37 citations · 148 across the 18 of their papers we have counts for
18 papers
Closing a Source Complexity Gap between Chapel and HPX
Shreyas Atre, Chris Taylor, Patrick Diehl +1
A previous case study measured performance vs source-code complexity across multiple languages. The case study identified Chapel and HPX provide similar performance and code comple…
Asynchronous-Many-Task Systems: Challenges and Opportunities -- Scaling an AMR Astrophysics Code on Exascale machines using Kokkos and HPX
Gregor Daiß, Patrick Diehl, Jiakun Yan +9
Dynamic and adaptive mesh refinement is pivotal in high-resolution, multi-physics, multi-model simulations, necessitating precise physics resolution in localized areas across expan…
Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophyiscs Application with HPX and Kokkos
Patrick Diehl, Panagiotis Syskakis, Gregor Daiß +7
In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance Computing where energy limitations ar…
HPX with Spack and Singularity Containers: Evaluating Overheads for HPX/Kokkos using an astrophysics application
Patrick Diehl, Steven R. Brandt, Gregor Daiß +1
Cloud computing for high performance computing resources is an emerging topic. This service is of interest to researchers who care about reproducible computing, for software packag…
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…
Benchmarking the Parallel 1D Heat Equation Solver in Chapel, Charm++, C++, HPX, Go, Julia, Python, Rust, Swift, and Java
Patrick Diehl, Steven R. Brandt, Max Morris +2
Many scientific high performance codes that simulate e.g. black holes, coastal waves, climate and weather, etc. rely on block-structured meshes and use finite differencing methods…