Productivity meets Performance: Julia on A64FX

The Fujitsu A64FX ARM-based processor is used in supercomputers such as Fugaku in Japan and Isambard 2 in the UK and provides an interesting combination of hardware features such as Scalable Vector Extension (SVE), and native support for reduced-precision floating-point arithmetic. The goal of this paper is to explore performance of the Julia programming language on the A64FX processor, with a particular focus on reduced precision. Here, we present a performance study on axpy to verify the compilation pipeline, demonstrating that Julia can match the performance of tuned libraries. Additionally, we investigate Message Passing Interface (MPI) scalability and throughput analysis on Fugaku showing next to no significant overheads of Julia of its MPI interface. To explore the usability of Julia to target various floating-point precisions, we present results of ShallowWaters.jl, a shallow water model that can be executed a various levels of precision. Even for such complex applications, Julia's type-flexible programming paradigm offers both, productivity and performance

Earth Virtualization Engines -- A Technical Perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change

Earth Virtualization Engines: a technical perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change

Earth Virtualization Engines (EVE)

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change

Earth Virtualization Engines (EVE)

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change

Low-precision climate computing: preserving information despite fewer bits

Progress towards more reliable weather and climate forecasts is limited by the resolution of numerical models and the complexity of simulated processes. Performance is therefore a major bottleneck and current models are not com- putationally efficient. High precision calculations are unnecessary, despite be- ing the standard, given the uncertainties in the climate system and the errors from discretisation, data assimilation and unresolved climate processes. In this thesis, we advance several aspects of low-precision climate computing to preserve information despite fewer bits: An information-preserving com- pression is developed that distinguishes between real and false information to reduce the very large volume of climate data produced by numerical mod- els, while minimising information loss. The bitwise real information content estimates the minimum required precision in climate data, which depends on the variable and is lower than the standard precision-levels of floating-point numbers. The impact of rounding errors introduced by different low-precision arithmetics with deterministic or stochastic rounding modes is analysed in chaotic dynamical systems. Standard floating-point numbers are not the best number format for weather and climate simulations. However, alternatives, such as posits, exist, but it is unclear whether the large effort needed to de- velop the respective hardware for future supercomputers is justified given the moderate advantage they provide in our applications. A much more central issue towards 16-bit climate models is the design of low precision-resilient al- gorithms. A naive transition to 16 bits either fails or was found to cause issues like amplified gravity waves, a change in geostrophy or rounding errors that grow as quickly as discretisation errors. However, many of these issues are found to be preventable with techniques such as scaling or a compensated time integration. Combining techniques, we develop a 16-bit fluid circulation model that approaches 4x speedups on Fujitsu’s A64FX processor compared to 64 bits, despite minimal rounding errors. The result of this thesis show that there is little reason to assume that 16-bit weather and climate models are not possible. While the design of models to compute and output only the bitwise real information is challenging, it will be a major step towards computationally efficient digital twins of the Earth’s climate system

Aspects of weather parameters at Neumayer station, Antarctica, and their representation in reanalysis and climate model data

ERA-Interim reanalysis data and data of the Hadley Centre Global Environmental Model version 2 (HadGEM2) are compared with continuous meteorological observations of near-surface wind and temperature carried out for more than 30 years at Neumayer station, situated on the Ekström Ice Shelf of Antarctica. Significant temperature correlations between Neumayer climate and the climate of both the interior of the Antarctic continent and oceanic regions north of Neumayer are investigated using observational data and model data. Mean sea level pressure fluctuations at Neumayer can be connected to changes in the Southern Annular Mode (SAM). Shortcomings in the ERA-Interim reanalysis data with spurious trends of up to 7 °C over 31 years are identified at several places in Antarctica. Furthermore, it is shown that katabatic winds in both the ERA-Interim reanalysis data and in the HadGEM2 climate model are underrepresented in frequency and speed, presumably due to the problems in representing topography in these relatively coarse resolution models. This may be one reason for the positive 2m air temperature bias of 3 °C in the models at Neumayer station. The results of this study reemphasize that climatic trends in regions with a low station density can not be assessed solely from model data. Thus, it is absolutely necessary to maintain polar observatories such as Neumayer station to quantify climate change over the Southern Ocean and Antarctica

Weather service in the Dronning Maud Land

For more than a decade meteorologists at the German Antarctic research station Neumayer (70°S, 008°W) offer detailed and individual summer weather forecasts for all activities in the Dronning Maud Land. Especially the intercontinental air link with Cape Town made the establishment of this service mandatory. The work is performed in close cooperation between the Alfred Wegener Institute for Polar and Marine Research (AWI) and the German Weather Service (DWD). The forecasts base mainly on in situ data including automatic weather stations (AWS), on near real time satellite pictures and on a variety of model products mainly from the Antarctic Mesoscale Prediction System (AMPS) and the European Centre for Medium-Range Weather Forecasts (ECMWF). To optimize this service the errors of a typical AWS had been quantified by running an unmaintained AWS one year side by side of the maintained instruments from the meteorological observatory from Neumayer. In a second year the same AWS was placed 11 km north of Neumayer to judge the spatial footprint of the observatory data. By comparing model products with the measurements of the observatory systematic errors in the forecast products have been observed. Also the ERA-Interim reanalysis differs significantly from the temperature time series observed at Neumayer despite the fact that the data is fed into the Global Telecommunication System GTS for more than 30 years. From these findings some guidance on optimizing the Antarctic observing and prediction systems could be developed