1335499
Dynamic configuration management in a graph-oriented Distributed Programming Environment
Dynamic configuration is a desirable property of a distributed system where dynamic modification and extension to the system and the applications are required. It allows the system configuration to be specified and changed while the system is executing. This paper describes a software platform that facilitates a novel approach to the dynamically configurable programming of parallel and distributed applications and systems. This platform is based on a graph-oriented model and it provides support for constructing reconfigurable distributed programs. We describe the design and implementation of a dynamic configuration manager for the graph-oriented distributed programming environment. The requirements and services for dynamic reconfiguration are identified. The architectural design of a dynamic configuration manager is presented, and a parallel virtual machine-based prototypical implementation of the manager, on a local area network of workstations, is described.
11115305
Graph Scaling: A Technique for Automating Program Construction and Deployment in ClusterGOP
Program development and resource management are critical issues in large-scaled parallel applications and they raise diffculties for the programmers. Automation tools can benefit the programmer by reducing the time and work required for programming, deploying, and managing parallel applications. In our previous work, we have developed a visual tool, VisualGOP, to help visual construction and automatic mapping of parallel programs to execute on the ClusterGOP platform, which provides a graph-oriented model and the environment for running the parallel applications on clusters. In VisualGOP, the programmer needs to manually build the task interaction graph. This may lead to scalability problem for large applications. In this paper, we propose a graph scaling approach that helps the programmer to develop and deploy a large-scale parallel application minimizing the effort of graph construction, task binding and program deployment. The graph scaling algorithms expand or reduce a task graph to match the specified scale of the program and the hardware architecture, e.g., the problem size, the number of processors and interconnection topology, so as to produce an automatic mapping. An example is used to illustrate the proposed approach and how programmer benefits in the automation tools.
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.
12578633
High-level abstractions for message-passing parallel programming
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.
47269670
Motion estimation for content adaptive video compression
A multistage motion estimation scheme is proposed. The scheme extracts video characteristics by first performing an online video analysis separately for foreground and background regions. Motion parameters are extracted and passed to the next stage. The next stage includes a mathematical model for the block distortion surface (BDS) that enables the algorithm to accordingly adjust its search technique. The search is performed on a precise search area adaptive to the statistical property of the motion vector prediction error. Due to its self-tuning property, not only does the proposed scheme adapt to scenes by yielding better visual quality but it also yields a lower computational complexity, compared with the other predictive motion estimation algorithms on standard benchmark sequences
12119839
Performance analysis of maximum intensity projection algorithm for display of MRA images
The maximum intensity projection (MIP) is a popularly used algorithm for display of MRA images, but its performance has not been rigorously analyzed before. In this paper, four measures are proposed for the performance of the MIP algorithm and the quality of images projected from three-dimensional (3-D) data, which are vessel voxel projection probability, vessel detection probability, false vessel probability, and vessel-tissue contrast-to-noise ratio (CNR). As side products, vessel-missing probability, vessel receiver operating characteristics (ROC's), and mean number of false vessels are also studied. Based on the assumptions that the intensities of vessel, tissue, and noise along a projection path are independent Gaussian, these measures are derived and obtained all in closed forms. All the measures are functions of explicit parameters: vessel-to-tissue noise ratio (VTNR) and CNR of 3 D data prior to the MIP, vessel diameter, and projection length. It is shown that the MIP algorithm increases the CNR of large vessels whose CNR prior to the MIP is high and whose diameters are large. The increase in CNR increases with projection path length. On the other hand, all the proposed measures indicate that the small vessels that have low CNR prior to the MIP and small diameters suffer from the MIP. The performance gets worse as projection path length increases. All measures demonstrate a better performance when the vessel diameter is larger. Other properties and possible applications of the derived measures are also discussed.
1430542
Reconfigurable resource scheduling
We consider a class of scheduling problems that we refer to as reconfigurable resource scheduling. This class of problems is motivated by emerging applications that involve dynamically allocating a large number of shared resources to a variety of services. We design efficient online algorithms for certain problems in this class. Our goal is to obtain constant competitive online algorithms where the online algorithm is given a constant factor advantage in terms of the number of resources. The main problem considered in this paper is as follows. The input is a sequence of requests, each of which is a set of unit jobs. Each job has a category, and needs to be processed within a fixed delay bound from its arrival, or else it is dropped and we incur a category-specific drop cost. A job of a given category can only be executed on a resource configured for that category. A resource can be reconfigured at any time at a fixed reconfiguration cost. Our main result is a constant competitive online algorithm for this problem, which is obtained by the following layered approach. First, we reduce our main problem to the special case in which all jobs arrive at integral multiples of the delay bound. Second, we reduce the latter problem to the special case of unit delay. Third, we reduce the unit-delay problem to a caching problem that we refer to as file caching with remote reads. Our solution to this caching problem generalizes certain existing work in the area of file caching.
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.