As the computing hardware landscape gets more diverse, and the complexity of hardware grows, the need for a general purpose parallel programming model capable of developing (performance) portable codes have become highly attractive. Intel’s OneAPI suite, which is based on the SYCL standard aims to fill this gap using a modern C++ API. In this paper, we use SYCL to parallelize MG-CFD, an unstructured-mesh computational fluid dynamics (CFD) code, to explore current performance of SYCL. The code is benchmarked on several modern processor systems from Intel (including CPUs and the latest Xe LP GPU), AMD, ARM and Nvidia, making use of a variety of current SYCL compilers, with a particular focus on OneAPI and how it maps to Intel’s CPU and GPU architectures. We compare performance with other parallelizations available in OP2, including SIMD, OpenMP, MPI and CUDA. The results are mixed; the performance of this class of applications, when parallelized with SYCL, highly depends on the target architecture and the compiler, but in many cases comes close to the performance of currently prevalent parallel programming models. However, it still requires different parallelization strategies or code-paths be written for different hardware to obtain the best performance.
|Title of host publication||High Performance Computing|
|Subtitle of host publication||36th International Conference, ISC High Performance 2021, Virtual Event, June 24 – July 2, 2021, Proceedings|
|Editors||Bradford L. Chamberlain, Ana-Lucia Varbanescu, Hatem Ltaief, Piotr Luszczek|
|Number of pages||20|
|Publication status||Published - 17 Jun 2021|
|Event||36th International Conference on High Performance Computing, ISC High Performance 2021 - Virtual, Online|
Duration: 24 Jun 2021 → 2 Jul 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||36th International Conference on High Performance Computing, ISC High Performance 2021|
|Period||24/06/21 → 2/07/21|
Bibliographical noteFunding Information:
Acknowledgment. This research is supported by Rolls-Royce plc., and by the UK Engineering and Physical Sciences Research Council (EPSRC): (EP/S005072/1 – Strategic Partnership in Computational Science for Advanced Simulation and Modelling of Engineering Systems – ASiMoV). Gihan Mudalige was supported by the Royal Society Industry Fellowship Scheme(INF/R1/1800 12). István Reguly was supported by National Research, Development and Innovation Fund of Hungary, project PD 124905, financed under the PD 17 funding scheme.
© 2021, Springer Nature Switzerland AG.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)