Livelihood diversification and the expansion of artisanal mining in rural Tanzania: drivers and policy implications

This paper provides an extended analysis of livelihood diversification in rural Tanzania, with special emphasis on artisanal and small-scale mining (ASM). Over the past decade, this sector of industry, which is labour-intensive and comprises an array of rudimentary and semi-mechanized operations, has become an indispensable economic activity throughout Sub-Saharan Africa, providing employment to a host of redundant public sector workers, retrenched large-scale mine labourers and poor farmers. In many of the region’s rural areas, it is overtaking subsistence agriculture as the primary industry. Such a pattern appears to be unfolding within the Morogoro and Mbeya regions of southern Tanzania, where findings from recent research suggest that a growing number of smallholder farmers are turning to ASM for employment and financial support. It is imperative that national rural development programmes take this trend into account and provide support to these people

Performance modelling of magnetohydrodynamics codes

Performance modelling is an important tool utilised by the High Performance Computing industry to accurately predict the run-time of science applications on a variety of different architectures. Performance models aid in procurement decisions and help to highlight areas for possible code optimisations. This paper presents a performance model for a magnetohydrodynamics physics application, Lare. We demonstrate that this model is capable of accurately predicting the run-time of Lare across multiple platforms with an accuracy of 90% (for both strong and weak scaled problems). We then utilise this model to evaluate the performance of future optimisations. The model is generated using SST/macro, the machine level component of the Structural Simulation Toolkit (SST) from Sandia National Laboratories, and is validated on both a commodity cluster located at the University of Warwick and a large scale capability resource located at Lawrence Livermore National Laboratory