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Open PhD posts

Open PhD positions

I’m currently looking for potential PhD candidates in the areas below. I prefer to tailor projects to the knowledge and interest of candidates, i.e. the descriptions below are just kind of an inspiration or a starting point before we flesh out a proper PhD project.

Numerical Relativity in the Realm of New Physics

Collaborative project with Baojiu Li (ICC)
Preferred funding stream: CSC or DDS

Numerical Relativity is the holy grail of computational physics. Yet, it took nearly a century after Einstein published General Relativity (GR) that long-term stable simulations of black holes became possible due to the highly nonlinear nature of the gravitational field equations. Since then, there have been huge progresses in this field, and interest has grown much stronger after the first gravitational wave (GW) detection less than a decade ago. Already, such detections have been used to shed light on the mysterious accelerated expansion of our Universe, ruling classes of gravity theories beyond GR.

With the advent of a new generation of GW detectors, in the coming decades GW cosmology will evolve into a mature subject in astronomy. The data collected will allow people to test new theories of fundamental physics with unprecedented exquisiteness. However, even today, simulating the evolution of compact object systems such as black holes and neutron stars in various theories of gravity is still a big challenge.

In this project, the candidate will work on the scientific developments and applications of a new numerical relativity simulation code, ExaGRyPE, developed by a collaboration between the Physics and Computer Science departments at Durham University. There are a range of potential directions this project can lead to, including, but not restricted to,

The project can be combined or used to facilitate and study (i) the implementation and test of new physics models in the code (e.g. non-standard gravity), (ii) developing hydrodynamics modules that will enable simulations of complicated coupled multi-physics systems, or (iii) a reformulation of the underlying physical model in a higher-dimensional brane world—a simulation unfeasible with other codes in the field.

Flexible computing

Current simulation codes invest an enormous amount of work into load balancing. They try to spread work evenly out over the machine to ensure that all resources are reasonably utilised. On modern exascale machines and for modern algorithms, this approach quickly starts to struggle: If the mesh of a simulation changes at runtime or if an algorithm consists of different compute phases, there is no “optimal” resource distribution. Rather than investing into better load balancing, we will investigate the paradigm of flexible computing, where the machine configuration is altered at runtime to suit the work arising.

There are two different direction of travel:

  • The next generation of supercomputers might feature smart NICs. These are network cards (and switches) with their own compute power (processors). NVIDIA’s BlueField technology is an example for such machinery. If the network becomes intelligent with its own processors – and in NVIDIA’s case soon GPUs – we can throw compute tasks into the network and leave it to the network to puzzle out where to run them. They can migrate tasks to underutilised cores, e.g. If multiple simulations run on a machine at the same time, the network cards and switches also can make their resources available to the simulation that need the resources most. The association of processors and GPUs follows the system load.
  • The next generation of supercomputer nodes will feature an unprecedented number of cores. Many codes at the moment run a fixed number of ranks on each node and each rank has a fixed number of cores at disposal. We propose to make each and every rank see each and every core initially. The ranks per node then can argue with each other who uses which core. Ranks with low load can retreat from cores, while ranks with high compute load can “invade” cores previously used by other ranks.

Both strands will eventually lead into a common set of algorithms, paradigms and code building blocks.