A Comparative Study of Completion Challenges Facing Regular and Parallel Degree Students of Egerton University Constituent Colleges, Kenya.

This study purposed to identify the completion challenges faced by parallel in comparison to regular degree students in their studies in constituent university colleges of Egerton university. The study was conducted in Kisii and Laikipia university colleges which until recently were university colleges of Egerton university. The study involved 607 and 332 parallel and regular degree students in Laikipia University, 191 and 177 parallel and Regular students in Laikipia university college respectively. Also included in the study were 15 and 25 regular lecturers in Kisii University College and the Deans of students’ and Academic registrars in Kisii and Laikipia university colleges. The study adopted a comparative design as it was appropriate in the investigation of different independent groups of university students. The samples were selected by use of both purposive and snowball sampling techniques and the data was obtained by use of questionnaires. The data collected was presented by use of tables and bar graphs and it was analyzed by use of descriptive statistics. The study found that fewer students in the parallel degree programme were provided with loans by HELB while the general public was not willing to fund their education. The study also established that students’ welfare with regard to access to morning and evening lectures, participation in co-curricular activities and selection of courses was not catered for. The students had challenges in accessing their lecture halls, Dean of students, lecturers and libraries. This had an impact on the completion rates of parallel degree students compared to the regular students. From the findings of the study recommendations were made to HELB, university management, local administration, parents and other stakeholders on how the situation could be improved. Key words: Completion, Parallel Degree Programme, Regular Degree Programme, Laikipia University College, Kisii University College

Parallel sorting by regular sampling

ABSTRACT A new parallel sorting algorithm suitable for MIMD multiprocessors is presented. The algorithm reduces memory and bus contention, which many parallel sorting algorithms suffer from, by using a regular sampling of the data to ensure good pivot selection. For n data elements to be sorted and p processors, when n ≥ p 3 the algorithm is shown to be asymptotically optimal. In theory, the algorithm is within a factor of two of achieving ideal load balancing. In practice, there is almost perfect partitioning of work. On a variety of shared and distributed memory machines, the algorithm achieves better than half-linear speedups. -4

EGOIST: Overlay Routing Using Selfish Neighbor Selection

A foundational issue underlying many overlay network applications ranging from routing to P2P file sharing is that of connectivity management, i.e., folding new arrivals into an existing overlay, and re-wiring to cope with changing network conditions. Previous work has considered the problem from two perspectives: devising practical heuristics for specific applications designed to work well in real deployments, and providing abstractions for the underlying problem that are analytically tractable, especially via game-theoretic analysis. In this paper, we unify these two thrusts by using insights gleaned from novel, realistic theoretic models in the design of Egoist – a prototype overlay routing system that we implemented, deployed, and evaluated on PlanetLab. Using measurements on PlanetLab and trace-based simulations, we demonstrate that Egoist's neighbor selection primitives significantly outperform existing heuristics on a variety of performance metrics, including delay, available bandwidth, and node utilization. Moreover, we demonstrate that Egoist is competitive with an optimal, but unscalable full-mesh approach, remains highly effective under significant churn, is robust to cheating, and incurs minimal overhead. Finally, we discuss some of the potential benefits Egoist may offer to applications.National Science Foundation (CISE/CSR 0720604, ENG/EFRI 0735974, CISE/CNS 0524477, CNS/NeTS 0520166, CNS/ITR 0205294; CISE/EIA RI 0202067; CAREER 04446522); European Commission (RIDS-011923

Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization

We propose a decomposition framework for the parallel optimization of the sum of a differentiable {(possibly nonconvex)} function and a nonsmooth (possibly nonseparable), convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. The main contribution of this work is a novel \emph{parallel, hybrid random/deterministic} decomposition scheme wherein, at each iteration, a subset of (block) variables is updated at the same time by minimizing local convex approximations of the original nonconvex function. To tackle with huge-scale problems, the (block) variables to be updated are chosen according to a \emph{mixed random and deterministic} procedure, which captures the advantages of both pure deterministic and random update-based schemes. Almost sure convergence of the proposed scheme is established. Numerical results show that on huge-scale problems the proposed hybrid random/deterministic algorithm outperforms both random and deterministic schemes.Comment: The order of the authors is alphabetica

Deterministic Versus Randomized Kaczmarz Iterative Projection

Kaczmarz's alternating projection method has been widely used for solving a consistent (mostly over-determined) linear system of equations Ax=b. Because of its simple iterative nature with light computation, this method was successfully applied in computerized tomography. Since tomography generates a matrix A with highly coherent rows, randomized Kaczmarz algorithm is expected to provide faster convergence as it picks a row for each iteration at random, based on a certain probability distribution. It was recently shown that picking a row at random, proportional with its norm, makes the iteration converge exponentially in expectation with a decay constant that depends on the scaled condition number of A and not the number of equations. Since Kaczmarz's method is a subspace projection method, the convergence rate for simple Kaczmarz algorithm was developed in terms of subspace angles. This paper provides analyses of simple and randomized Kaczmarz algorithms and explain the link between them. It also propose new versions of randomization that may speed up convergence