AD Tool: ADiJaC
Introduction
Applications
Tools
Research Groups
Workshops
Publications
My Account
About

ADiJaC


Summary:
ADiJaC uses source code transformation to generate derivative codes in both the forward and the reverse modes of automatic differentiation.

URL: http://adijac.cs.pub.ro

Developers:
  • Emil-Ioan Slusanschi, Vlad Dumitrel, Silvia Stegaru, Cristina Ilie, Alex Teaca, Daniel Mahu, Computer Science and Engineering, University Politehnica of Bucharest (https://cs.pub.ro/)
  • Christian Bischof, Institute for Scientific Computing, Technische Universitšt Darmstadt (http://www.sc.informatik.tu-darmstadt.de/)

Mode: Forward
Reverse
 
Method: Source transformation
 
supported Language: Java

Reference:
E. Slusanschi
Algorithmic differentiation of Java programs
Ph.D. thesis, Department of Computer Science, RWTH Aachen University, 2008



Features:
- Internal Representations - Jimple and Grimp from the Soot Framework
- Interprocedural Activity Analysis
- Dependency Analysis & Instruction Reordering
- Vector mode for the Forward Mode Implementation
- TBR Analysis in the Reverse Mode Implementation

Supported Platforms:
  • Windows
  • Unix/Linux
  • Mac


Licensing: license

References on ADiJaC in our publication database:  1

10+
#Pubs
0
1
'08
Year
  

LinkedIn:    Contact:
autodiff.org
Username:
Password:
(lost password)