Monday, 3 September 2007, 10:00 (note the unusual weekday and time!)
Cybernetica Bldg (Akadeemia tee 21), room B101
Slides from the talk [pdf]
Abstract: In scientific computation, there is a frequent need to compute first derivatives of a function represented by a computer program. One way to achieve this is to use Automatic Differentiation (AD) which allows for the computation of derivatives of a function represented by a computer program. This is carried out by a semantics augmentation process transforming the input code to a new one that computes the function as well as its derivative.
In this talk, I will introduce the AD technique and I will motivate it through an application from computational engineering. Then, I will present an overview of tools from sparse matrix technology and compiler optimisation techniques aimed at enhancing the performance of the transformed code. I will also discuss the evaluation of these techniques in the AD context.