Faces of HPC: Martin Fitzner
Martin is a PhD candidate in computational physics at University College London. He studies heterogenous ice nucleation (the process by which water freezes into ice) and uses HPC clusters to follow the freezing process from a molecule’s eye view in the computer as everything happens very quickly.
Martin has been interested in programming and coding since his high school days, but it was not until he was an undergraduate that he realised that amazingly complex formulae could be understood by using algorithms and computers. Martin now enjoys using HPC for the freedom it gives him. He can work anytime, anywhere he wants unlike a lot of researchers who don’t have this opportunity.
Tell us a bit about yourself – where you’re from, what you’ve studied and where, and what some of your outside interests are.
I am from the city of Jena, located in central Germany. There I studied physics with a particular focus on solid-state and computational physics. In my free time I enjoy playing the guitar and cooking.
What is your current job? Describe what you do in HPC. Is this your main interest, or something you fell into?
Currently I am a PhD candidate in computational physics at University College London in the ICE group of Angelos Michaelides. My project is on understanding heterogeneous ice nucleation (the process by which water freezes into ice), a ubiquitous phenomenon that affects many aspects ranging across different scales from e.g. intercellular freezing to anti-icing surfaces to the prediction of weather. It is very difficult to study the microscopic details of nucleation with experiments because everything happens very fast (nano seconds) on very small scales (nano meters). Thus, we approach the problem from a different perspective and employ large-scale atomistic simulations to follow the freezing process from a molecule’s eye view in the computer. This entails running scientific software on HPC clusters like ARCHER or THOMAS, as well as writing and extending software for the purpose of scientific simulations and analysis.
How did you become interested in HPC? Briefly describe your path into HPC.
I became interested in coding and programming quite early during high school. However, it was not until the computational physics lectures during my undergrad when I realized that the secrets behind most of these beautiful, yet impossible-to-solve formulas can, in principle, be uncovered with numerical algorithms and massive computer power. This canny symbiosis between natural science and computer science is intriguing and I made it the centre of my work.
As part of this project we want to celebrate the diversity of HPC, in particular to promote equality across the nine “protected” characteristics of the UK Equality Act, which are replicated in world-wide equality legislation. Do you feel an affiliation with this matter, and if so how has this interacted with or impacted your job in the HPC community?
Generally, I care about equality and observe with concern the persisting inequalities in our society, e.g. between men and women. In typical HPC interactions, be it a support request or communication with another user, there can be no pre-judgement about e.g. religion, race or age of your counterpart. Hence, I see HPC as a natural place where equal opportunities are promoted.
Is there something about you that’s given you a unique or creative approach to what you do?
It’s not particularly creative, but I love automation. It is an investment when you start something new as there are no immediate results, but once something is automated your calculation throughput increases manifold. It can feel very antsy and annoying though if there is some part of the workflow than cannot be automated or is out of reach.
Were there any challenges when you first entered the field? How have you overcome these, or do they continue to challenge you?
Nothing out of the ordinary, but the first code I wrote (a solver for quantum electronic structure) crashed the whole supercomputer it was ran on (luckily it was a rather small university cluster and not a Tier 0 ;). Back then I knew very little about HPC clusters, their architecture and how they work. But it hasn’t happened again, so I reckon I improved.
What’s the best thing about working in HPC?
I like the ability to work remotely and from anywhere I want at any time I like. Experimental scientists often wish they had this freedom as they have to stick to rather rigid plans for using the devices that are shared amongst many people. In HPC the clusters are of course also shared, but often that does not affect you as much.
If there’s one thing about HPC you could change, what would it be?
One of the problems in my opinion regarding scientific applications of HPC is that natural scientists that are creating the software often have priorities other than coming up with the cleanest code. Since they have to focus on getting papers and projects done, they often do not have the time to make code portable, readily usable for others and compatible with other software. It is heart-breaking to see a new code coming up that is able to solve a very complicated physical problem in silico, only to find that you have to spend months to make it work on your system. It is hard however to think of a solution to this other than having more people at the interface of computer science and natural science.
What’s next for you in HPC – where does your career lead you?
I will most likely continue to utilize the capabilities of HPC to keep learning and understanding from data and simulations – whether in natural science or somewhere else remains to be seen.