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

Diethanolaminliganden in der Synthese von 3d und 3d/4f molekularen Magneten

Im Rahmen dieser Arbeit gelingt die systematische Darstellung, Charakterisierung und magnetische Analyse neuer 3d/4f Cluster und homometallischer Cobaltverbindungen unter Verwendung verschiedener N-Alkyldiethanolaminliganden. Innerhalb der dargestellten heterometallischen Verbindungen können die Dysprosiumhaltigen Verbindungen als Einzelmolekülmagneten verifiziert werden. Die Darstellung kernstrukturanaloger Co5 Verbindungen wird zur Optimierung der SMM Eigenschaften dieser Cluster verwendet

Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding

Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model, which provides a stress test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system, a shallow water approximation for flow over a ridge, and a coarse-resolution spectral global atmospheric model with simplified parameterizations (SPEEDY). Although double precision [52 significant bits (sbits)] is standard across operational climate models, in our experiments we find that single precision (23 sbits) is more than enough and that as low as half precision (10 sbits) is often sufficient. For example, SPEEDY can be run with 12 sbits across the code with negligible rounding error, and with 10 sbits if minor errors are accepted, amounting to less than 0.1 mm (6 h)−1 for average gridpoint precipitation, for example. Our test is based on the Wasserstein metric and this provides stringent nonparametric bounds on rounding error accounting for annual means as well as extreme weather events. In addition, by testing models using both round-to-nearest (RN) and stochastic rounding (SR) we find that SR can mitigate rounding error across a range of applications, and thus our results also provide some evidence that SR could be relevant to next-generation climate models. Further research is needed to test if our results can be generalized to higher resolutions and alternative numerical schemes. However, the results open a promising avenue toward the use of low-precision hardware for improved climate modeling

Quantifying aviation's contribution to global warming

Growth in aviation contributes more to global warming than is generally appreciated because of the mix of climate pollutants it generates. Here, we model the CO2 and non-CO2 effects like nitrogen oxide emissions and contrail formation to analyse aviation's total warming footprint. Aviation contributed approximately 4% to observed human-induced global warming to date, despite being responsible for only 2.4% of global annual emissions of CO2. Aviation is projected to cause a total of about 0.1 °C of warming by 2050, half of it to date and the other half over the next three decades, should aviation's pre-COVID growth resume. The industry would then contribute a 6%-17% share to the remaining 0.3 °C-0.8 °C to not exceed 1.5 °C-2 °C of global warming. Under this scenario, the reduction due to COVID-19 to date is small and is projected to only delay aviation's warming contribution by about five years. But the leveraging impact of growth also represents an opportunity: aviation's contribution to further warming would be immediately halted by either a sustained annual 2.5% decrease in air traffic under the existing fuel mix, or a transition to a 90% carbon-neutral fuel mix by 2050

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

Recent progress in understanding and predicting Atlantic decadal climate variability

Recent Atlantic climate prediction studies are an exciting new contribution to an extensive body of research on Atlantic decadal variability and predictability that has long emphasized the unique role of the Atlantic Ocean in modulating the surface climate. We present a survey of the foundations and frontiers in our understanding of Atlantic variability mechanisms, the role of the Atlantic Meridional Overturning Circulation (AMOC), and our present capacity for putting that understanding into practice in actual climate prediction systems

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