Baydin, Atilim Gunes and Pearlmutter, Barak A. and Siskind, Jeffrey Mark (2016) DiffSharp: An AD Library for .NET Languages. In: AD 2016 Conference: 7th International Conference on Algorithmic Differentiation, September 12-15, 2016, Oxford, U.K..

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Abstract

DiffSharp is an algorithmic differentiation or automatic differentiation (AD) library for the .NET ecosystem, which is targeted by the C# and F# languages, among others. The library has been designed with machine learning applications in mind, allowing very succinct implementations of models and optimization routines. DiffSharp is implemented in F# and exposes forward and reverse AD operators as general nestable higher-order functions, usable by any .NET language. It provides high-performance linear algebra primitives---scalars, vectors, and matrices, with a generalization to tensors underway---that are fully supported by all the AD operators, and which use a BLAS/LAPACK backend via the highly optimized OpenBLAS library. DiffSharp currently uses operator overloading, but we are developing a transformation-based version of the library using F#'s "code quotation" metaprogramming facility. Work on a CUDA-based GPU backend is also underway.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Extended abstract presented at the AD 2016 Conference, Sep 2016, Oxford UK. Cite as: arXiv:1611.03423 [cs.MS]
Keywords:	DiffSharp; algorithmic differentiation; automatic differentiation (AD);
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	8114
Depositing User:	Barak Pearlmutter
Date Deposited:	03 Apr 2017 15:38
Refereed:	Yes
Funders:	Science Foundation Ireland (SFI)
URI:	https://arxiv.org/abs/1611.03423
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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