57 citations · 220 across the 26 of their papers we have counts for
26 papers
An Open-Source Power Measurement Platform for System-Level Semiconductor Testing
Linus Bantel, Sarah Rottacker, Dirk Pflüger
Accurate power measurement is not only essential for evaluating the energy efficiency of modern embedded and semiconductor systems, but power draw is an important proxy during stre…
PortLBM: A Portable Lattice Boltzmann Tool Leveraging SYCL on AMD, NVIDIA, and Intel GPUs
Alexander Strack, Marcel Graf, Alexander Van Craen +1
The lattice Boltzmann method (LBM) is a well-established approach for simulating fluid flows at the mesoscopic scale. With the stagnation of Moore's law, high-performance computing…
Generated, Parallel, Scalable? A Study of Agentic AI-Generated Julia Code on Supercomputers
Linus Bantel, Anna-Lena Roth, Jonas Posner +1
Julia is increasingly used in HPC as a single-language alternative to combining high-level scripting with low-level systems languages, but achieving scalable performance still requ…
From Fork-Join to Asynchronous Tasks: Parallelizing Tiled Cholesky Decomposition with OpenMP and HPX
Alexander Strack, Alexander Van Craen, Dirk Pflüger
Fork-join parallelism, popularized by OpenMP, remains the dominant model for shared-memory parallel programming, but its implicit synchronization barriers can penalize algorithms w…
Is RISC-V Ready for Machine Learning? Portable Gaussian Processes Using Asynchronous Tasks
Alexander Strack, Patrick Diehl, Dirk Pflüger
Gaussian processes are widely used in machine learning domains but remain computationally demanding, limiting their efficient scalability across emerging hardware platforms. The GP…
Comparing the Performance of Heterogeneous Conjugate Gradient and Cholesky Solvers on Various Hardware Using SYCL
Tim Thüring, Alexander Strack, Dirk Pflüger
Many important real-world applications, such as System Identification with Gaussian Processes, involve solving linear systems with symmetric positive-definite matrices. The iterati…
Experiences Porting Distributed Applications to Asynchronous Tasks: A Multidimensional FFT Case-study
Alexander Strack, Christopher Taylor, Patrick Diehl +1
Parallel algorithms relying on synchronous parallelization libraries often experience adverse performance due to global synchronization barriers. Asynchronous many-task runtimes of…
Hydrodynamic simulations of WD-WD mergers and the origin of RCB stars
Sagiv Shiber, Orsola De Marco, Patrick M. Motl +10
We study the properties of double white dwarf (DWD) mergers by performing hydrodynamic simulations using the new and improved adaptive mesh refinement code Octo-Tiger. We follow th…
Simulating Stellar Merger using HPX/Kokkos on A64FX on Supercomputer Fugaku
Patrick Diehl, Gregor Daiß, Kevin Huck +4
The increasing availability of machines relying on non-GPU architectures, such as ARM A64FX in high-performance computing, provides a set of interesting challenges to application d…
Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL
Gregor Daiß, Patrick Diehl, Hartmut Kaiser +1
Ranging from NVIDIA GPUs to AMD GPUs and Intel GPUs: Given the heterogeneity of available accelerator cards within current supercomputers, portability is a key aspect for modern HP…
Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps
Kyle Davis, Raphael Leiteritz, Dirk Pflüger +1
The ability for groundwater heat pumps to meet space heating and cooling demands without relying on fossil fuels, has prompted their mass roll out in dense urban environments. In r…
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Makoto Takamoto, Timothy Praditia, Raphael Leiteritz +4
Machine learning-based modeling of physical systems has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of benchmarks for Sc…
From Merging Frameworks to Merging Stars: Experiences using HPX, Kokkos and SIMD Types
Gregor Daiß, Srinivas Yadav Singanaboina, Patrick Diehl +2
Octo-Tiger, a large-scale 3D AMR code for the merger of stars, uses a combination of HPX, Kokkos and explicit SIMD types, aiming to achieve performance-portability for a broad rang…
From Task-Based GPU Work Aggregation to Stellar Mergers: Turning Fine-Grained CPU Tasks into Portable GPU Kernels
Gregor Daiß, Patrick Diehl, Dominic Marcello +3
Meeting both scalability and performance portability requirements is a challenge for any HPC application, especially for adaptively refined ones. In Octo-Tiger, an astrophysics app…
A mass-conserving sparse grid combination technique with biorthogonal hierarchical basis functions for kinetic simulations
Theresa Pollinger, Johannes Rentrop, Dirk Pflüger +1
The exact numerical simulation of plasma turbulence is one of the assets and challenges in fusion research. For grid-based solvers, sufficiently fine resolutions are often unattain…
A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps
Raphael Leiteritz, Kyle Davis, Miriam Schulte +1
Climate control of buildings makes up a significant portion of global energy consumption, with groundwater heat pumps providing a suitable alternative. To prevent possibly negative…
PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine
Alexander Van Craen, Marcel Breyer, Dirk Pflüger
Machine learning algorithms must be able to efficiently cope with massive data sets. Therefore, they have to scale well on any modern system and be able to exploit the computing po…
Surrogate-data-enriched Physics-Aware Neural Networks
Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk +1
Neural networks can be used as surrogates for PDE models. They can be made physics-aware by penalizing underlying equations or the conservation of physical properties in the loss f…
How to Avoid Trivial Solutions in Physics-Informed Neural Networks
Raphael Leiteritz, Dirk Pflüger
The advent of scientific machine learning (SciML) has opened up a new field with many promises and challenges in the field of simulation science by developing approaches at the int…
Learning Free-Surface Flow with Physics-Informed Neural Networks
Raphael Leiteritz, Marcel Hurler, Dirk Pflüger
The interface between data-driven learning methods and classical simulation poses an interesting field offering a multitude of new applications. In this work, we build on the notio…
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\
Peter Domanski, Dirk Pflüger, Jochen Rivoir +1
In recent years, the usage of ensemble learning in applications has grown significantly due to increasing computational power allowing the training of large ensembles in reasonable…
Self-Learning Tuning for Post-Silicon Validation
Peter Domanski, Dirk Pflüger, Jochen Rivoir +1
Increasing complexity of modern chips makes design validation more difficult. Existing approaches are not able anymore to cope with the complexity of tasks such as robust performan…
Octo-Tiger's New Hydro Module and Performance Using HPX+CUDA on ORNL's Summit
Patrick Diehl, Gregor Daiß, Dominic Marcello +5
Octo-Tiger is a code for modeling three-dimensional self-gravitating astrophysical fluids. It was particularly designed for the study of dynamical mass transfer between interacting…
From Piz Daint to the Stars: Simulation of Stellar Mergers using High-Level Abstractions
Gregor Daiß, Parsa Amini, John Biddiscombe +7
We study the simulation of stellar mergers, which requires complex simulations with high computational demands. We have developed Octo-Tiger, a finite volume grid-based hydrodynami…