Computational steering and the SCIRun integrated problem solving environment

Journal ArticleSCIRun is a problem solving environment that allows the interactive construction, debugging, and steering of large-scale scientific computations. We review related systems and introduce a taxonomy that explores different computational steering solutions. Considering these approaches, we discuss why a tightly integrated problem solving environment, such as SCIRun, simplifies the design and debugging phases of computational science applications and how such an environment aids in the scientific discovery process

Development of an Intelligent Monitoring and Control System for a Heterogeneous Numerical Propulsion System Simulation

The NASA Numerical Propulsion System Simulation (NPSS) project is exploring the use of computer simulation to facilitate the design of new jet engines. Several key issues raised in this research are being examined in an NPSS-related research project: zooming, monitoring and control, and support for heterogeneity. The design of a simulation executive that addresses each of these issues is described. In this work, the strategy of zooming, which allows codes that model at different levels of fidelity to be integrated within a single simulation, is applied to the fan component of a turbofan propulsion system. A prototype monitoring and control system has been designed for this simulation to support experimentation with expert system techniques for active control of the simulation. An interconnection system provides a transparent means of connecting the heterogeneous systems that comprise the prototype

Process tracking for dynamic tuning applications on the grid

Los recursos computacionales requeridos por la comunidad científica para solucionar problemas son mayores que los ofrecidos por la infraestructura actual. La necesidad de mayores prestaciones se debe al constante progreso de la investigación, nuevos problemas o aumento del detalle en los problemas corrientes. Usuarios crean nuevos sistemas distribuidos en larga escala como sistemas Grid para lograr prestaciones deseadas. Sistemas Grid son generalmente construidos sobre los recursos computacionales disponibles como clusters, maquinas paralelas o dispositivos de almacenamiento distribuidos en diferentes organizaciones e interconectado por una red. Sintonizar aplicaciones en un sistema Grid no es fácil debido a las características de distribución de procesos en múltiples clusters controlados por diferentes sistemas de colas y heterogeneidad de la red de comunicaciones. Nosotros tenemos un entorno de monitorización, análisis y sintonización (MATE) que permite la sintonización dinámica de aplicaciones en entornos cluster. Debido a las muchas capas de software presente en sistemas Grid, dos ejecuciones de una misma aplicación pueden usar recursos distintos. Para sintonizar los procesos de la aplicación, nuestra herramienta debe localizar y seguir la ejecución de los procesos en el sistema. Nosotros llamamos eso como problema de localización de procesos. Este artículo presenta la integración de MATE con Gris y dos aproximaciones implementadas para solucionar el problema de localización de procesos dentro de sistemas Grid.The computational resources need by the scientific community to solve problems is beyond the current available infrastructure. Performance requirements are needed due constant research progress, new problems studies or detail increase of the current ones. Users create new wide distributed systems such as computational Grids to achieve desired performance goals. Grid systems are generally built on top of available computational resources as cluster, parallel machines or storage devices distributed within different organizations and those resources are interconnected by a network. Tune applications on Grid environment is a hard task due system characteristics like multi-cluster job distribution among different local schedulers and dynamic network bandwidth behavior. We had a Monitoring, Analysis and Tuning Environment (MATE) that allows dynamic performance tuning applications within a cluster. Due to the many software layers present on the grid, similar job submission may execute on different places. To tune application jobs, our tool needs to locate and follow the jobs execution within the system. We call this a process tracking problem. This paper presents MATE integration to the Grid and the two process tracking approaches implemented in order to solve the process tracking problem within Grid systemsVII Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Process tracking for dynamic tuning applications on the grid

Los recursos computacionales requeridos por la comunidad científica para solucionar problemas son mayores que los ofrecidos por la infraestructura actual. La necesidad de mayores prestaciones se debe al constante progreso de la investigación, nuevos problemas o aumento del detalle en los problemas corrientes. Usuarios crean nuevos sistemas distribuidos en larga escala como sistemas Grid para lograr prestaciones deseadas. Sistemas Grid son generalmente construidos sobre los recursos computacionales disponibles como clusters, maquinas paralelas o dispositivos de almacenamiento distribuidos en diferentes organizaciones e interconectado por una red. Sintonizar aplicaciones en un sistema Grid no es fácil debido a las características de distribución de procesos en múltiples clusters controlados por diferentes sistemas de colas y heterogeneidad de la red de comunicaciones. Nosotros tenemos un entorno de monitorización, análisis y sintonización (MATE) que permite la sintonización dinámica de aplicaciones en entornos cluster. Debido a las muchas capas de software presente en sistemas Grid, dos ejecuciones de una misma aplicación pueden usar recursos distintos. Para sintonizar los procesos de la aplicación, nuestra herramienta debe localizar y seguir la ejecución de los procesos en el sistema. Nosotros llamamos eso como problema de localización de procesos. Este artículo presenta la integración de MATE con Gris y dos aproximaciones implementadas para solucionar el problema de localización de procesos dentro de sistemas Grid.The computational resources need by the scientific community to solve problems is beyond the current available infrastructure. Performance requirements are needed due constant research progress, new problems studies or detail increase of the current ones. Users create new wide distributed systems such as computational Grids to achieve desired performance goals. Grid systems are generally built on top of available computational resources as cluster, parallel machines or storage devices distributed within different organizations and those resources are interconnected by a network. Tune applications on Grid environment is a hard task due system characteristics like multi-cluster job distribution among different local schedulers and dynamic network bandwidth behavior. We had a Monitoring, Analysis and Tuning Environment (MATE) that allows dynamic performance tuning applications within a cluster. Due to the many software layers present on the grid, similar job submission may execute on different places. To tune application jobs, our tool needs to locate and follow the jobs execution within the system. We call this a process tracking problem. This paper presents MATE integration to the Grid and the two process tracking approaches implemented in order to solve the process tracking problem within Grid systemsVII Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Fast Simulation of Vehicles with Non-deformable Tracks

This paper presents a novel technique that allows for both computationally fast and sufficiently plausible simulation of vehicles with non-deformable tracks. The method is based on an effect we have called Contact Surface Motion. A comparison with several other methods for simulation of tracked vehicle dynamics is presented with the aim to evaluate methods that are available off-the-shelf or with minimum effort in general-purpose robotics simulators. The proposed method is implemented as a plugin for the open-source physics-based simulator Gazebo using the Open Dynamics Engine.Comment: Submitted to IROS 201

Resource Management Services for a Grid Analysis Environment

Selecting optimal resources for submitting jobs on a computational Grid or accessing data from a data grid is one of the most important tasks of any Grid middleware. Most modern Grid software today satisfies this responsibility and gives a best-effort performance to solve this problem. Almost all decisions regarding scheduling and data access are made by the software automatically, giving users little or no control over the entire process. To solve this problem, a more interactive set of services and middleware is desired that provides users more information about Grid weather, and gives them more control over the decision making process. This paper presents a set of services that have been developed to provide more interactive resource management capabilities within the Grid Analysis Environment (GAE) being developed collaboratively by Caltech, NUST and several other institutes. These include a steering service, a job monitoring service and an estimator service that have been designed and written using a common Grid-enabled Web Services framework named Clarens. The paper also presents a performance analysis of the developed services to show that they have indeed resulted in a more interactive and powerful system for user-centric Grid-enabled physics analysis.Comment: 8 pages, 7 figures. Workshop on Web and Grid Services for Scientific Data Analysis at the Int Conf on Parallel Processing (ICPP05). Norway June 200

A multiarchitecture parallel-processing development environment

A description is given of the hardware and software of a multiprocessor test bed - the second generation Hypercluster system. The Hypercluster architecture consists of a standard hypercube distributed-memory topology, with multiprocessor shared-memory nodes. By using standard, off-the-shelf hardware, the system can be upgraded to use rapidly improving computer technology. The Hypercluster's multiarchitecture nature makes it suitable for researching parallel algorithms in computational field simulation applications (e.g., computational fluid dynamics). The dedicated test-bed environment of the Hypercluster and its custom-built software allows experiments with various parallel-processing concepts such as message passing algorithms, debugging tools, and computational 'steering'. Such research would be difficult, if not impossible, to achieve on shared, commercial systems