CABARET scheme in vorticity-velocity variables for the numerical modeling of ideal fluid motion in a two-dimensional domain

A modification of the CABARET scheme is proposed for the numerical solution of equations of ideal fluid motion in vorticity-velocity variables. The dissipative and dispersive properties of the obtained numerical algorithm were investigated for the problem of an isolated vortex. Calculations were performed for decaying homogeneous isotropic turbulence on grids of varying density. In all investigated grids, the spectral density of the kinetic energy was found to obey the ??-3?? law, which conforms to the Kraichnan-Batchelor theory. The structural functions of the obtained vortex flow conform to the law derived using the dimension theory.     

Fabrication of focused two-dimensional grids

A method to fabricating two-dimensional antiscatter grids with septa walls oriented toward the focal point using deep X-ray lithography and copper electroforming is described. These focused grids can be used in mammography to eliminate scattered X-rays, and result in contrast improvement and significantly better image quality in comparison with the conventional one-dimensional antiscatter grids. Freestanding copper antiscatter grids, up to 2 mm thick, 60 mm × 60 mm in size, and focused to one point have been fabricated. This method can be used for fabrication of various other structures with gradually inclined walls.We would like to thank Dr. Francesco De Carlo for the beam line support. We acknowledge Judi Yaeger for experimental assistance and Joseph Arko for technical assistance in this work. The work is supported by NIH SBIR Phase II Grant: R44 CA76752, and by the U.S. Department of Energy, under Contract No. W-31–109-ENG-38.     

Generalization of the dynamic programming scheme

A new generalization of the Bellman optimality principle and the scheme of dynamic programming in terms of the language of choice functions was proposed. Proved were their correctness and applicability to the multistage decision making.     

Generalization of the incenter subdivision scheme

We introduce a new interpolatory subdivision scheme generalizing the incenter subdivision [8]. The proposed scheme is equipped with a shape controlling tension parameter, is Hermitian, and reproduces circles from non-uniform samples. We prove that for any value of the free parameter the limit curve is G¹ continuous. The scheme is shape preserving and avoids undesirable oscillations by producing curves with a finite number of inflection points at the regions where the control polygon suggests a change of convexity. Several examples are presented demonstrating the properties of the scheme.     

On the performance-driven load distribution for heterogeneous computational grids

Load balancing has been a key concern for traditional multiprocessor systems. The emergence of computational grids extends this challenge to deal with more serious problems, such as scalability, heterogeneity of computing resources and considerable transfer delay. In this paper, we present a dynamic and decentralized load balancing algorithm for computationally intensive jobs on a heterogeneous distributed computing platform. The time spent by a job in the system is considered as the main issue that needs to be minimized. Our main contributions are: (1) Our algorithm uses site desirability for processing power and transfer delay to guide load assignment and redistribution, (2) Our transfer and location policies are a combination of two specific strategies that are performance driven to minimize execution cost. These two policies are the Instantaneous Distribution Policy (IDP) and the Load Adjustment Policy (LAP), (3) The communication overhead involved in information collection is reduced using mutual information feedback. The simulation results show that our proposed algorithm outperforms conventional approaches over a wide range of system parameters.     

A computational reinforced learning scheme to blind imagedeconvolution

This paper presents a new approach to adaptive blind image deconvolution based on computational reinforced learning in an attractor-embedded solution space. The new technique develops an evolutionary strategy that generates the improved blur and image populations progressively. A dynamic attractor space is constructed by integrating the knowledge domain of the blur structures into the algorithm. The attractors are predicted using a maximum a posteriori estimator and their relevance is evaluated with respect to the computed blurs. We develop a novel reinforced mutation scheme that combines stochastic search and pattern acquisition throughout the blur identification. It enhances the algorithmic convergence and reduces the computational cost significantly. The new technique is robust in alleviating the constraints and difficulties encountered by most conventional methods. Experimental results show that the new algorithm is effective in restoring the degraded images and identifying the blurs     

Scalability analysis of parallel Particle-In-Cell codes on computational grids

We have performed benchmarks of two three-dimensional parallel Particle-In-Cell (PIC) codes that are similar but have quite different communication patterns on different computational Grids. An electrostatic code with only electrons based on the three-dimensional skeleton PIC code employs the FFT Poisson solver that uses collective communication patterns. Another is the TRISTAN (TRI-dimensional STATNford) code parallelized with MPI, an electromagnetic full particle code, which uses a field solver that only requires point-to-point neighbor communication patterns. We present the mpptest benchmarks on cluster-based computational Grids, where both the basic point-to-point communication patterns and the basic collective communication patterns used in these PIC codes are tested. The results of these benchmarks clearly allow us to quantify and understand the scalability of both communication patterns on the Grids. The present results show that the parallelized TRISTAN code (without all-to-all collective communication) is more scalable than the parallelized skeleton PIC code (with all-to-all collective communication), in cluster-based computational Grid systems where communication performances is poor.     

Time and space adaptation for computational grids with the ATOP-Grid middleware

Adaptive resource allocation is becoming an important feature for local applications and for grid applications that run simultaneously on multiple sites. The applications may face varying resource availability during execution, may need to schedule around obstacles as from reservation, and may have to deal with varying system load under time-shared execution and with a lack of accurate runtime predictability on heterogeneous resources. Thus, middleware support is needed to make the applications adaptable. We present our ATOP-Grid middleware which employs the well-known Zoltan/ParMETIS library for high-quality workload adaptation, enhanced with an over-partitioning extension. ATOP-Grid constitutes a unified approach for application-internal workload self-adaptation under different resource-sharing types, integration with the local batch job schedulers, and largely autonomous decision making.     

A novel approach to resource scheduling for parallel query processing on computational grids

Advances in network technologies and the emergence of Grid computing have both increased the need and provided the infrastructure for computation and data intensive applications to run over collections of heterogeneous and autonomous nodes. In the context of database query processing, existing parallelisation techniques cannot operate well in Grid environments because the way they select machines and allocate tasks compromises partitioned parallelism. The main contribution of this paper is the proposal of a low-complexity, practical resource selection and scheduling algorithm that enables queries to employ partitioned parallelism, in order to achieve better performance in a Grid setting. The evaluation results show that the scheduler proposed outperforms current techniques without sacrificing the efficiency of resource utilisation. Recommended by: Ioannis Vlahavas     

A queuing network model for minimizing the total makespan of computational grids

This paper offers a mathematical solution based on queuing theory and a generalized stochastic Petri net model to minimize the total makespan of the grid computing environments. A grid manager could minimize the total makespan through cautious distribution of subtasks to the grid resources. Subtask arrival rates depend on the arrival rate of the grid tasks submitted to the grid manager, local tasks directly submitted to the grid resources and the processing speed of the resources. Modeling the grid environment using queuing network, the steady state analysis of the network will result in the mean response time of the resources. Therefore, the total makespan could be minimized by minimizing the longest mean response time of the resources. The accuracy of the values obtained for the subtasks arrival rates at each of the grid resources from solving the corresponding queuing network could be further evaluated by steady state analysis of the generalized stochastic Petri net modeling the same grid environment.     

基于网格的消息传递接口的实现:MPICH-G2

MPI(消息传递接口)作为一种著名的底层并行编程模型已被提出来作为网格编程的基础。描述了基于网格的消息传递接口的实现MPICH-G2,它基于MPICH和Gllobus工具包实现,在启动和管理中隐藏了异构性,具有良好的异构通讯性能。用一个例子说明如何在一个由Globus搭建的计算网格环境中通过MPICH-G2来创建和执行MPI计算。     

On the distribution of sequential jobs in random brokering for heterogeneous computational grids

Scheduling stochastic workloads is a difficult task. In order to design efficient scheduling algorithms for such workloads, it is required to have a good in-depth knowledge of basic random scheduling strategies. This paper analyzes the distribution of sequential jobs and the system behavior in heterogeneous computational grid environments where the brokering is done in such a way that each computing element has a probability to be chosen proportional to its number of CPUs and (new from the previous paper) its relative speed. We provide the asymptotic behavior for several metrics (queue-sizes, slowdowns, etc.) or, in some cases, an approximation of this behavior. We study these metrics for a variety of workload configurations (load, distribution, etc.). We compare our probabilistic analysis to simulations in order to validate our results. These results provide a good understanding of the system behavior for each metric proposed. This enables us to design advanced and efficient algorithms for more complex cases.     

An adaptive finite element scheme to solve fluid-structure vibration problems on non-matching grids

This paper deals with the computation of the vibration modes of a system consisting of a linear elastic solid interacting with an acoustic fluid. A finite element method based on meshes for each medium not matching on the fluid-solid interface is analyzed. Optimal order of convergence is proved for the approximation of the eigenfunctions, as well as a double order for the eigenvalues. Numerical tests confirming the theoretical results and showing the advantage of using non-matching grids are reported. Finally, an a posteriori error estimator for this method is introduced and combined with a mesh refinement strategy. The efficiency of this adaptive technique is tested with further numerical experiments. Received: 30 January 2001 / Accepted: 30 May 2001     

Resource allocation on computational grids using a utility model and the knapsack problem

This work introduces a utility model (UM) for resource allocation on computational grids and formulates the allocation problem as a variant of the 0–1 multichoice multidimensional knapsack problem. The notion of task-option utility is introduced, and it is used to effect allocation policies. We present a variety of allocation policies, which are expressed as functions of metrics that are both intrinsic and external to the task and resources. An external user-defined credit-value metric is shown to allow users to intervene in the allocation of urgent or low priority tasks. The strategies are evaluated in simulation against random workloads as well as those drawn from real systems. We measure the sensitivity of the UM-derived schedules to variations in the allocation policies and their corresponding utility functions. The UM allocation strategy is shown to optimally allocate resources congruent with the chosen policies.     

An agent-based approach for dynamic adjustment of scheduled jobs in computational grids

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Grid scheduling is an important infrastructure in the grid computing environment. Most of the existing grids scheduling methods focus on maximizing processor utilization without taking grid load into consideration. This may lead to significant inefficiencies in performance such as large job queues and processing delays. In this paper, we propose a multiagent-based scheduling system for computational grids with a new approach. Agent technology is suitable for a computational grid because of the dynamic, heterogeneous, and autonomous nature of the grid. The main idea of the proposed system is a combination of a static scheduling using a fixed scheduling algorithm and a dynamic adjustment through the autonomous behavior of agents. The superiority of the proposed system, in reducing the load of the grid and minimizing the response time for executing user applications, is demonstrated by simulation experiments.     

Evaluating the reliability of computational grids from the end user’s point of view

Reliability, in terms of Grid component fault tolerance and minimum quality of service, is an important aspect that has to be addressed to foster Grid technology adoption. Software reliability is critically important in today’s integrated and distributed systems, as is often the weak link in system performance. In general, reliability is difficult to measure, and specially in Grid environments, where evaluation methodologies are novel and controversial matters. This paper describes a straightforward procedure to analyze the reliability of computational grids from the viewpoint of an end user. The procedure is illustrated in the evaluation of a research Grid infrastructure based on Globus basic services and the GridWay meta-scheduler. The GridWay support for fault tolerance is also demonstrated in a production-level environment. Results show that GridWay is a reliable workload management tool for dynamic and faulty Grid environments. Transparently to the end user, GridWay is able to detect and recover from any of the Grid element failure, outage and saturation conditions specified by the reliability analysis procedure.     

Generalization of generalized orthogonal polynomial operational matrices for fractional and operational calculus

A method is given for the approximation of generalized orthogonal polynomials (GOP) to solve the problems of fractional and operational calculus. A more rigorous derivation for the generalized orthogonal polynomial operational matrices is proposed. The Riemann-Liouville fractional integral for repeated fractional (and operational) integration is integrated exactly, then expanded in generalized orthogonal polynomials to yield the generalized orthogonal polynomial operational matrices. The generalized orthogonal polynomial operational matrices perform as s^α(α ≥ αε R) in the Laplace domain and as fractional (and operational) integrators in the time domain. Using these results, inversions of the Laplace transforms of irrational and rational transfer functions are solved in a simple way. Very accurate results are obtained.     

A multicore periodical preemption virtual machine scheduling scheme to improve the performance of computational tasks

In virtualized environments, the VMM (virtual machine monitor) scheduler is critical to overall performance, as it allocates the physical resources. However, traditional schedulers have poor I/O performance of mixed workloads. Although recent research significantly improves I/O performance, they degrade the performance of computational tasks by shortening time slices and reducing cache efficiency. In order to eliminate these problems while guaranteeing I/O performance, this paper presents a multicore periodical preemption scheduling scheme with three optimization techniques: (1) periodically coalescing and handling I/O events to reduce the preemption rate and scheduling latency, which guarantees I/O performance; (2) taking advantage of multicore environments and centrally handling I/O events on different cores in a Round-Robin manner to lengthen time slices, which improves the performance of computational tasks; (3) using a dedicated priority for I/O event handling to keep the CPU fairness. We implement a Xen-based prototype and evaluate the performance of I/O workloads and computation-intensive workloads. The experimental results demonstrate that our scheduling scheme efficiently lengthens time slices and improves the performance of computational tasks, achieving the same I/O performance as the existing approaches optimized for I/O.