Student Posters Repository

We, the HPC community, are a special branch of computing where any technique is acceptable if it comes with more performance. But as a result, HPC often requires to take care of very low-level details such as spotting false sharing, while working on hybrid DM/SM software applied to large distributed heterogeneous architectures. The software developed requires time, effort and HPC expertise to be understood, maintained or improved, due to its great complexity. Paired with that fact that Exascale and its multi-million node scalability are in foreseeable future, writing HPC codes the way we do today will not be sustainable for long.
To that end, my PhD research investigates the use of behavioural types; similar to data types, but which express aspects of parallelism to be exploited by the compiler to infer additional parallelisation and HPC optimisation techniques to the code. For instance, one can think of the "pure elemental" keywords in High Performance Fortran, providing information about the behaviour of the function to which they are assigned. This additional information is later exploited by the compiler to apply automatic parallelisation over arrays.
From the above, it is easily understandable that this research is consubstantial with HPC.