Women's work choices in Kenya: the role of social institutions and household gender attitudes

This study considers the factors that influence women’s work behavior in Kenya. In particular, it examines whether gender attitudes and certain types of social institutions influence the probability of employment or type of employment for women. Using data from the Demographic and Health Survey of 2008–9, we find that religion and ethnicity are significant determinants of women’s employment in Kenya. While personal experience of female genital mutilation is insignificant, spousal age and education differences, as well as marital status (which reflect attitudes both in women’s natal and marital families), are significant determinants of women’s employment choices.

Visualizing knowledge in project-based work

This article considers how visual practices are used to manage knowledge in project-based work. It compares project-based work in a capital goods manufacturer and an architectural firm. Visual representations are used extensively in both cases, but the nature of visual practice differs significantly between the two. The research explores the kinds of knowledge that are (and aren't) developed and made visible in strategizing and planning activities. For example, whereas the emphasis of project-based work in the former firm is on exploitation of knowledge and it visualizes its project context largely in commercial and processual terms, the emphasis in the latter is on exploration and it uses a wide range of visual materials to understand physical interdependencies across the project boundary. We contend particular kinds of visual tools can help project teams step between exploration and exploitation within a project, and articulate the types of representations, foci of attention and patterns of interaction involved. The findings suggest that business managers can make more deliberate choices about how knowledge is made visible, and can change visual practice to align the project with exploring and exploiting opportunities. It raises the question: What don't you see within your organization? The work contributes to academic debates about managing through projects, strategising and organizing, while the focus on visual representation disrupts the tacit-codified dichotomy in the broad debate on knowledge and learning, and highlights the craft skills central to strategizing and organizing.

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.