CAMORRA: a C++ library for recursive computation of particle scattering amplitudes

We present a new Monte Carlo tool that computes full tree-level matrix elements in high-energy physics. The program accepts user-defined models and has no restrictions on the process multiplicity. To achieve acceptable performance, CAMORRA evaluates the matrix elements in a recursive way by combining off-shell currents. Furthermore, CAMORRA can be used to compute amplitudes involving continuous color and helicity final states.Comment: 22 page

Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study

Earth system models have considerably increased their spatial resolution to solve more complex problems and achieve more realistic solutions. However, this generates an enormous amount of model data which requires proper management. Some Earth system models use inefficient sequential input/output (I/O) schemes that do not scale well when many parallel resources are used. In order to address this issue, the most commonly adopted approach is to use scalable parallel I/O solutions that offer both computational performance and efficiency. In this paper we analyse the I/O process of the European Centre for Medium-Range Weather Forecasts (ECMWF) operational Integrated Forecasting System (IFS) CY43R3. IFS can use two different output schemes: a parallel I/O server developed by Météo-France used operationally and an obsolete sequential I/O scheme. The latter is the only scheme that is being exposed by the OpenIFS variant of IFS. “Downstream” Earth system models that have adopted older versions of an IFS derivative as a component – such as the EC-Earth 3 climate model – also face a bottleneck due to the limited I/O capabilities and performance of the sequential output scheme. Moreover, it is often desirable to produce grid-point-space Network Common Data Format (NetCDF) files instead of the IFS native spectral and grid-point output fields in General Regularly-distributed Information in Binary form (GRIB), which requires the development of model-specific post-processing tools. We present the integration of the XML Input/Output Server (XIOS) 2.0 into IFS CY43R3. XIOS is an asynchronous Message Passing Interface (MPI) I/O server that offers features especially targeted at climate models: NetCDF output files, inline diagnostics, regridding, and, when properly configured, the capability to produce CMOR-compliant data. We therefore expect our work to reduce the computational cost of data-intensive (high-resolution) climate runs, thereby shortening the critical path of EC-Earth 4 experiments. The performance evaluation suggests that the use of XIOS 2.0 in IFS CY43R3 to output data achieves an adequate performance as well, outperforming the sequential I/O scheme. Furthermore, when we also take into account the post-processing task, which is needed to convert GRIB files to NetCDF files and also transform IFS spectral output fields to grid-point space, our integration not only surpasses the sequential output scheme but also the operational IFS I/O server.This research has been supported by Horizon 2020 (ESiWACE2 (grant no. 823988) and PRIMAVERA (grant no. 641727)).Peer ReviewedPostprint (published version

High-resolution superparameterization of OpenIFS with DALES

Poster about superparameterization of clouds, convection and turbulence in the global atmospheric model OpenIFS, using DALES, a high-resolution, three-dimensional large-eddy simulation code

Regional superparameterization in a global circulation model using Large Eddy Simulations

As a computationally attractive alternative for global large eddy simulations (LESs), we investigate the possibility of using comprehensive three‐dimensional LESs as a superparameterization that can replace all traditional parameterizations of atmospheric processes that are currently used in global models. We present the technical design for a replacement of the parameterization for clouds, convection, and turbulence of the global atmospheric model of the European Centre for Medium‐Range Weather Forecasts by the Dutch Atmospheric Large Eddy Simulation model. The model coupling consists of bidirectional data exchange between the global model and the high‐resolution LES models embedded within the columns of the global model. Our setup allows for selective superparameterization, that is, for applying superparameterization in local regions selected by the user, while keeping the standard parameterization of the global model intact outside this region. Computationally, this setup can result in major geographic load imbalance, because of the large difference in computational load between superparameterized and nonsuperparameterized model columns. To resolve this issue, we use a modular design where the local and global models are kept as distinct model codes and organize the model coupling such that all the local models run in parallel, separate from the global model. First simulation results, employing this design, demonstrate the potential of our approach

Creating a reusable cross-disciplinary multi-scale and multi-physics framework: From AMUSE to OMUSE and beyond

Here, we describe our efforts to create a multi-scale and multi-physics framework that can be retargeted across different disciplines. Currently we have implemented our approach in the astrophysical domain, for which we developed AMUSE (github.com/amusecode/amuse ), and generalized this to the oceanographic and climate sciences, which led to the development of OMUSE (bitbucket.org/omuse ). The objective of this paper is to document the design choices that led to the successful implementation of these frameworks as well as the future challenges in applying this approach to other domains

Performance optimization and load-balancing modeling for superparametrization by 3D LES

In order to eliminate climate uncertainty w.r.t. cloud and convection parametrizations, superpramaterization (SP) [1] has emerged as one of the possible ways forward. We have implemented (regional) superparametrization of the ECMWF weather model OpenIFS [2] by cloud-resolving, three-dimensional large-eddy simulations. This setup, described in [3], contains a two-way coupling between a global meteorological model that resolves large-scale dynamics, with many local instances of the Dutch Atmospheric Large Eddy Simulation (DALES) [4], resolving cloud and boundary layer physics. The model is currently prohibitively expensive to run over climate or even seasonal time scales, and a global SP requires the allocation of millions of cores. In this paper, we study the performance and scaling behavior of the LES models and the coupling code and present our implemented optimizations. We mimic the observed load imbalance with a simple performance model and present strategies to improve hardware utilization in order to assess the feasibility of a world-covering superparametrization. We conclude that (quasi-)dynamical load-balancing can significantly reduce the runtime for such large-scale systems with wide variability in LES time-stepping speeds

Representing cloud mesoscale variability in superparameterized climate models

In atmospheric modeling, superparameterization (SP) has gained popularity as a technique to improve cloud and convection representations in large-scale models by coupling them locally to cloud-resolving models. We show how the different representations of cloud water in the local and the global models in SP lead to a suppression of cloud advection and ultimately to a systematic underrepresentation of the cloud amount in the large-scale model. We demonstrate this phenomenon in a regional SP experiment with the global model OpenIFS coupled to the local model Dutch Atmospheric Large Eddy Simulation, as well as in an idealized setup, where the large-scale model is replaced by a simple advection scheme. As a starting point for mitigating the problem of suppressed cloud advection, we propose a scheme where the spatial variability of the local model's total water content is enhanced in order to match the global model's cloud condensate amount. The proposed scheme enhances the cloud condensate amount in the test cases, however a large discrepancy remains, caused by rapid dissipation of the clouds added by the proposed scheme