High Performance Network of PC Cluster Maestro

This paper presents a design, an architecture, and performance evaluation of high-performance network of PC cluster, called Maestro. Most networks of recent clusters have been organized based on WAN or LAN technology, due to their market availability. However, communication protocols and functions of such conventional networks are not optimal for parallel computing, which requires low latency and high bandwidth communication. In this paper, we propose two optimizations for high-performance communication: (1) transferring in burst as many packets as the receiving buffer accepts at once, and (2) having each hardware component pass one data unit to another in a pipelined manner. We have developed a network interface and a switch, which are composed of dedicated hardware modules to realize these optimizations. An implementatin of the message passing library developed on Maestro cluster is also described. Performance evaluation shows that the proposed optimizations can extract the potential performance of the physical layer efficiently and improve the performance in communication.     

An Effective Logical Cache for a Clustered LRC-Based DSM System

As local-area workstation networks are widely available, the idea of offering a software distributed shared memory (SDSM) system across interconnects of clusters is quite an attractive alternative for compute-intensive applications. However, the higher cost of sending a message over an inter-cluster link compared to an intra-cluster one can limit applications' performance on a multi-cluster SDSM system. In this paper, we present the extensions that we have added to the SDSM TreadMarks, which provides the lazy release consistency (LRC) memory model, in order to adapt it to a loosely-coupled cluster-based platform. We have implemented a logical per-cluster cache that exploits cluster locality. By accessing the cache of its cluster, a processor can share data previously requested by a second processor of its cluster, thereby, minimizing, the cost of inter-cluster communication.     

The Rutgers Computational Grid: A Distributed Linux PC Cluster

The Rutgers Computational Grid (RCG) project is aimed at providing high throughput performance to Rutgers university faculty and students. The RCG employs dual processor PCs, with Pentium II and III processors, as computational nodes, running the Linux RedHat operating system. The Load Sharing Facility (LSF) scheduling system from Platform Computing is used for job control and monitoring. The nodes are grouped into subclusters physically located in several departments and controlled by a single master node through LSF. The hardware and software used in RCG are described. Utilization and performance issues, including parallel performance, are discussed based on the experience of the first two years of RCG operation.     

Performance Analysis of a Myrinet-Based Cluster

In recent years, there has been a growing interest in the cluster system as an accepted form of supercomputing, due to its high performance at an affordable cost. This paper attempts to elaborate performance analysis of Myrinet-based cluster. The communication performance and effect of background load on parallel applications were analyzed. For point-to-point communication, it was found that an extension to the Hockney's model was required to estimate the performance. The proposed model suggested that there should be two ranges to be used for the performance metrics to cope with the cache effect. Moreover, based on the extension of the point-to-point communication model, the Xu and Hwang's model for collective communication performance was also extended. Results showed that our models can make better estimation of the communication performance than the previous models. Finally, the interference of other user processes to the cluster system is evaluated by using synthetic background load generation programs.     

The MOSIX Direct File System Access Method for Supporting Scalable Cluster File Systems

MOSIX is a cluster management system that supports preemptive process migration. This paper presents the MOSIX Direct File System Access (DFSA), a provision that can improve the performance of cluster file systems by allowing a migrated process to directly access files in its current location. This capability, when combined with an appropriate file system, could substantially increase the I/O performance and reduce the network congestion by migrating an I/O intensive process to a file server rather than the traditional way of bringing the file's data to the process. DFSA is suitable for clusters that manage a pool of shared disks among multiple machines. With DFSA, it is possible to migrate parallel processes from a client node to file servers for parallel access to different files. Any consistent file system can be adjusted to work with DFSA. To test its performance, we developed the MOSIX File-System (MFS) which allows consistent parallel operations on different files. The paper describes DFSA and presents the performance of MFS with and without DFSA.     

An Equivalent Tree Network Methodology for Efficient Utilization of Front-Ends in Linear Network

In this paper, divisible load scheduling in a linear network of processors is presented. The cases of processing load originating at the boundary and also at the interior of the network are considered. An equivalent tree network for the given linear network is derived. Using this equivalent tree network, we prove all the results obtained in the earlier studies. The equivalent tree network methodology presented in this paper, is more general than the earlier results, because in this approach, we can solve the scheduling problem even in an hetrogeneous linear network. The earlier studies considered only homogeneous linear network.     

An Evaluation of Some Beowulf Clusters

We report the results of an evaluation project on three Beowulf type clusters. The purpose of this study was to assess both the performance of the clusters and the availability and quality of the software for cluster management and management of the available resources. This last goal could hardly be achieved because at the time this project was undertaken much of the management software was either very immature or not yet available. However, it was possible to assess the cluster performance both from the point of view of single program execution as well as with respect to throughput by loading the systems according to a predefined schedule via the available batch systems. To this end a set of application programs, ranging from astronomy to quantum chemistry, together with a synthetic benchmark were employed. From the results we wanted to derive answers about the viability of using cluster systems routinely in a multi-user environment with comparable maintenance cost and effort to that of an integrated parallel machine.     

Low Latency High Bandwidth Message Transfer Mechanisms for a Network Interface Plugged into a Memory Slot

The communication architecture of the DIMMnet-1 network interface based on MEMOnet is described. MEMOnet is a class of a network interface plugged into a memory slot. This paper proposes three message transfer mechanisms named atomic on-the-fly sending (AOTF), block on-the-fly sending (BOTF) and OTF receiving with selective address translation. The DIMMnet-1 prototype will have an ASIC named Martini, two banks of PC133 based SO-DIMM slots and an 8 Gbps full duplex optical link. The software overhead incurred to generate a message is only 1 CPU cycle and the estimated hardware delay is 105 ns using AOTF. The estimated hardware delay for receiving to on chip memory using OTF receiver is 90 ns. The estimated achievable sending bandwidth of DIMMnet-1 using BOTF is 984 MB/s which was observed in our experiments. This bandwidth is 7.4 times higher than the maximum bandwidth of PCI. This high performance is available even when simultaneous sending and receiving are executed on a cheap personal computer with DIMM slots. This paper also discribes the effects of BOTF for a PCI-based NIC.     

网络药理学与药物发现研究进展

将生物学网络与药物作用网络整合,分析药物在网络中与节点或网络模块的关系,由寻找单一靶点转向综合网络分析,就形成了网络药理学.通过系统生物学的研究方法进行网络药理学分析,能够在分子水平上更好的理解细胞以及器官的行为,加速药物靶点的确认以及发现新的生物标志物.这使得我们有可能系统地预测和解释药物的作用,优化药物设计,发现影响药物作用有效性和安全性的因素,从而设计多靶点药物或药物组合.本文综述了网络药理学的新近研究进展,介绍在生物学网络的各个层面上网络药理学的研究和应用,展望网络药理未来的发展方向,对药物发现具有重要意义.     

Configurable Network Processing for Mobile Agents on the Internet

This paper presents a framework for building and deploying protocols for migrating mobile agents over the Internet. The framework enables network protocols for agent migration to be naturally implemented within mobile agents and then dynamically deployed at remote hosts by migrating the agents that perform the protocols. It is built on a hierarchical mobile agent system, called MobileSpaces, and several protocols for migrating agents for managing cluster computing systems have been designed and implemented based on the framework. This paper describes the framework and its prototype implementation, which uses Java as both the implementation language and the protocol development language.     

GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation

Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we developed GCEN, a cross-platform command-line toolkit developed with C++. It is an efficient and easy-to-use solution that will allow everyone to perform gene co-expression network analysis without the requirement of sophisticated programming skills, especially in cases of RNA-Seq research and lncRNAs function annotation. Because of its modular design, GCEN can be easily integrated into other pipelines.     

构建我国航空医疗救护体系需要规划和预研的有关问题

航空医疗救护是航空救援的重要组成,是构建和推进急救医疗体系和急救医疗网络向现代化和立体化发展的重要方式。无论从现实需要、发展需要,还是供给缺失、供需矛盾等哪个方面讲,加强我国航空医疗救护体系建设都势在必行。但开展航空医疗救护体系建设是一个复杂的系统工程,要科学规划、科学论证,对有关问题必须及早规划、及早预研。     

Dynamic Neural Network Models of the Premotoneuronal Circuitry Controlling Wrist Movements in Primates

Dynamic recurrent neural networks were derived to simulate neuronal populations generating bidirectional wrist movements in the monkey. The models incorporate anatomical connections of cortical and rubral neurons, muscle afferents, segmental interneurons and motoneurons; they also incorporate the response profiles of four populations of neurons observed in behaving monkeys. The networks were derived by gradient descent algorithms to generate the eight characteristic patterns of motor unit activations observed during alternating flexion-extension wrist movements. The resulting model generated the appropriate input-output transforms and developed connection strengths resembling those in physiological pathways. We found that this network could be further trained to simulate additional tasks, such as experimentally observed reflex responses to limb perturbations that stretched or shortened the active muscles, and scaling of response amplitudes in proportion to inputs. In the final comprehensive network, motor units are driven by the combined activity of cortical, rubral, spinal and afferent units during step tracking and perturbations.The model displayed many emergent properties corresponding to physiological characteristics. The resulting neural network provides a working model of premotoneuronal circuitry and elucidates the neural mechanisms controlling motoneuron activity. It also predicts several features to be experimentally tested, for example the consequences of eliminating inhibitory connections in cortex and red nucleus. It also reveals that co-contraction can be achieved by simultaneous activation of the flexor and extensor circuits without invoking features specific to co-contraction.     

Deciphering Ascorbic Acid Regulatory Pathways in Ripening Tomato Fruit Using a Weighted Gene Correlation Network Analysis Approach

Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid （AsA）, which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato （Solanum lycopersicum L.） fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty- four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level.     

Growth Medium and Phosphorus Supply Affect Cluster Root Formation and Citrate Exudation by Lupinus albus Grown in a Sand/Solution Split-Root System

We examined cluster root formation and root exudation by white lupin (Lupinus albus L. cv. Kiev Mutant) in response to growth medium and phosphorus supply in a sand/solution split-root system. The split-root system consisted of a nutrient solution compartment and a siliceous sand compartment. Phosphorus was applied at 1 (low-P plants) or 50 (high-P plants) μM as KH₂PO₄ to the solution compartment and at 10, 50 or 250 mg P kg⁻¹ as hydroxyapatite (Ca-P) to the sand compartment. In contrast to the high-P plants, P concentration and P uptake in the low-P plants increased with increasing P supply to the sand compartment. The NaHCO₃-extractable P was lower in the rhizosphere of the low-P plants than the high-P ones. The proton extrusion rate by the solution-grown roots of the low-P plants was higher than that of the high-P plants at the early growth stage. For the low-P plants, the proportion of dry root biomass allocated to cluster roots was higher in the solution compartment than that in the sand compartment. The citrate exudation increased in the sand compartment and decreased in the solution compartment with time, showing a lack of synchronization in citrate exudation by two root halves grown in different media. The cluster root proportion and citrate exudation in both compartments decreased with increasing shoot P concentration. An additional experiment with no P added to either root compartment showed that the proportion of cluster roots was about 9% lower in the sand than solution compartments. The results suggest that cluster root formation and citrate exudation can be significantly affected by the root growth medium in addition to being regulated by shoot P status. More P can be exploited from sparingly available Ca-P by the low-P plants than the high-P ones due to greater citrate exudation under P deficiency.     

An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm

A high efficient Supply Chain (SC) would bring great benefits to an enterprise such as integrated resources, reduced logistics costs, improved logistics efficiency and high quality of overall level of services. So it is important to research various methods, performance indicator systems and technology for evaluating, monitoring, predicting and optimizing the performance of a SC. In this paper, the existing performance indicator systems and methods are discussed and evaluated. Various nature-inspired algorithms are reviewed and their applications for SC Performance Evaluation (PE) are discussed. Then, a model is proposed and developed using 5 Dimensional Balanced Scorecard (5DBSC) and LMBP (Levenberg-Marquardt Back Propagation) neural network for SC PE. A program is written using Matlab tool box to implement the model based on the practical values of the 14 indicators of 5DBSC of a given previous period. This model can be used to evaluate, predict and optimize the performance of a SC. The analysis results of a case study of a company show that the proposed model is valid, reliable and effective. The convergence speed is faster than that in the previous work.     

Population Dynamics Modelling in an Hierarchical Arborescent River Network: An Attempt with Salmo trutta

The balance between births and deaths in an age-structured population is strongly influenced by the spatial distribution of sub-populations. Our aim was to describe the demographic process of a fish population in an hierarchical dendritic river network, by taking into account the possible movements of individuals. We tried also to quantify the effect of river network changes (damming or channelling) on the global fish population dynamics. The Salmo trutta life pattern was taken as an example for.We proposed a model which includes the demographic and the migration processes, considering migration fast compared to demography. The population was divided into three age-classes and subdivided into fifteen spatial patches, thus having 45 state variables. Both processes were described by means of constant transfer coefficients, so we were dealing with a linear system of difference equations. The discrete case of the variable aggregation method allowed the study of the system through the dominant elements of a much simpler linear system with only three global variables: the total number of individuals in each age-class.From biological hypothesis on demographic and migratory parameters, we showed that the global population dynamics of fishes is well characterized in the reference river network, and that dams could have stronger effects on the global dynamics than channelling.     

情绪模仿的神经生理机制：从镜像神经系统到神经网络

情绪模仿是指观察者对表达者传递出的非言语情绪信号进行模仿,进而表现出一致的表情与行为.以往关于情绪模仿的神经机制着重强调镜像神经系统的作用,然而随着研究成果越来越丰富,研究者们发现仅仅是镜像神经系统不足以解释情绪模仿的发生过程.梳理以往实证研究可以发现,情绪模仿是包括镜像神经系统、情绪系统、运动系统以及与社会认知相关脑区在内的脑网络共同作用的结果,该网络同时受到内分泌系统的调节.本文首先基于过往研究对情绪模仿的神经生理基础进行总结,然后介绍新近的神经网络概念模型,试图解释情绪信息从表达者传递至观察者完成模仿的神经路径,为情绪模仿的神经生理机制提供较为完整的框架,并在此基础上指出未来可能的研究方向.     

Network parameters quantify loss of assemblage structure in human‐impacted lake ecosystems

Lake biodiversity is an incomplete indicator of exogenous forcing insofar as it ignores underlying deformations of community structure. Here, we seek a proxy for deformation in a network of diatom assemblages comprising 452 species in 273 lakes across China. We test predictions from network theory that nodes of similar type will tend to self‐organize in an unstressed system to a positively skewed frequency distribution of nodal degree. The empirical data reveal shifts in the frequency distributions of species associations across regions, from positive skew in lakes in west China with a history of low human impacts, to predominantly negative skew amongst lakes in highly disturbed regions in east China. Skew values relate strongly to nutrient loading from agricultural activity and urbanization, as measured by total phosphorus in lake water. Reconstructions through time show that positive skew reduces with temporal intensification of human impacts in the lake and surrounding catchments, and rises as lakes recover from disturbance. Our study illustrates how network parameters can track the loss of aquatic assemblage structure in lakes associated with human pressures.