Dynamic mesh partitioning and load-balancing for parallel computational mechanics codes

In this Chapter we discuss the load-balancing issues arising in parallel mesh based computational mechanics codes for which the processor loading changes during the run. We briefly touch on geometric repartitioning ideas and then focus on different ways of using a graph both to solve the load-balancing problem and the optimisation problem, both locally and globally. We also briefly discuss whether repartitioning is always valid. Sample illustrative results are presented and we conclude that repartitioning is an attractive option if the load changes are not too dramatic and that there is a certain trade-off between partition quality and volume of data that the underlying application needs to migrate.

A combined evolutionary search and multilevel optimisation approach to graph-partitioning

The graph-partitioning problem is to divide a graph into several pieces so that the number of vertices in each piece is the same within some defined tolerance and the number of cut edges is minimised. Important applications of the problem arise, for example, in parallel processing where data sets need to be distributed across the memory of a parallel machine. Very effective heuristic algorithms have been developed for this problem which run in real-time, but it is not known how good the partitions are since the problem is, in general, NP-complete. This paper reports an evolutionary search algorithm for finding benchmark partitions. A distinctive feature is the use of a multilevel heuristic algorithm to provide an effective crossover. The technique is tested on several example graphs and it is demonstrated that our method can achieve extremely high quality partitions significantly better than those found by the state-of-the-art graph-partitioning packages.

JOSTLE: multilevel graph partitioning software: an overview

In this chapter we look at JOSTLE, the multilevel graph-partitioning software package, and highlight some of the key research issues that it addresses. We first outline the core algorithms and place it in the context of the multilevel refinement paradigm. We then look at issues relating to its use as a tool for parallel processing and, in particular, partitioning in parallel. Since its first release in 1995, JOSTLE has been used for many mesh-based parallel scientific computing applications and so we also outline some enhancements such as multiphase mesh-partitioning, heterogeneous mapping and partitioning to optimise subdomain shape

Age and gender influences on sensory perceptions of novel low cost nutrient-rich food products developed using traditional Ghanaian food ingredients

Background: A number of factors are known to influence food preferences and acceptability of new products. These include their sensory characteristics and strong, innate neural influences. In designing foods for any target group, it is important to consider intrinsic and extrinsic characteristics which may contribute to palatability, and acceptability of foods. Objective: To assess age and gender influences on sensory perceptions of novel low cost nutrient-rich food products developed using traditional Ghanaian food ingredients. Materials and Methods: In this study, a range of food products were developed from Ghanaian traditional food sources using the Food Multimix (FMM) concept. These products were subjected to sensory evaluation to assess the role of sensory perception on their acceptability among different target age groups across the life cycle (aged 11-68 years olds) and to ascertain any possible influences of gender on preference and choice. Variables including taste, odour, texture, flavour and appearance were tested and the results captured on a Likert scale and scores of likeness and acceptability analysed. Multivariate analyses were used to develop prediction models for targeted recipe development for different target groups. Multiple factor analysis of variance (ANOVA) and logistic linear regression were employed to test the strength of acceptability and to ascertain age and gender influences on product preference. Results: The results showed a positive trend in acceptability (r = 0.602) which tended towards statistical significance (p = 0.065) with very high product favourability rating (91% acceptability; P=0.005). However, age [odds ratios=1.44 (11-15 years old) odds ratios=2.01 (18-68 years old) and gender (P=0.000)] were major influences on product preference with children and females (irrespective of age) showing clear preferences or dislike of products containing certain particular ingredients. Conclusion: These findings are potentially useful in planning recipes for feeding interventions involving different vulnerable and target groups.