The cross product of interconnection networks

We study the cross product as a method for generating and analyzing interconnection network topologies for multiprocessor systems. Consider two interconnection graphs G₁ and G₂ each with some established properties such as symmetry, low degree and diameter, scalability, simple optimal routing, recursive structure (partitionability), fault tolerance, existence of node-disjoint paths, low cost embedding, and efficient broadcasting. We investigate and evaluate the corresponding properties for the cross product of G₁ and G₂ based on the properties of G₁ and those of G₂. We also give a mathematical characterization of product families of graphs which are closed under the cross product operation. This investigation is useful in two ways. On one hand, it gives a new tool for further studying some of the known interconnection topologies, such as the hypercube and the mesh, which can be defined using the cross product operation. On the other hand, it can be used in defining and evaluating new interconnection graphs using the cross product operation on known topologies     

Topological properties of OTIS-networks

We conduct a general study of the topological properties of optical transpose interconnection systems (OTIS). We first obtain their basic topological metrics of size, degree, shortest distance and diameter, and then we obtain results related to the recursive structure and efficient embedding of meshes, cubes, spanning trees and cycles. We also present minimal one-to-one routing and optimal broadcasting algorithms, and we show how to construct node-disjoint paths between any two nodes of an OTIS network. Recent studies have addressed only particular members of the general class of OTIS networks. In this paper, we present unified tools for obtaining the topological properties of an arbitrary OTIS network based on the properties of the corresponding factor network     

Accelerating compute-intensive image segmentation algorithms using GPUs

Image segmentation is an important process that facilitates image analysis such as in object detection. Because of its importance, many different algorithms were proposed in the last decade to enhance image segmentation techniques. Clustering algorithms are among the most popular in image segmentation. The proposed algorithms differ in their accuracy and computational efficiency. This paper studies the most famous and new clustering algorithms and provides an analysis on their feasibility for parallel implementation. We have studied four algorithms which are: fuzzy C-mean, type-2 fuzzy C-mean, interval type-2 fuzzy C-mean, and modified interval type-2 fuzzy C-mean. We have implemented them in a sequential (CPU only) and a parallel hybrid CPU–GPU version. Speedup gains of 6\(\times \) to 20\(\times \) were achieved in the parallel implementation over the sequential implementation. We detail in this paper our discoveries on the portions of the algorithms that are highly parallel so as to help the image processing community, especially if these algorithms are to be used in real-time processing where efficient computation is critical.     

Improving the performance of the needleman-wunsch algorithm using parallelization and vectorization techniques

The Needleman-Wunsch (NW) is a dynamic programming algorithm used in the pairwise global alignment of two biological sequences. In this paper, three sets of parallel implementations of the NW algorithm are presented using a mixture of specialized software and hardware solutions: POSIX Threads-based, SIMD Extensions-based and a GPU-based implementations. The three implementations aim at improving the performance of the NW algorithm on large scale input without affecting its accuracy. Our experiments show that the GPU-based implementation is the best implementation as it achieves performance 72.5X faster than the sequential implementation, whereas the best performance achieved by the POSIX threads and the SIMD techniques are 2X and 18.2X faster than the sequential implementation, respectively.

     

Collaboration networks of arab biomedical researchers

Social networks (SN) consist of a set of actors and connections between them. A collaboration network (ColNet) is a special type of SN, in which the actors represent researchers and the link between them indicate that they have co-authored at least one paper. ColNet analysis reveals how researchers interact and behave. A wide range of applications can be based on such studies. The current works on ColNet usually focus on a specific domain/discipline, country/geographical region or time interval. In our study, we focus on one of the understudied regions (the Arab world), and present a novel study on the ColNet of researchers in this region. The domain of interest in our study is biomedicine. We construct, analyze, and study ColNet of biomedical researchers in the Arab world. We divide the region of interest (the Arab world) into four geographical regions and look into the evolution of ColNet of each region separately over time. Our analysis reveals that there is an increase in the number of both authors and publications over time, and that authors tend to work in increasingly larger groups rather than working individually, which is consistent with what is assumed about the nature of research in this field. Our analysis also reveals that a researcher’s productivity is correlated with the amount of change in his/her circle of collaborators over time. For example, researchers working in stable or fixed groups and researchers who have completely different research group every few years are not necessarily the most productive ones.

     

Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations

Medical image processing is one of the most famous image processing fields in this era. This fame comes because of the big revolution in information technology that is used to diagnose many illnesses and saves patients lives. There are many image processing techniques used in this field, such as image reconstructing, image segmentation and many more. Image segmentation is a mandatory step in many image processing based diagnosis procedures. Many segmentation algorithms use clustering approach. In this paper, we focus on Fuzzy C-Means based segmentation algorithms because of the segmentation accuracy they provide. In many cases, these algorithms need long execution times. In this paper, we accelerate the execution time of these algorithms using Graphics Process Unit (GPU) capabilities. We achieve performance enhancement by up to 8.9x without compromising the segmentation accuracy.     

Collusion attacks mitigation in internet of things: a fog based model

Multimedia Tools and Applications - Collusion attacks are among the major security concerns nowadays due to the growth exposure in networks and communications. Internet of Things (IoT) environments...     

On the Performance of Parallel Matrix Factorisation on the Hypermesh

Most common multicomputer networks, e.g. d-ary h-cubes, are graph topologies where an edge (channel) interconnects exactly two vertices (nodes). Hypergraphs are a generalisation of the graph model, where a channel interconnects an arbitrary number of nodes. Previous studies have used synthetic workloads (e.g. statistical distributions) to stress the superior performance characteristics of regular multi-dimensional hypergraphs, also known as hypermeshes, over d-ary h-cubes. There has been, however, hardly any study that has considered real-world parallel applications. This paper contributes towards filling this gap by providing a comparative study of the performance of one of the most common numerical problems, namely matrix factorisation, on the hypermesh, hypercube, and d-ary h-cube. To this end, the paper first introduces orthogonal networks as a unified model for describing both the graph and hypergraph topologies. It then develops a generalised parallel algorithm for matrix factorisation and evaluates its performance on the hypermesh, hypercube and d-ary h-cube. The results reveal that the hypermesh supports matrix computation more efficiently, and therefore provides more evidence of the hypermesh as a viable network for future large-scale multicomputers.     

Fault diameter of k-ary n-cube networks

We obtain the fault diameter of k-ary n-cube interconnection networks (also known as n-dimensional k-torus networks). We start by constructing a complete set of node-disjoint paths (i.e., as many paths as the degree) between any two nodes of a k-ary n-cube. Each of the obtained paths is of length zero, two, or four plus the minimum length except for one path in a special case (when the Hamming distance between the two nodes is one) where the increase over the minimum length may attain eight. These results improve those obtained by B. Bose et al. (1995) where the length of some of the paths has a variable increase (which can be arbitrarily large) over the minimum length. These results are then used to derive the fault diameter of the k-ary n-cube, which is shown to be Δ+1 where Δ is the fault free diameter     

Accelerating 3D medical volume segmentation using GPUs

Multimedia Tools and Applications - Medical images have an undeniably integral role in the process of diagnosing and treating of a very large number of ailments. Processing such images (for...