Parallel dynamic graph-partitioning for unstructured meshes

A parallel method for the dynamic partitioning of unstructured meshes is described. The method introduces a new iterative optimization technique known as relative gain optimization which both balances the workload and attempts to minimize the interprocessor communications overhead. Experiments on a series of adaptively refined meshes indicate that the algorithm provides partitions of an equivalent or higher quality to static partitioners (which do not reuse the existing partition) and much more rapidly. Perhaps more importantly, the algorithm results in only a small fraction of the amount of data migration compared to the static partitioners.

Parallel dynamic load-balancing for adaptive unstructured meshes

This chapter describes a parallel optimization technique that incorporates a distributed load-balancing algorithm and provides an extremely fast solution to the problem of load-balancing adaptive unstructured meshes. Moreover, a parallel graph contraction technique can be employed to enhance the partition quality and the resulting strategy outperforms or matches results from existing state-of-the-art static mesh partitioning algorithms. The strategy can also be applied to static partitioning problems. Dynamic procedures have been found to be much faster than static techniques, to provide partitions of similar or higher quality and, in comparison, involve the migration of a fraction of the data. The method employs a new iterative optimization technique that balances the workload and attempts to minimize the interprocessor communications overhead. Experiments on a series of adaptively refined meshes indicate that the algorithm provides partitions of an equivalent or higher quality to static partitioners (which do not reuse the existing partition) and much more quickly. The dynamic evolution of load has three major influences on possible partitioning techniques; cost, reuse, and parallelism. The unstructured mesh may be modified every few time-steps and so the load-balancing must have a low cost relative to that of the solution algorithm in between remeshing.

Using a modified shepards method for optimization of a nanoparticulate cyclosporine a formulation prepared by a static mixer technique

An innovative methodology has been used for the formulation development of Cyclosporine A (CyA) nanoparticles. In the present study the static mixer technique, which is a novel method for producing nanoparticles, was employed. The formulation optimum was calculated by the modified Shepard's method (MSM), an advanced data analysis technique not adopted so far in pharmaceutical applications. Controlled precipitation was achieved injecting the organic CyA solution rapidly into an aqueous protective solution by means of a static mixer. Furthermore the computer based MSM was implemented for data analysis, visualization, and application development. For the optimization studies, the gelatin/lipoid S75 amounts and the organic/aqueous phase were selected as independent variables while the obtained particle size as a dependent variable. The optimum predicted formulation was characterized by cryo-TEM microscopy, particle size measurements, stability, and in vitro release. The produced nanoparticles contain drug in amorphous state and decreased amounts of stabilizing agents. The dissolution rate of the lyophilized powder was significantly enhanced in the first 2 h. MSM was proved capable to interpret in detail and to predict with high accuracy the optimum formulation. The mixer technique was proved capable to develop CyA nanoparticulate formulations.

Fast divide-and-conquer algorithms for preemptive scheduling problems with controllable processing times - a polymatroid optimization approach

We consider a variety of preemptive scheduling problems with controllable processing times on a single machine and on identical/uniform parallel machines, where the objective is to minimize the total compression cost. In this paper, we propose fast divide-and-conquer algorithms for these scheduling problems. Our approach is based on the observation that each scheduling problem we discuss can be formulated as a polymatroid optimization problem. We develop a novel divide-and-conquer technique for the polymatroid optimization problem and then apply it to each scheduling problem. We show that each scheduling problem can be solved in $ \O({\rm T}_{\rm feas}(n) \times\log n)$ time by using our divide-and-conquer technique, where n is the number of jobs and Tfeas(n) denotes the time complexity of the corresponding feasible scheduling problem with n jobs. This approach yields faster algorithms for most of the scheduling problems discussed in this paper.