HPC, Big Data, and Data Science

Selecting a Finite Element Analysis Backend for Exascale Fusion Reactor Simulations

UB5.132
Aleksander J. Dubas
Accelerating the development of fusion energy requires large scale simulations on cutting edge supercomputing resources. Great hardware is only half the challenge and the software must be scalable to match. This talk presents an objective approach to selecting a suitable back end to fusion simulations.
The UKAEA's mission is to develop commercially viable fusion energy. Current fusion technology is yet to break even on power out compared to power in, thus designs for future reactors, which necessarily must exceed break even, carry a great amount of uncertainty. With cost estimates of a first of a kind fusion reactor in the order of billions of euros, any design flaw making it through to the construction stage will be an expensive mistake. Thankfully, software can help. By simulating a fusion reactor prior to construction, the design can be tested and refined for a considerably lower cost. However, covering all the necessary scales and physics for a digital twin of a fusion reactor requires computational resources at the exascale. In this work, a number of potential finite element backends for a multiphysics reactor simulation are evaluated. The sheer scale makes open source a practical necessity and scalability is the primary performance metric. From the plethora of open source finite element libraries, the most promising are selected and compared against a number of objective, unbiased criteria. None of the tested back ends scored perfectly in all criteria, so a method and rationale for weighting the results to select the best one for the purpose is presented. The aspects of open source projects that are important to high performance computing are highlighted.

Additional information

Type devroom

More sessions

2/2/20
HPC, Big Data, and Data Science
Colin Sauze
UB5.132
This talk will discuss the development of a RaspberryPi cluster for teaching an introduction to HPC. The motivation for this was to overcome four key problems faced by new HPC users: The availability of a real HPC system and the effect running training courses can have on the real system, conversely the availability of spare resources on the real system can cause problems for the training course. A fear of using a large and expensive HPC system for the first time and worries that doing something ...
2/2/20
HPC, Big Data, and Data Science
Adrian Woodhead
UB5.132
This presentation will give an overview of the various tools, software, patterns and approaches that Expedia Group uses to operate a number of large scale data lakes in the cloud and on premise. The data journey undertaken by the Expedia Group is probably similar to many others who have been operating in this space over the past two decades - scaling out from relational databases to on premise Hadoop clusters to a much wider ecosystem in the cloud. This talk will give an overview of that journey ...
2/2/20
HPC, Big Data, and Data Science
Félix-Antoine Fortin
UB5.132
Compute Canada provides HPC infrastructures and support to every academic research institution in Canada. In recent years, Compute Canada has started distributing research software to its HPC clusters using with CERN software distribution service, CVMFS. This opened the possibility for accessing the software from almost any location and therefore allow the replication of the Compute Canada experience outside of its physical infrastructure. From these new possibilities emerged an open-source ...
2/2/20
HPC, Big Data, and Data Science
Moritz Meister
UB5.132
Maggy is an open-source framework built on Apache Spark, for asynchronous parallel execution of trials for machine learning experiments. In this talk, we will present our work to tackle search as a general purpose method efficiently with Maggy, focusing on hyperparameter optimization. We show that an asynchronous system enables state-of-the-art optimization algorithms and allows extensive early stopping in order to increase the number of trials that can be performed in a given period of time on ...
2/2/20
HPC, Big Data, and Data Science
Suneel Marthi
UB5.132
The advent of Deep Learning models has led to a massive growth of real-world machine learning. Deep Learning allows Machine Learning Practitioners to get the state-of-the-art score on benchmarks without any hand-engineered features. These Deep Learning models rely on massive hand-labeled training datasets which is a bottleneck in developing and modifying machine learning models. Most large scale Machine Learning systems today like Google’s DryBell use some form of Weak Supervision to construct ...
2/2/20
HPC, Big Data, and Data Science
Frank McQuillan
UB5.132
In this session we will present an efficient way to train many deep learning model configurations at the same time with Greenplum, a free and open source massively parallel database based on PostgreSQL. The implementation involves distributing data to the workers that have GPUs available and hopping model state between those workers, without sacrificing reproducibility or accuracy. Then we apply optimization algorithms to generate and prune the set of model configurations to try.
2/2/20
HPC, Big Data, and Data Science
UB5.132
Predictive maintenance and condition monitoring for remote heavy machinery are compelling endeavors to reduce maintenance cost and increase availability. Beneficial factors for such endeavors include the degree of interconnectedness, availability of low cost sensors, and advances in predictive analytics. This work presents a condition monitoring platform built entirely from open-source software. A real world industry example for an escalator use case from Deutsche Bahn underlines the advantages ...