H-Revolve: A Framework for Adjoint Computation on Synchrone Hierarchical Platforms

Guillaume Aupy (1) , Julien Herrmann (1)

1 TADAAM - Topology-Aware System-Scale Data Management for High-Performance Computing

In this work, we study the problem of checkpointing strategies for adjoint computation on synchrone hierarchical platforms. Specifically we consider computational platforms with several levels of storage with different writing and reading costs. When reversing a large adjoint chain, choosing which data to checkpoint and where is a critical decision for the overall performance of the computation. We introduce H-Revolve, an optimal algorithm for this problem. We make it available in a public Python library along with the implementation of several state-of-the-art algorithms for the variant of the problem with two levels of storage. We provide a detailed description of how one can use this library in an adjoint computation software in the field of automatic differentiation or backpropagation. Finally, we evaluate the performance of H-Revolve and other checkpointing heuristics though an extensive campaign of simulation.

Mots clés: Adjoint computation; Automatic differentiation; Backpropagation algorithm; Revolve; Hierarchical memory

Domaines: Analyse numérique [cs.NA]; Logiciel mathématique [cs.MS]

Guillaume Aupy, Julien Herrmann. H-Revolve: A Framework for Adjoint Computation on Synchrone Hierarchical Platforms. 2019. ⟨hal-02080706v1⟩