WG211/M16Scholz

Traditionally, programs are specified in terms of data structures and successive modifications of these. This separation dictates at what time which piece of data is located in what space, be it main memory, disc or registers. When aiming at high-performance, parallel executions of programs, it turns out that the choice of this time / space separation can have a vast impact on the performance that can be achieved. Consequently, a lot of work has been spent on compiler technology for identifying dependencies between data and on techniques for rearranging codes for improved locality with respect to both, time and space. As it turns out, the programmer specified choice of data-structures often limits what can be achieved by such optimisation techniques.

In this talk, we argue that a new way of formulating parallel programs that is based on a unified view on space and time not only matches much better typical scientific specifications, it also increases the re-usability of programs and, most importantly, it enables more radical space-time optimisations through compilers.