ICFP 2021 (series) / FHPNC 2021 (series) / FHPNC 2021 /
Reverse Automatic Differentiation for Accelerate (Extended Abstract)
We define a new, pragmatic source-code transformation algorithm for reverse-mode automatic differentiation (AD) on a significant subset of the purely functional array programming language Accelerate. The supported language subset includes standard parallel second-order array combinators but excludes sequential loops. We have a preliminary implementation of this algorithm in the Accelerate compiler that, despite being very unoptimised, comes to within an order of magnitude of state-of-the-art AD implementations. Our reverse-mode AD algorithm could generalise to other second-order, purely functional array programming languages like Futhark.
Sun 22 AugDisplayed time zone: Seoul change
Sun 22 Aug
Displayed time zone: Seoul change
23:30 - 01:00 | |||
23:30 30mTalk | Parallelism-preserving automatic differentiation for second-order array languages FHPNC Adam Paszke Google Research, Matthew J. Johnson Google Research, Roy Frostig Google Research, Dougal Maclaurin Google Research | ||
00:00 30mTalk | Reverse Automatic Differentiation for Accelerate (Extended Abstract) FHPNC Tom Smeding Utrecht University, Matthijs Vákár Utrecht University, Trevor L. McDonell Utrecht University | ||
00:30 30mTalk | Computing Persistent Homology in Futhark FHPNC Erik von Brömssen Chalmers University of Technology |