Ongoing CASUS Open Projects

(1) November 2019 to August 2022

Exploring a performance portable software stack for PIConGPU to target a next-generation computing system, the FRONTIER Exascale System at ORNL

Partners: Oak Ridge National Laboratory, University of Delaware, Georgia Tech University, Helmholtz-Zentrum Dresden-Rossendorf

This project has three major goals:

  • Adapting the large-scale plasma simulation code PIConGPU to run on the DOE Exascale system Frontier
  • Develop the Alpaka library for performance-portable many-core programming
  • Develop the openPMD I/O ecosystem for Exascale use, especially in-memory workflows

First highlight: Access to DOE Pre-Exascale resources, 95% weak scaling efficiency achieved on DOE SUMMIT system going from 27 to 4600 nodes, results presented at SC’19 and other conferences.


(2) May 2020 to June 2023

Memory layout optimization and efficient interconversion of data structures for heterogeneous architectures

Partners: The European Organization for Nuclear Research CERN, Center for Information Services and High Performance Computing (ZIH) of Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf

Heterogeneous hardware architectures for complex parallel applications exhibit data throughput limitations that result in lower performances than theoretically accessible with optimal parallelism. Within this open project, a C++ library, called LLAMA (Low Level Abstraction of Memory Access), is developed to improve throughput by optimizing data access and movement. LLAMA will allow both a usable, minimal overhead memory layout description and an optimization of data layouts for various hardware architectures. The goal is to allow the user to describe a data structure suitable for their needs. As a first testbed, LLAMA will be used by ROOT, the big data analysis framework used by virtually all high energy physicists worldwide.


(3) November 2020 to October 2023

Computational methods for cell shapes and elastic materials

Partners: Center for Systems Biology Dresden, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden University of Technology

Natural or synthetic elastic materials can form complex shapes that are hard to gasp computationally. In biology, for example, the mechanical behavior of the cell surface itself, as well as additional processes taking place on the surface result in dynamic shape deformations. To validate hypotheses about mechanisms influencing an elastic material’s shape, effective mathematics and numerical simulation methods are required. This open project aims to contribute them. An overarching theme of the planned work is to advance established calculation methods used for flat (one-, two-, and three-dimensional) spaces to make them work in curved (high-dimensional) spaces.


(4) November 2020 to October 2023

An optimal control approach to maximizing the benefits of limited testing capacity in an emerging pandemic

Partner: University of Maryland

Insufficient testing leaves public health authorities with little information on how to coordinate efforts to effectively combat an emerging epidemic. Specifically, quick identification and isolation of new infection clusters is of critical importance. While there are recommendations that provide useful qualitative guidance when testing resources are limited, quantitative determination of optimal allocation strategies is lacking despite its potential to increase testing efficiency. Within this project a series of mathematical disease models, so-called ordinary differential equation models, are constructed to analyze the influence of total testing capacity, information limitations, and other logistical constraints on optimal allocation strategies for flattening the infection curve and reducing mortality. The goal is to identify real-world parameter thresholds and transmission scenarios that determine the viability and optimality of resource allocation strategies. The results are expected to contribute to future testing policy guidelines.


(5) January 2021 to December 2023

A machine-learning inversion framework for materials under extreme conditions

Partner: University of California Merced

The project’s main goal is to adapt the physics-informed neural networks framework for the inversion of Kohn-Sham equations, one of the world’s largest overall computational expenses, due to its prevalence in physical, biological, and materials sciences. More specifically, the proposed work has the potential to improve accuracy of low-cost electronic structure calculations. The results would have a major impact on the simulation of materials under high energy density (HED) conditions – one of the most challenging frontiers of plasma physics and materials science.