Accelerating advection for atmospheric modelling on Xilinx and Intel FPGAs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Reconfigurable architectures, such as FPGAs, execute code at the electronics level, avoiding assumptions imposed by the general purpose black-box micro-architectures of CPUs and GPUs. Such tailored execution can result in increased performance and power efficiency, and as the HPC community moves towards exascale an important question is the role these hardware technologies can play in future supercomputers.In this paper we explore the porting of the PW advection kernel, an important code component used in a variety of atmospheric simulations and accounting for around 40% of the runtime of the popular Met Office NERC Cloud model (MONC). Building upon previous work which ported this kernel to an older generation of Xilinx FPGA, we target latest generation Xilinx Alveo U280 and Intel Stratix 10 FPGAs. Designing around the abstraction of an Application Specific Dataflow Machine (ASDM), we develop a design which is performance portable between vendors and explore implementation differences between the tool chains and compare kernel performance between FPGA hardware. This is followed by a more general performance comparison, scaling up the number of kernels on the Xilinx Alveo and Intel Stratix 10, against a 24 core Xeon Platinum Cascade Lake CPU and NVIDIA Tesla V100 GPU. When overlapping the transfer of data to and from the boards with compute, the FPGA solutions considerably outperform the CPU and, whilst falling short of the GPU in terms of performance, demonstrate power usage benefits, with the Alveo being especially power efficient. The result of this work is a comparison and set of design techniques that apply both to this specific atmospheric advection kernel on Xilinx and Intel FPGAs, and that are also of interest more widely when looking to accelerate HPC codes on a variety of reconfigurable architectures.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Cluster Computing (CLUSTER)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-7281-9666-4
ISBN (Print)978-1-7281-9667-1
Publication statusE-pub ahead of print - 13 Oct 2021
EventIEEE Cluster 2021 - Virtual
Duration: 7 Sept 202110 Sept 2021

Publication series

ISSN (Print)1552-5244
ISSN (Electronic)2168-9253


ConferenceIEEE Cluster 2021
Internet address


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