239 citations · 434 across the 32 of their papers we have counts for
6 papers · 1 filter
Divide-and-Conquer Checkpointing for Arbitrary Programs with No User Annotation
Jeffrey Mark Siskind, Barak A. Pearlmutter
Classical reverse-mode automatic differentiation (AD) imposes only a small constant-factor overhead in operation count over the original computation, but has storage requirements t…
Evolving the Incremental λ Calculus into a Model of Forward Automatic Differentiation (AD)
Robert Kelly, Barak A. Pearlmutter, Jeffrey Mark Siskind
Formal transformations somehow resembling the usual derivative are surprisingly common in computer science, with two notable examples being derivatives of regular expressions and d…
Efficient Implementation of a Higher-Order Language with Built-In AD
Jeffrey Mark Siskind, Barak A. Pearlmutter
We show that Automatic Differentiation (AD) operators can be provided in a dynamic language without sacrificing numeric performance. To achieve this, general forward and reverse AD…
Binomial Checkpointing for Arbitrary Programs with No User Annotation
Jeffrey Mark Siskind, Barak A. Pearlmutter
Heretofore, automatic checkpointing at procedure-call boundaries, to reduce the space complexity of reverse mode, has been provided by systems like Tapenade. However, binomial chec…
AD in Fortran, Part 2: Implementation via Prepreprocessor
Alexey Radul, Barak A. Pearlmutter, Jeffrey Mark Siskind
We describe an implementation of the Farfel Fortran AD extensions. These extensions integrate forward and reverse AD directly into the programming model, with attendant benefits to…
AD in Fortran, Part 1: Design
Alexey Radul, Barak A. Pearlmutter, Jeffrey Mark Siskind
We propose extensions to Fortran which integrate forward and reverse Automatic Differentiation (AD) directly into the programming model. Irrespective of implementation technology,…