Currently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limitedarea numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

Most studies of parenting children with Down syndrome (DS) have been conducted in industrialized countries. They suggest that sensitive communication on the part of professionals, and social support, can lead to acceptance and positive adjustments in the family. This study examined the impact of a diagnosis of DS on Ecuadorian families, in particular at how the diagnosis had been communicated and received, as well as the feelings and experiences which followed. Despite considerable progress in recent years, Ecuador is still marked by discriminatory attitudes which affect children with disabilities and their families, and by the persistence of widespread poverty. This qualitative study, conducted in Cuenca, Ecuador’s third largest city, is based on a focus group discussion and four in-depth interviews with Ecuadorian parents of DS children attending a specialist center in the city. The study shows that, reflecting the effects of status differences and lack of appropriate training, professionals rarely communicate a DS diagnosis in an appropriate manner. Further, it is shown that lack of social support, and the widespread stigmatization confronting children with DS and their families, hinder development of positive and empowering adjustments that would best serve the child’s and the family’s interest.

We propose a software implementation strategy for complex weather and climate models that produces performance portable, architecture agnostic codes. It relies on domain and data structure specific tools that are usable within common model development frameworks-Fortran today and possibly high-level programming environments like Python in the future. We present the strategy in terms of a refactoring project of the atmospheric model COSMO, where we have rewritten the dynamical core and refactored the remaining Fortran code. The dynamical core is built on top of the domain specific "Stencil Loop Language" for stencil computations on structured grids, a generic framework for halo exchange and boundary conditions, as well as a generic communication library that handles data exchange on a distributed memory system. All these tools are implemented in C++ making extensive use of generic programming and template metaprogramming. The refactored code is shown to outperform the current production code and is performance portable to various hybrid CPU-GPU node architectures.

Abstract-Bit-reproducibility has many advantages in the context of high-performance computing. Besides simplifying and making more accurate the process of debugging and testing the code, it can allow the deployment of applications on heterogeneous systems, maintaining the consistency of the computations. In this work we analyze the basic operations performed by scientific applications and identify the possible sources of non-reproducibility. In particular, we consider the tasks of evaluating transcendental functions and performing reductions using non-associative operators. We present a set of techniques to achieve reproducibility and we propose improvements over existing algorithms to perform reproducible computations in a portable way, at the same time obtaining good performance and accuracy. By applying these techniques to more complex tasks we show that bit-reproducibility can be achieved on a broad range of scientific applications.

In view of the rapid rise of the number of cores in modern supercomputers, time-parallel methods that introduce concurrency along the temporal axis are becoming increasingly popular. For the solution of time-dependent partial differential equations, these methods can add another direction for concurrency on top of spatial parallelization. The paper presents an implementation of the time-parallel Parareal method in a C++ domain specific language for stencil computations (STELLA). STELLA provides both an OpenMP and a CUDA backend for a shared memory parallelization, using the CPU or GPU inside a node for the spatial stencils. Here, we intertwine this node-wise spatial parallelism with the time-parallel Parareal. This is done by adding an MPI-based implementation of Parareal, which allows us to parallelize in time across nodes. The performance of Parareal with both backends is analyzed in terms of speedup, parallel efficiency and energy-to-solution for an advection-diffusion problem with a time-dependent diffusion coefficient.