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

Julien Herrmann (1) , Guillaume Pallez (1)

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

We study the problem of checkpointing strategies for adjoint computation on synchronous hierarchical platforms, specifically 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: Backpropagation algorithm; Adjoint computation; Automatic differentiation; Deep learning; Revolve; Hierarchical memory

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

Julien Herrmann, Guillaume Pallez. H-Revolve: A Framework for Adjoint Computation on Synchronous Hierarchical Platforms. ACM Transactions on Mathematical Software, 2020, ⟨10.1145/3378672⟩. ⟨hal-02080706v2⟩