SpF: Enabling Petascale Performance for Pseudospectral Dynamo Models

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community.We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. High-level abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely in-processor. The granularity of domain-decomposition provided by SpF is only constrained by the data-locality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures.In this presentation, we will describe the basic architecture of SpF as well as preliminary performance data and experience with adapting legacy dynamo codes. We will conclude with a discussion of planned extensions to SpF that will provide pseudospectral applications with additional flexibility with regard to time integration, linear solvers, and discretization in the radial direction

PROcess Based Diagnostics PROBE

Many of the aspects of the climate system that are of the greatest interest (e.g., the sensitivity of the system to external forcings) are emergent properties that arise via the complex interplay between disparate processes. This is also true for climate models most diagnostics are not a function of an isolated portion of source code, but rather are affected by multiple components and procedures. Thus any model-observation mismatch is hard to attribute to any specific piece of code or imperfection in a specific model assumption. An alternative approach is to identify diagnostics that are more closely tied to specific processes -- implying that if a mismatch is found, it should be much easier to identify and address specific algorithmic choices that will improve the simulation. However, this approach requires looking at model output and observational data in a more sophisticated way than the more traditional production of monthly or annual mean quantities. The data must instead be filtered in time and space for examples of the specific process being targeted.We are developing a data analysis environment called PROcess-Based Explorer (PROBE) that seeks to enable efficient and systematic computation of process-based diagnostics on very large sets of data. In this environment, investigators can define arbitrarily complex filters and then seamlessly perform computations in parallel on the filtered output from their model. The same analysis can be performed on additional related data sets (e.g., reanalyses) thereby enabling routine comparisons between model and observational data. PROBE also incorporates workflow technology to automatically update computed diagnostics for subsequent executions of a model. In this presentation, we will discuss the design and current status of PROBE as well as share results from some preliminary use cases

CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-Scale Pattern Generation

Generative Adversarial Networks (GANs) are proving to be a powerful indirect genotype-to-phenotype mapping for evolutionary search, but they have limitations. In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious way of combining multiple GAN outputs into a cohesive whole, which would be useful in many areas, such as the generation of video game levels. Game levels often consist of several segments, sometimes repeated directly or with variation, organized into an engaging pattern. Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). Specifically, a CPPN can define latent vector GAN inputs as a function of geometry, which provides a way to organize level segments output by a GAN into a complete level. This new CPPN2GAN approach is validated in both Super Mario Bros. and The Legend of Zelda. Specifically, divergent search via MAP-Elites demonstrates that CPPN2GAN can better cover the space of possible levels. The layouts of the resulting levels are also more cohesive and aesthetically consistent.Comment: GECCO 2020. arXiv admin note: text overlap with arXiv:2004.0015

Pilot Evaluation of an Online Weight Management Programme

This intervention examined the efficacy of a six-week online weight loss programme. Students and staff of a third level institution (n=183) were recruited to the programme which provided individualised dietary advice for weight loss. Eighty-five participants (mean age 29.7 years, mean BMI 28.9kg/m2, 33% male) met the minimum inclusion criterion of logging on to the study website at least twice. All participants who completed the full six-week programme lost weight (n=31), with significant reductions in mean weight (2.8kg), BMI (0.9kg/m2) and waist circumference (4.1cm) observed between the start and end of the programme (all P5% of their bodyweight, with reductions in biscuit and alcohol consumption being most predictive of weight loss. These findings suggest that individualised online dietary advice is effective in achieving short-term weight loss, especially in males

Abortion and lamb mortality between pregnancy scanning and lamb marking for maiden ewes in southern Australia

The contribution of abortions to the overall mortality of lambs born to maiden (primiparous) ewes in Australia remains unclear. This cohort study aimed to quantify abortion and lamb mortality for ewe lambs and maiden Merino two-tooth ewes. Lamb mortality from pregnancy scanning to marking were determined for 19 ewe lamb and 11 Merino two-tooth ewe flocks across southern Australia. Average lamb mortality from scanning to marking was 35.8% (range 14.3–71.1%) for the ewe lambs and 29.4% (range 19.7–52.7%) for the two-tooth ewes. Mid-pregnancy abortion was detected in 5.7% of ewes (range 0–50%) in the ewe lamb flocks and 0.9% of ewes (range 0–4.4%) in the two-tooth ewe flocks. Mid-pregnancy abortion affecting ≥2% of ewes was observed in 6/19 ewe lamb flocks and 2/11 two-tooth ewe flocks. Lamb mortality from birth to marking represented the greatest contributor to foetal and lamb mortality after scanning, but mid-pregnancy abortion was an important contributor to lamb mortality in some ewe lamb flocks. Variability between the flocks indicates scope to improve the overall reproductive performance for maiden ewes by reducing foetal and lamb losses. Addressing mid-pregnancy abortion may improve the reproductive performance in some flocks

Buoyancy-induced time delays in Babcock-Leighton flux-transport dynamo models

The Sun is a magnetic star whose cyclic activity is thought to be linked to internal dynamo mechanisms. A combination of numerical modelling with various levels of complexity is an efficient and accurate tool to investigate such intricate dynamical processes. We investigate the role of the magnetic buoyancy process in 2D Babcock-Leighton dynamo models, by modelling more accurately the surface source term for poloidal field. Methods. To do so, we reintroduce in mean-field models the results of full 3D MHD calculations of the non-linear evolution of a rising flux tube in a convective shell. More specifically, the Babcock-Leighton source term is modified to take into account the delay introduced by the rise time of the toroidal structures from the base of the convection zone to the solar surface. We find that the time delays introduced in the equations produce large temporal modulation of the cycle amplitude even when strong and thus rapidly rising flux tubes are considered. Aperiodic modulations of the solar cycle appear after a sequence of period doubling bifurcations typical of non-linear systems. The strong effects introduced even by small delays is found to be due to the dependence of the delays on the magnetic field strength at the base of the convection zone, the modulation being much less when time delays remain constant. We do not find any significant influence on the cycle period except when the delays are made artificially strong. A possible new origin of the solar cycle variability is here revealed. This modulated activity and the resulting butterfly diagram are then more compatible with observations than what the standard Babcock-Leighton model produces.Comment: 13 pages, 10 figures, accepted for publication in A&

Neural Networks for State Evaluation in General Game Playing

Abstract. Unlike traditional game playing, General Game Playing is concerned with agents capable of playing classes of games. Given the rules of an unknown game, the agent is supposed to play well without human intervention. For this purpose, agent systems that use deterministic game tree search need to automatically construct a state value function to guide search. Successful systems of this type use evaluation functions derived solely from the game rules, thus neglecting further improvements by experience. In addition, these functions are fixed in their form and do not necessarily capture the game’s real state value function. In this work we present an approach for obtaining evaluation functions on the basis of neural networks that overcomes the aforementioned problems. A network initialization extracted from the game rules ensures reasonable behavior without the need for prior training. Later training, however, can lead to significant improvements in evaluation quality, as our results indicate.

Influence of through-flow on linear pattern formation properties in binary mixture convection

We investigate how a horizontal plane Poiseuille shear flow changes linear convection properties in binary fluid layers heated from below. The full linear field equations are solved with a shooting method for realistic top and bottom boundary conditions. Through-flow induced changes of the bifurcation thresholds (stability boundaries) for different types of convective solutions are deter- mined in the control parameter space spanned by Rayleigh number, Soret coupling (positive as well as negative), and through-flow Reynolds number. We elucidate the through-flow induced lifting of the Hopf symmetry degeneracy of left and right traveling waves in mixtures with negative Soret coupling. Finally we determine with a saddle point analysis of the complex dispersion relation of the field equations over the complex wave number plane the borders between absolute and convective instabilities for different types of perturbations in comparison with the appropriate Ginzburg-Landau amplitude equation approximation. PACS:47.20.-k,47.20.Bp, 47.15.-x,47.54.+rComment: 19 pages, 15 Postscript figure

Design for Behaviour Change as a Driver for Sustainable Innovation: Challenges and Opportunities for Implementation in the Private and Public Sectors

Over the last decade, design for behaviour change has become increasingly recognised as a strategy for enabling social change. Despite this, we are far from understanding its implementation, especially through the private and public sectors. This study has surveyed private and public sector stakeholders with regard to their current knowledge of, and approach to, design for behaviour change. The aim was to identify the challenges for professional stakeholders in understanding, accessing and implementing design for behaviour change. Underpinned by a literature review of design for behaviour change theories and approaches, an online survey and two focus groups with private and public sector stakeholders were conducted with particular focus on small and medium size enterprises (SMEs). The results identified that there is a significant disconnect between available theoretical knowledge of design for behaviour change and its practical implementation. Reasons for this include a lack of awareness and common language, of evidence based examples, and of evaluation methods and inter-sector collaborations. In response, a set of recommendations has been developed to propose ways forward for the wider understanding and application of design for behaviour change