Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Symbolic bounds analysis of pointers, array indices, and accessed memory regions
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
"To be recorded" analysis in reverse-mode automatic differentiation
Future Generation Computer Systems
Efficient reversal of the intraprocedural flow of control in adjoint computations
Journal of Systems and Software - Special issue: Selected papers from the 4th source code analysis and manipulation (SCAM 2004) workshop
On the implementation of automatic differentiation tools
Higher-Order and Symbolic Computation
"To be recorded" analysis in reverse-mode automatic differentiation
Future Generation Computer Systems
Hybrid static/dynamic activity analysis
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
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The fast computation of gradients in reverse mode Automatic Differentiation (AD) requires the generation of adjoint versions of every statement in the original code. Due to the resulting reversal of the control flow certain intermediate values have to be made available in reverse order to compute the local partial derivatives. This can be achieved by storing these values or by recomputing them when they become required. In any case one is interested in minimizing the size of this set. Following an extensive introduction of the "To-Be-Recorded" (TBR) problem we will present flow equations for propagating the TBR status of variables in the context of reverse mode AD of structured programs.