Evaluating performance portability of five shared-memory programming models using a high-order unstructured CFD solver

Zhe Dai

doi:10.1016/j.jpdc.2023.104831

2024-05-01

PRE

DOI：10.1016/j.jpdc.2023.104831

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摘要

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This paper presents implementing and optimizing a high-order unstructured computational fluid dynamics (CFD) solver using five shared-memory programming models: CUDA, OpenACC, OpenMP, Kokkos, and OP2. The study aims to evaluate the performance of these models on different hardware architectures, including NVIDIA GPUs, x86-based Intel/AMD, and Arm-based systems. The goal is to determine whether these models can provide developers with performance-portable solvers running efficiently on various architectures. The paper forms a more holistic view of a high-order solver across multiple platforms by visualizing performance portability (PP) and measuring productivity. It gives guidelines for translating existing codebases and their data structures to these models.

Keyword:

performance

high-order cfd

portability

kokkos

dsl

期刊

Journal of Parallel and Distributed Computing

IF:

论文数:

3.8K

被引数:

4.8K

机构

national university of defense technology - china

学者数:

1.8W

论文数: 1.4W

被引数: 9

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