Scheduling Finite Difference Approximations for DAG-Modeled Large Scale Applications
Xavier Meyer, Bastien Chopard, Nicolas Salamin · 2017
An increasing number of scientific domains are confronted with the arduous task of managing large scale applications. For such applications, gradient estimations come at a large computational cost. Despite notable advances in automatic differentiation during the last years, its use in this context may reveal too costly in memory, inadequate for parallel architecture or require expert knowledge. For these reasons, we investigate an alternative approach that uses the finite difference method to evaluate the gradient of functions modeled as a directed acyclic graph. This approach enables the reuse of partial results from previous partial derivatives evaluations and thus reduces the computational cost.