8079300
Solving irregularly structured problems based on distributed object model
This paper presents a distributed object model called MOIDE (multi-threading object-oriented infrastructure on distributed environment) for solving irregularly structured problems. The model creates an adaptive computing infrastructure for developing and executing irregular applications on distributed systems. The infrastructure allows dynamic reconfiguration to match the evolution of irregular computation and available system resources. A unified communication mechanism is built to integrate different communication paths on heterogeneous systems to support efficient communication. Autonomous load scheduling approach is proposed for dynamic load balancing. A runtime system is developed to implement MOIDE-based computing. Applications including N-body problem, ray tracing, and conjugate gradient are developed to demonstrate the advantages of the model.
12751111
A taxonomy of application scheduling tools for high performance cluster computing
Application scheduling plays an important role in high-performance cluster computing. Application scheduling can be classified as job scheduling and task scheduling. This paper presents a survey on the software tools for the graph-based scheduling on cluster systems with the focus on task scheduling. The tasks of a parallel or distributed application can be properly scheduled onto multi-processors in order to optimize the performance of the program (e.g., execution time or resource utilization). In general, scheduling algorithms are designed based on the notion of task graph that represents the relationship of parallel tasks. The scheduling algorithms map the nodes of a graph to the processors in order to minimize overall execution time. Although many scheduling algorithms have been proposed in the literature, surprisingly not many practical tools can be found in practical use. After discussing the fundamental scheduling techniques, we propose a framework and taxonomy for the scheduling tools on clusters. Using this framework, the features of existing scheduling tools are analyzed and compared. We also discuss the important issues in improving the usability of the scheduling tools.
14338621
A distributed object model for solving irregularly structured problems on cluster
This paper presents a distributed object model MOIDE (Multithreaded Object-oriented Infrastructure on Distributed Environment) for solving irregularly structured problems on cluster. The primary appeal of MOIDE is its flexible system structure that is adaptive to heterogeneous architecture of a cluster. MOIDE integrates the object-oriented and multithreaded methodologies to set up a unified computing environment. Both the shared-data access and remote messaging are incorporated in a two-layer communication mechanism for efficient inter-object communication with the common communication interface. MOIDE supports dynamic load balancing by its autonomous load scheduling technique. A runtime support system implements the MOIDE model as a platform-independent infrastructure for developing and executing irregularly structured applications. N-body, ray tracing, and conjugate gradient applications are implemented to illustrate the advantages of MOIDE model.
15800513
ClusterProbe: an open, flexible and scalable cluster monitoring tool
In this paper, we describe the ClusterProbe, an open, flexible, scalable, and Java-based cluster monitoring tool. The tool provides an open environment by developing a multiple-protocol communication interface that can be connected to various types of external accesses from the clients. ClusterProbe is flexible that the monitoring tool can be easily extended to adapt to the resource changes by using the Java-RMI mechanism. In addition, the design of ClusterProbe allows it to scale up to commodious capacity with its cascading hierarchical architecture. Several useful services are implemented based on ClusterProbe, including the visualization of cluster resource information in various form and cluster fault management. The tool has been used to assist the execution of a cluster-based search engine and a distributed N-body application. All experiments demonstrate high efficiency.
14951106
A grid-based system for microbial genome comparison and analysis
Genome comparison and analysis can reveal the structures and junctions of genome sequences of different species. As more genomes are sequenced, genomic data sources are rapidly increasing such that their analysis is beyond the processing capabilities of most research institutes. The grid is a powerful solution to support large-scale genomic data processing and genome analysis. This paper presents the Microbase project that is developing a grid-based system for genome comparison and analysis, and discusses the first implementation of the system (called MicrobaseLite). MicrobaseLite uses a scalable computing environment to support computationally intensive microbial genome comparison and analysis, employing state-of-the-art technologies of Web services, notification, comparative genomics and parallel computing. Microbase will support not only system-defined genome comparison and analysis but also user-defined, remotely conceived genome analysis.
14112050
Distributed particle simulation method on adaptive collaborative system
Abstract This paper presents a distributed N-body method based on an adaptive collaborative system model. The collaborative system is formed by the distributed objects on a distributed system. The system can be reconfigured during the computation to fully utilize the computing power of the networked hosts. The method is implemented in Java and RMI to support distributed computing in heterogeneous environment. A distributed tree structure is designed for communication-efficient computation of N-body method. The performance test shows satisfactory speedup and portability of the method on both homogeneous and heterogeneous clusters. The collaborative system model can be used in various applications and it is expandable to wide-area environment.
17387040
Automated judgment of document qualities
The authors report on a series of experiments to automate the assessment of document qualities such as depth and objectivity. The primary purpose is to develop a quality-sensitive functionality, orthogonal to relevance, to select documents for an interactive question-answering system. The study consisted of two stages. In the classifier construction stage, nine document qualities deemed important by information professionals were identified and classifiers were developed to predict their values. In the confirmative evaluation stage, the performance of the developed methods was checked using a different document collection. The quality prediction methods worked well in the second stage. The results strongly suggest that the best way to predict document qualities automatically is to construct classifiers on a person-by-person basis.
61852216
Cross-Evaluation: A new model for information system evaluation
In this article, we introduce a new information system evaluation method and report on its application to a collaborative information seeking system, AntWorld. The key innovation of the new method is to use precisely the same group of users who work with the system as judges, a system we call Cross-Evaluation. In the new method, we also propose to assess the system at the level of task completion. The obvious potential limitation of this method is that individuals may be inclined to think more highly of the materials that they themselves have found and are almost certain to think more highly of their own work product than they do of the products built by others. The keys to neutralizing this problem are careful design and a corresponding analytical model based on analysis of variance. We model the several measures of task completion with a linear model of five effects, describing the users who interact with the system, the system used to finish the task, the task itself, the behavior of individuals as judges, and the self-judgment bias. Our analytical method successfully isolates the effect of each variable. This approach provides a successful model to make concrete the “three-realities” paradigm, which calls for “real tasks,” “real users,” and “real systems.” © 2006 Wiley Periodicals, Inc.
28447870
On quantization error of self-organizing map network
Abstract In this paper, we analyze how neighborhood size and number of weights in the self-organizing map (SOM) effect quantization error. A sequence of i.i.d. one-dimensional random variable with uniform distribution is considered as input of the SOM. First obtained is the linear equation that an equilibrium state of the SOM satisfies with any neighborhood size and number of weights. Then it is shown that the SOM converges to the unique minimum point of quantization error if and only if the neighborhood size is one, the smallest. If the neighborhood size increases with the increasing number of weights at the same ratio, the asymptotic quantization error does not converge to zero and the asymptotic distribution of weights differs from the distribution of input samples. This suggests that in order to achieve a small quantization error and good approximation of input distribution, a small neighborhood size must be used. Weight distributions in numerical evaluation confirm the result.
3124333
Stochastic iterative algorithms for signal set design for Gaussian channels and optimality of the L2 signal set
We propose a stochastic iteration approach to signal set design. Four practical stochastic iterative algorithms are proposed with respect to equal and average energy constraints and sequential and batch modes. By simulation, a new optimal signal set, the L2 signal set (consisting of a regular simplex set of three signals and some zero signals), is found under the strong simplex conjecture (SSC) condition (equal a priori probability and average energy constraint) at low signal-to-noise ratios (SNR). The optimality of the L1 signal set, the confirmation of the weak simplex conjecture, and two of Dunbridge's (1967) theorems are some of the results obtained by simulations. The influence of SNR and a priori probabilities on the signal sets is also investigated via simulation. As an application to practical communication system design, the signal sets of eight two-dimensional (2-D) signals are studied by simulation under the SSC condition. Two signal sets better than 8-PSK are found. Optimal properties of the L2 signal set are analyzed under the SSC condition at low SNRs. The L2 signal set is proved to be uniquely optimal in 2-D space. The class of signal sets S(M, K) (consisting of a regular simplex set of K signals and M-K zero signals) is analyzed. It is shown that any of the signal sets S(M, K) for 3/spl les/K/spl les/M-1 disproves the strong simplex conjecture for M/spl ges/4, and if M/spl ges/7, S(M, 2) (the L1 signal set) also disproves the strong simplex conjecture. It is proved that the L2 signal set is the unique optimal signal set in the class of signal sets S(M, K) for all M/spl ges/4. Several results obtained by Steiner (see ibid., vol.40, no.5, p.721-31, 1994) for all M/spl ges/7 are extended to all M/spl ges/4. Finally, we show that for M/spl ges/7, there exists an integer K'
7298536
A multistage fast motion estimation scheme for video compression
This paper presents a novel multistage motion estimation (ME) scheme called content adaptive search technique (CAST). The proposed scheme consists of four stages: motion vector field (MVF) prediction, block-based segmentation, motion parameter extraction, and adaptive search strategy. Through pre-processing the MVF of the previous reference frame in the first three stages, CAST extracts the motion parameters for each region. The 4th stage is a combination of various techniques including MV prediction, search area decision and an adaptive fast search algorithm that is adjusted by a mathematical model for the block distortion surface (BDS). Experiment shows that the proposed scheme improves the visual quality, while yielding a faster speed, comparing with the other predictive ME algorithms.