Sustainable building solutions: a review of lessons from the natural world

The realisation that much of conventional. modern architecture is not sustainable over the long term is not new. Typical approaches are aimed at using energy and materials more efficiently. However, by clearly understanding the natural processes and their interactions with human needs in view, designers can create buildings that are delightful. functional productive and regenerative by design. The paper aims to review the biomimetics literature that is relevant to building materials and design. Biomimetics is the abstraction of good design from Nature, an enabling interdisciplinary science. particularly interested in emerging properties of materials and structures as a result of their hierarchical organisation. Biomimetics provides ideas relevant to: graded functionality of materials (nano-scale), adaptive response (nano-, micro-. and macro-scales): integrated intelligence (sensing and actuation at all scales), architecture and additional functionality. There are many examples in biology where emergent response of plants and animals to temperature, humidity and other changes in their physical environments is based on relatively simple physical principles. However, the implementation of design solutions which exploit these principles is where inspiration for man-made structures should be. We analyse specific examples of sustainability from Nature and the benefits or value that these solutions have brought to different creatures. By doing this, we appreciate how the natural world fits into the world of sustainable buildings and how as building engineers we can value its true application in delivering sustainable building.

A benchmark-driven modelling approach for evaluating deployment choices on a multicore architecture

The complexity of current and emerging high performance architectures provides users with options about how best to use the available resources, but makes predicting performance challenging. In this work a benchmark-driven performance modelling approach is outlined that is appro- priate for modern multicore architectures. The approach is demonstrated by constructing a model of a simple shallow water code on a Cray XE6 system, from application-specific benchmarks that illustrate precisely how architectural char- acteristics impact performance. The model is found to recre- ate observed scaling behaviour up to 16K cores, and used to predict optimal rank-core affinity strategies, exemplifying the type of problem such a model can be used for.

A benchmark-driven modelling approach for evaluating deployment choices on a multi-core architecture

The complexity of current and emerging architectures provides users with options about how best to use the available resources, but makes predicting performance challenging. In this work a benchmark-driven model is developed for a simple shallow water code on a Cray XE6 system, to explore how deployment choices such as domain decomposition and core affinity affect performance. The resource sharing present in modern multi-core architectures adds various levels of heterogeneity to the system. Shared resources often includes cache, memory, network controllers and in some cases floating point units (as in the AMD Bulldozer), which mean that the access time depends on the mapping of application tasks, and the core's location within the system. Heterogeneity further increases with the use of hardware-accelerators such as GPUs and the Intel Xeon Phi, where many specialist cores are attached to general-purpose cores. This trend for shared resources and non-uniform cores is expected to continue into the exascale era. The complexity of these systems means that various runtime scenarios are possible, and it has been found that under-populating nodes, altering the domain decomposition and non-standard task to core mappings can dramatically alter performance. To find this out, however, is often a process of trial and error. To better inform this process, a performance model was developed for a simple regular grid-based kernel code, shallow. The code comprises two distinct types of work, loop-based array updates and nearest-neighbour halo-exchanges. Separate performance models were developed for each part, both based on a similar methodology. Application specific benchmarks were run to measure performance for different problem sizes under different execution scenarios. These results were then fed into a performance model that derives resource usage for a given deployment scenario, with interpolation between results as necessary.