by gathering information about the memory access patterns from the input program, which is assumed to be correctly parallelized. This information is then used to build a model of assertions in a theorem prover, which can be used to check the safety of shared memory accesses during the parallel derivative loops. We demonstrate this approach on scientific computing benchmarks including a lattice-Boltzmann method (LBM) solver from the Parboil benchmark suite and a Green's function Monte Carlo (GFMC) kernel from the CORAL benchmark suite.
Fichier principal
Vignette du fichier
pap492s3-file1.pdf (531.18 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03923346 , version 1 (04-01-2023)

Identifiants

Citer

Jan Hückelheim, Laurent Hascoët. Automatic Differentiation of Parallel Loops with Formal Methods. ICPP 2022 - 51st International Conference on Parallel Processing, Aug 2022, Bordeaux, France. ⟨10.1145/3545008.3545089⟩. ⟨hal-03923346⟩
81 Consultations
109 Téléchargements

Altmetric

Partager

More