The Tapenade automatic differentiation tool: Principles, model, and specification

By Laurent Hascoët, Valérie Pascual. TOMS, 2013

552 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Hardware and Architecture, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f334d1bf82c3ea85aad622a266741c6cd16baf01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Charles C. Margossian
2. Gaetan K. W. Kenway
3. Charles A. Mader
4. Ping He
5. Michael Betancourt
6. Joaquim R. Martins
7. Frédo Durand
8. Barak A. Pearlmutter

In Hardware and Architecture

1. Jonathan Ragan-Kelley
2. Joaquim R. Martins
3. Frédo Durand
4. Barak A. Pearlmutter
5. Jeffrey Mark Siskind
6. Atilim Gunes Baydin
7. Alexey Radul
8. Charles C. Margossian

In Management Science and Operations Research

1. Charles C. Margossian
2. Jonathan Ragan-Kelley
3. Joaquim R. Martins
4. Frédo Durand
5. Barak A. Pearlmutter
6. Jeffrey Mark Siskind
7. Atilim Gunes Baydin
8. Alexey Radul

Citations in the corpus

• Dynamic Tensor Rematerialization (2020, methodology): “[37] Laurent Hascoet and Valérie Pascual.”
• The Stan Math Library: Reverse-Mode Automatic Differentiation in C++ (2015, methodology): “The difficulty is in parsing and dealing with all of the constructions in a complex language such as C++; existing code-generating tools such as Tapenade (Hascoët and Pascual, 2013) have only been developed for relatively simple languages like C and Fortran.”
• DiffTaichi: Differentiable Programming for Physical Simulation (2019, methodology): “Earlier work on automatic differentiation focuses on transforming existing scalar code to obtain derivatives (e.g. Utke et al. (2008), Hascoet & Pascual (2013), Pearlmutter & Siskind (2008)).”
• Effective adjoint approaches for computational fluid dynamics (2019, methodology): “The source code transformation tool Tapenade [174] was used to compute the partial derivatives for these three codes.”
• A review of automatic differentiation and its efficient implementation (2018, methodology): “There is in general, as argued by (Hascoet & Pascual, 2013), no optimal placement and users should be given the freedom to place checkpoints by hand.”
• Universal Differential Equations for Scientific Machine Learning (2020, unknown): “[91] Laurent Hascoet and Valérie Pascual.”
• Automatic differentiation in machine learning: a survey (2015, methodology): “A recent and popular tool also utilizing this approach is Tapenade (Pascual and Hascoët, 2008; Hascoët and Pascual, 2013), implementing forward and reverse mode AD for Fortran and C programs.”