Design of Neko—A Scalable High‐Fidelity Simulation Framework With Extensive Accelerator Support

ABSTRACT Recent trends and advancements in including more diverse and heterogeneous hardware in High‐Performance Computing (HPC) are challenging scientific software developers in their pursuit of efficient numerical methods with sustained performance across a diverse set of platforms. As a result, researchers are today forced to re‐factor their codes to leverage these powerful new heterogeneous systems. We present our design considerations of Neko—a portable framework for high‐fidelity spectral element flow simulations. Unlike prior works, Neko adopts a modern object‐oriented Fortran 2008 approach, allowing multi‐tier abstractions of the solver stack and facilitating various hardware backends ranging from general‐purpose processors, accelerators down to exotic vector processors and Field‐Programmable Gate Arrays (FPGAs). Focusing on the performance and portability of Neko, we describe the framework's device abstraction layer managing device memory, data transfer and kernel launches from Fortran, allowing for a solver written in a hardware‐neutral yet performant way. Accelerator‐specific optimizations are also discussed, with auto‐tuning of key kernels and various communication strategies using device‐aware MPI. Finally, we present performance measurements on a wide range of computing platforms, including the EuroHPC pre‐exascale system LUMI, where Neko achieves excellent parallel efficiency for a large direct numerical simulation (DNS) of turbulent fluid flow using up to 80% of the entire LUMI supercomputer.

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Publication Details

Journal
Concurrency and Computation Practice and Experience
Published
2024-12-29
DOI
https://doi.org/10.1002/cpe.8340
Citations
2
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.98

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article

Design of Neko—A Scalable High‐Fidelity Simulation Framework With Extensive Accelerator Support

Philipp Schlatter, Martin Karp, Jacob Wahlgren, Niclas Jansson et al.
2 citations
Concurrency and Computation Practice and Experience
Parallel Computing and Optimization Techniques
0.98
article

Design of Neko—A Scalable High‐Fidelity Simulation Framework With Extensive Accelerator Support

Philipp Schlatter, Martin Karp, Jacob Wahlgren, Niclas Jansson, Stefano Markidis
article en
2 citations

Abstract

ABSTRACT Recent trends and advancements in including more diverse and heterogeneous hardware in High‐Performance Computing (HPC) are challenging scientific software developers in their pursuit of efficient numerical methods with sustained performance across a diverse set of platforms. As a result, researchers are today forced to re‐factor their codes to leverage these powerful new heterogeneous systems. We present our design considerations of Neko—a portable framework for high‐fidelity spectral element flow simulations. Unlike prior works, Neko adopts a modern object‐oriented Fortran 2008 approach, allowing multi‐tier abstractions of the solver stack and facilitating various hardware backends ranging from general‐purpose processors, accelerators down to exotic vector processors and Field‐Programmable Gate Arrays (FPGAs). Focusing on the performance and portability of Neko, we describe the framework's device abstraction layer managing device memory, data transfer and kernel launches from Fortran, allowing for a solver written in a hardware‐neutral yet performant way. Accelerator‐specific optimizations are also discussed, with auto‐tuning of key kernels and various communication strategies using device‐aware MPI. Finally, we present performance measurements on a wide range of computing platforms, including the EuroHPC pre‐exascale system LUMI, where Neko achieves excellent parallel efficiency for a large direct numerical simulation (DNS) of turbulent fluid flow using up to 80% of the entire LUMI supercomputer.

Concurrency and Computation Practice and ExperienceVol. 37(2)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), KTH Royal Institute of Technology (SE)
Swedish e-Science Research Centre, Partnership for Advanced Computing in Europe AISBL, Vetenskapsrådet
Openalex Percentile: Top 22%
Parallel Computing and Optimization Techniques
0.98
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