Analyzing the performance of optical multistage interconnection networks with limited crosstalk

Analytical modeling techniques can be used to study the performance of optical multistage interconnection network (OMIN) effectively. MINs have assumed importance in recent times, because of their cost-effectiveness. An N×N MIN consists of a mapping from N processors to N memories, with log ₂ N stages of 2×2 switches with N/2 switches per stage. The interest is on the study of the performance of unbuffered optical multistage interconnection network using the banyan network. The uniform reference model approach is assumed for the purpose of analysis. In this paper the analytical modeling approach is applied to an N×N OMIN with limited crosstalk (conflicts between messages) up to (log ₂ N−1). Messages with switch conflicts satisfying the constraint of (log ₂ N−1) are allowed to pass in the same group, but in case of a link conflict, the message is routed in a different group. The analysis is performed by calculating the bandwidth and throughput of the network operating under a load l and allowing random traffic and using a greedy routing strategy. A number of equations are derived using the theory of probability and the performance curves are plotted. The results obtained show that the performance of the network improves by allowing limited crosstalk in the network.

Some global properties of neural networks

Some properties of the global behaviour of a model of neural network are considered.The geometric concept of quadrant-degeneration is studied and it is shown to be independent of the algebraic concept of rank-degeneration. The results obtained are employed to solve some global problems of synthesis (i.e., independent of the initial state of the network) without the use of the theory of linear inequalities.     

Local modeling of global interactome networks

MOTIVATION: Systems biology requires accurate models of protein complexes, including physical interactions that assemble and regulate these molecular machines. Yeast two-hybrid (Y2H) and affinity-purification/mass-spectrometry (AP-MS) technologies measure different protein-protein relationships, and issues of completeness, sensitivity and specificity fuel debate over which is best for high-throughput 'interactome' data collection. Static graphs currently used to model Y2H and AP-MS data neglect dynamic and spatial aspects of macromolecular complexes and pleiotropic protein function. RESULTS: We apply the local modeling methodology proposed by Scholtens and Gentleman (2004) to two publicly available datasets and demonstrate its uses, interpretation and limitations. Specifically, we use this technology to address four major issues pertaining to protein-protein networks. (1) We motivate the need to move from static global interactome graphs to local protein complex models. (2) We formally show that accurate local interactome models require both Y2H and AP-MS data, even in idealized situations. (3) We briefly discuss experimental design issues and how bait selection affects interpretability of results. (4) We point to the implications of local modeling for systems biology including functional annotation, new complex prediction, pathway interactivity and coordination with gene-expression data. AVAILABILITY: The local modeling algorithm and all protein complex estimates reported here can be found in the R package apComplex, available at http://www.bioconductor.org CONTACT: dscholtens@northwestern.edu SUPPLEMENTARY INFORMATION: http://daisy.prevmed.northwestern.edu/~denise/pubs/LocalModeling     

Performance analysis of MPI collective operations

Previous studies of application usage show that the performance of collective communications are critical for high-performance computing. Despite active research in the field, both general and feasible solution to the optimization of collective communication problem is still missing. In this paper, we analyze and attempt to improve intra-cluster collective communication in the context of the widely deployed MPI programming paradigm by extending accepted models of point-to-point communication, such as Hockney, LogP/LogGP, and PLogP, to collective operations. We compare the predictions from models against the experimentally gathered data and using these results, construct optimal decision function for broadcast collective. We quantitatively compare the quality of the model-based decision functions to the experimentally-optimal one. Additionally, in this work, we also introduce a new form of an optimized tree-based broadcast algorithm, splitted-binary. Our results show that all of the models can provide useful insights into various aspects of the different algorithms as well as their relative performance. Still, based on our findings, we believe that the complete reliance on models would not yield optimal results. In addition, our experimental results have identified the gap parameter as being the most critical for accurate modeling of both the classical point-to-point-based pipeline and our extensions to fan-out topologies.

From local to global dilemmas in social networks

Social networks affect in such a fundamental way the dynamics of the population they support that the global, population-wide behavior that one observes often bears no relation to the individual processes it stems from. Up to now, linking the global networked dynamics to such individual mechanisms has remained elusive. Here we study the evolution of cooperation in networked populations and let individuals interact via a 2-person Prisoner's Dilemma--a characteristic defection dominant social dilemma of cooperation. We show how homogeneous networks transform a Prisoner's Dilemma into a population-wide evolutionary dynamics that promotes the coexistence between cooperators and defectors, while heterogeneous networks promote their coordination. To this end, we define a dynamic variable that allows us to track the self-organization of cooperators when co-evolving with defectors in networked populations. Using the same variable, we show how the global dynamics--and effective dilemma--co-evolves with the motifs of cooperators in the population, the overall emergence of cooperation depending sensitively on this co-evolution.     

Visualizing global properties of large complex networks

For complex biological networks, graphical representations are highly desired for understanding some design principles, but few drawing methods are available that capture topological features of a large and highly heterogeneous network, such as a protein interaction network. Here we propose the circular perspective drawing (CPD) method to visualize global structures of large complex networks. The presented CPD combines the quasi-continuous search (QCS) analogous to the steepest descent method with a random node swapping strategy for an enhanced calculation speed. The CPD depicts a network in an aesthetic manner by showing connection patterns between different parts of the network instead of detailed links between nodes. Global structural features of networks exhibited by CPD provide clues toward a comprehensive understanding of the network organizations. Availability: Software is freely available at http://www.cadlive.jp.     

A new family of Cayley graph interconnection networks based on wreath product and its topological properties

This paper introduces a new type of Cayley graphs for building large-scale interconnection networks, namely WG_n^2m\mathit{WG}_{n}^{2m}, whose vertex degree is m+3 when n≥3 and is m+2 when n=2. A routing algorithm for the proposed graph is also presented, and the upper bound of the diameter is deduced as ⌊5n/2⌋. Moreover, the embedding properties and maximal fault tolerance are also analyzed. Finally, we compare the proposed networks with some other similar network topologies. It is found that WG_n^2m\mathit{WG}_{n}^{2m} is superior to other interconnection networks because it helps to construct large-scale networks with lower cost.     

Biobanks: transnational, European and global networks.

Biobanks contain biological samples and associated information that are essential raw materials for advancement of biotechnology, human health, and research and development in life sciences. Population-based and disease-oriented biobanks are major biobank formats to establish the disease relevance of human genes and provide opportunities to elucidate their interaction with environment and lifestyle. The developments in personalized medicine require molecular definition of new disease subentities and biomarkers for identification of relevant patient subgroups for drug development. These emerging demands can only be met if biobanks cooperate at the transnational or even global scale. Establishment of common standards and strategies to cope with the heterogeneous legal and ethical landscape in different countries are seen as major challenges for biobank networks. The Central Research Infrastructure for Molecular Pathology (CRIP), the concept for a pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI), and the Organization for Economic Co-operation and Development (OECD) global Biological Resources Centres network are examples for transnational, European and global biobank networks that are described in this article.     

The properties of global risk networks and corresponding risk management strategies

Global risks interconnect with one another and threaten our society as a highly interdependent system. However, knowledge on how risk materializations influence one another is limited. Thus, this study provides a network model to measure risk interdependence and presents recommendations on the integrative risk management framework.

This study develops a global risk network and calculates its key indicators of structural characteristics. Results show that global risks are closely interconnected and a particular triangle relationship exists among environmental, geopolitical, and societal risks. To improve the resilience of a risk system, the systematic important risks, which are “man-made environmental catastrophes” and “interstate conflict” in this study, should be monitored and controlled. Furthermore, we simplify the risk system by entirely managing risks in the same group based on our reclassification. Our method is considerably effective in detecting upcoming crisis and assists supervisors take timely action to prevent crisis.     

Performance management of manufacturing system networks

Performance management of communication networks is critical for speed, reliability, and flexibility of information exchange between different components, subsystems, and sectors (e.g., factory, engineering design, and administration) of production process organizations in the environment of computer integrated manufacturing (CIM). Essential to this distributed total manufacturing system is the integrated communications network over which the information leading to process interactions and plant management and control is exchanged. Such a network must be capable of handling heterogeneous traffic resulting from intermachine communications at the factory floor, CAD drawings, design specifications, and administrative information. The objective is to improve the efficiency in handling various types of messages, e.g., control signals, sensor data, and production orders, by on-line adjustment of the parameters of the network protocol. This paper presents a conceptual design, development, and implementation of a network performance management scheme for CIM applications including flexible manufacturing. The performance management algorithm is formulated using the concepts of: (1) Perturbation analysis of discrete event dynamic systems; (2) stochastic approximation; and (3) learning automata. The proposed concept for performance management can also serve as a general framework to assist design, operation, and management of flexible manufacturing systems. The performance management procedure has been tested via emulation on a network test bed that is based on the manufacturing automation protocol (MAP) which has been widely used for CIM networking. The conceptual design presented in this paper offers a step forward to bridging the gap between management standards and users' demands for efficient network operations since most standards such as ISO and IEEE address only the architecture, services, and interfaces for network management.     

How the global structure of protein interaction networks evolves

Two processes can influence the evolution of protein interaction networks: addition and elimination of interactions between proteins, and gene duplications increasing the number of proteins and interactions. The rates of these processes can be estimated from available Saccharomyces cerevisiae genome data and are sufficiently high to affect network structure on short time-scales. For instance, more than 100 interactions may be added to the yeast network every million years, a fraction of which adds previously unconnected proteins to the network. Highly connected proteins show a greater rate of interaction turnover than proteins with few interactions. From these observations one can explain (without natural selection on global network structure) the evolutionary sustenance of the most prominent network feature, the distribution of the frequency P(d) of proteins with d neighbours, which is broad-tailed and consistent with a power law, that is: P(d) proportional, variant d (-gamma).     

Cardiac evaluation and risk reduction in patients undergoing major vascular operations.

Occult coronary artery disease often accompanies symptomatic peripheral vascular disease and has an important effect on survival. Most perioperative and late fatalities after peripheral vascular operations are due to cardiac causes. Noninvasive cardiac testing can identify patients at increased risk for postoperative cardiac complications, although controversy exists regarding the optimal preoperative evaluation. Risk reduction strategies for patients known to be at high risk are also controversial. Some authors advocate coronary revascularization with coronary artery bypass grafting or percutaneous transluminal coronary angioplasty before the vascular procedure. Others believe that the combined morbidity and mortality of 2 operations exceed those of a peripheral vascular operation performed with aggressive monitoring and medical therapy. Continuous electrocardiographic monitoring after an operation has identified silent myocardial ischemia as a powerful predictor of cardiac complications. Ongoing research is likely to provide insights into the pathogenesis of postoperative cardiac complications and may lead to specific therapeutic interventions. Few prospective studies have been done in this area, and the threshold for preoperative and postoperative intervention is unknown. I review the literature and present an algorithm to guide cardiac testing and risk reduction in patients undergoing elective vascular surgical procedures.     

Design principles of molecular networks revealed by global comparisons and composite motifs

Background  

Molecular networks are of current interest, particularly with the publication of many large-scale datasets. Previous analyses have focused on topologic structures of individual networks.     

Performance analysis of virtual clusters in personal communication networks

The objective of the paper is to analyze the performance of virtual cluster architectures in wireless networks. The key issues in wireless domain are flooding, connectivity, and power management. These issues arise during the path finding and maintenance between source and destination nodes. To overcome these issues three approaches are introduced in this paper namely; fusion virtual structure, link quality connected dominating set and cluster backbone approach. These approaches follow the distributed localized computations for virtual cluster constructions and focus on fundamental connectivity problems and are partially involved in power saving process of individual nodes. The proposed methods are analyzed in terms of backbone size, packet delivery ratio and normalized routing overhead and the results are witnessed by simulation.     

Software tools and databases for bacterial systematics and their disseminationvia global networks

The dynamic expansion of the taxonomic knowledge base is fundamental to further developments in biotechnology and sustainable conservation strategies. The vast array of software tools for numerical taxonomy and probabilistic identification, in conjunction with automated systems for data generation are allowing the construction of large computerised strain databases. New techniques available for the generation of chemical and molecular data, associated with new software tools for data analysis, are leading to a quantum leap in bacterial systematics. The easy exchange of data through an interactive and highly distributed global computer network, such as the Internet, is facilitating the dissemination of taxonomic data. Relevant information for comparative sequence analysis, ribotyping, protein and DNA electrophoretic pattern analysis is available on-line through computerised networks. Several software packages are available for the analysis of molecular data. Nomenclatural and taxonomic Authority Files are available from different sources together with strain specific information. The increasing availability of public domain software, is leading to the establishment and integration of public domain databases all over the world, and promoting co-operative research projects on a scale never seen before.