[en] We show initial performance results executing the FEniCS Project finite element software on Amazon Web Services (AWS) c7g instances with Graviton3 processors. Graviton3 processors are based on the ARM64 instruction set and provide Scalable Vector Extensions (SVE) for single instruction, multiple data (SIMD) operations. The c7g instances include a fast Elastic Fabric Adaptor (EFA) interconnect for low-latency high-bandwidth Message Passing Interface (MPI) based parallel communication. Comparing clang 15 and GCC 12 series compilers for compiling a high-order elasticity finite element kernel our results show that GCC emitted more vectorised loops with variable width SVE instructions than clang. The runtime performance of the GCC compiled kernel was 20% faster than the clang compiled kernel. We also tested multi-node weak scalability of a Poisson solver on the EFA interconnect up to 512 MPI processes. We find that overall performance and weak scalability of the AWS provisioned cluster is similar to a dedicated AMD EPYC x86-64 HPC installed at the University of Luxembourg.
Research center :
ULHPC - University of Luxembourg: High Performance Computing
HALE, Jack ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Language :
English
Title :
The FEniCS Project on AWS Graviton3
Publication date :
14 November 2023
Version :
Preprint for distribution at Supercomputing' 23
Focus Area :
Computational Sciences
FnR Project :
FNR17205623 - Constraint Aware Optimization Of Topology In Design-for-additive-manufacturing, 2022 (01/11/2022-31/10/2024) - Michal Habera
Funders :
Amazon [US-WA]
Funding text :
This project has received compute resources from Amazon Web Services (AWS) through the first and second University of Luxembourg/AWS collaborative Graviton3 call.
Commentary :
This research was funded in whole, or in part, by the National Research Fund (FNR), grant reference COAT/17205623. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.
