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 data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight thr

SUBMITTER: Gremse F 

PROVIDER: S-EPMC4772124 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature