GPU-accelerated adjoint algorithmic differentiation

Abstract
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated da...

Title of program: AD-GPU
Catalogue Id: AEYX_v1_0

Nature of problem
Gradients are required for many optimization problems, e.g. classifier training or nonlinear image reconstruction. Often, the function, of which the gradient is required, can be implemented as a computer program. Then, algorithmic differentiation methods can be used to compute the gradient. Depending on the approach this may result in excessive requirements of computational resources, i.e. memory and arithmetic computations. GPUs provide massive computational resources but require special consid ...

Versions of this program held in the CPC repository in Mendeley Data
AEYX_v1_0; AD-GPU; 10.1016/j.cpc.2015.10.027

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)