21st Century Simulation: Exploiting High Performance Computing and Data Analysis

This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success

Beyond swarm intelligence: The Ultraswarm

This paper explores the idea that it may be possible to combine two ideas – UAV flocking, and wireless cluster computing – in a single system, the UltraSwarm. The possible advantages of such a system are considered, and solutions to some of the technical problems are identified. Initial work on constructing such a system based around miniature electric helicopters is described

Reliable scalable symbolic computation: The design of SymGridPar2

Symbolic computation is an important area of both Mathematics and Computer Science, with many large computations that would benefit from parallel execution. Symbolic computations are, however, challenging to parallelise as they have complex data and control structures, and both dynamic and highly irregular parallelism. The SymGridPar framework (SGP) has been developed to address these challenges on small-scale parallel architectures. However the multicore revolution means that the number of cores and the number of failures are growing exponentially, and that the communication topology is becoming increasingly complex. Hence an improved parallel symbolic computation framework is required. This paper presents the design and initial evaluation of SymGridPar2 (SGP2), a successor to SymGridPar that is designed to provide scalability onto 10^5 cores, and hence also provide fault tolerance. We present the SGP2 design goals, principles and architecture. We describe how scalability is achieved using layering and by allowing the programmer to control task placement. We outline how fault tolerance is provided by supervising remote computations, and outline higher-level fault tolerance abstractions. We describe the SGP2 implementation status and development plans. We report the scalability and efficiency, including weak scaling to about 32,000 cores, and investigate the overheads of tolerating faults for simple symbolic computations

Reliable scalable symbolic computation: The design of SymGridPar2

Symbolic computation is an important area of both Mathematics and Computer Science, with many large computations that would benefit from parallel execution. Symbolic computations are, however, challenging to parallelise as they have complex data and control structures, and both dynamic and highly irregular parallelism. The SymGridPar framework (SGP) has been developed to address these challenges on small-scale parallel architectures. However the multicore revolution means that the number of cores and the number of failures are growing exponentially, and that the communication topology is becoming increasingly complex. Hence an improved parallel symbolic computation framework is required. This paper presents the design and initial evaluation of SymGridPar2 (SGP2), a successor to SymGridPar that is designed to provide scalability onto 10^5 cores, and hence also provide fault tolerance. We present the SGP2 design goals, principles and architecture. We describe how scalability is achieved using layering and by allowing the programmer to control task placement. We outline how fault tolerance is provided by supervising remote computations, and outline higher-level fault tolerance abstractions. We describe the SGP2 implementation status and development plans. We report the scalability and efficiency, including weak scaling to about 32,000 cores, and investigate the overheads of tolerating faults for simple symbolic computations

MATLAB*P 2.0: A unified parallel MATLAB

MATLAB is one of the most widely used mathematical computing environments in technical computing. It is an interactive environment that provides high performance computational routines and an easy-to-use, C-like scripting language. Mathworks, the company that develops MATLAB, currently does not provide a version of MATLAB that can utilize parallel computing. This has led to academic and commercial efforts outside Mathworks to build a parallel MATLAB, using a variety of approaches. In a survey, 26 parallel MATLAB projects utilizing four different approaches have been identified. MATLAB*P is one of the 26 projects. It makes use of the backend support approach. This approach provides parallelism to MATLAB programs by relaying MATLAB commands to a parallel backend. The main difference between MATLAB*P and other projects that make use of the same approach is in its focus. MATLAB*P aims to provide a user-friendly supercomputing environment in which parallelism is achieved transparently through the use of objected oriented programming features in MATLAB. One advantage of this approach is that existing scripts can be run in parallel with no or minimal modifications. This paper describes MATLAB*P 2.0, which is a complete rewrite of MATLAB*P. This new version brings together the backend support approach with embarrassingly parallel and MPI approaches to provide the first complete parallel MATLAB framework.Singapore-MIT Alliance (SMA

Mosix the Cluster Operating System Having Advancements & Many Features

Mosix is a running of modifications to the Linux kernel. MOSIX Design Objectives turn a network of Linux computers into a High Performance Cluster computer. The Founder o f MOSIX is the Amnon Barak. MOSIX is a cluster operating system that provides users and applications with the impression of running on a single computer with multiple processors which is called as single - system image and Hide cluster complexity to users. T his paper describes the enhancement of MOSIX to openMosix and its cloud environment. There are many advance features of MOSIX by which large number of appli cation work fastly and properly. Balancing Load is the most effective feature we mentioned it in thi s paper

Leveraging HTC for UK eScience with very large Condor pools: demand for transforming untapped power into results

We provide an insight into the demand from the UK eScience community for very large HighThroughput Computing resources and provide an example of such a resource in current productionuse: the 930-node eMinerals Condor pool at UCL. We demonstrate the significant benefits thisresource has provided to UK eScientists via quickly and easily realising results throughout a rangeof problem areas. We demonstrate the value added by the pool to UCL I.S infrastructure andprovide a case for the expansion of very large Condor resources within the UK eScience Gridinfrastructure. We provide examples of the technical and administrative difficulties faced whenscaling up to institutional Condor pools, and propose the introduction of a UK Condor/HTCworking group to co-ordinate the mid to long term UK eScience Condor development, deploymentand support requirements, starting with the inaugural UK Condor Week in October 2004