57 citations · 220 across the 26 of their papers we have counts for
6 papers · 1 filter
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…