Natalia Currle-Linde

GriCoL: A Language for Scientific Grids

The development of the Grid has opened new possibilities for scientists and engineers to execute large-scale modeling experiments. This has stimulated the generation and development of tools for the creation and management of complex computing experiments in the Grid. Among these, tools for the automation of the programming of experiments play a significant role. In this paper we present GriCoL, which we propose as a simple and efficient language for the description of complex Grid experiments. We also describe the environment within which GriCoL works, namely the Science Experimental Grid Laboratory (SEGL) system for designing and executing complex experiments. As an illustration, the paper includes the description of a biochemical experiment using Molecular Dynamics simulations.

Towards Simulation Workflows with BPEL: Deriving Missing Features from GriCoL

In this paper, we investigate the suitability of the generalpurpose workflow language BPEL to create executable simulation workflows. We therefore compare BPEL to GriCoL, a graphical language with proven applicability for simulation workflows in Grid environments. We discover a number of incomparable concepts in the two languages. On the one hand, BPEL’s unique features in comparison to GriCoL reveal the rationale behind the approach of using BPEL as basis for a simulation workflow language. On the other hand, based on the features of GriCoL, we are able to discuss how to extend BPEL in order to increase its expressiveness for simulation workflows.

Redesigning the Segl Problem Solving Environment: A Case Study of Using Mediator Components

The Science Experimental Grid Laboratory (SEGL) problem solving environment allows users to describe and execute complex parameter study workflows in Grid environments. Its current implementation provides much high-level functionality for executing complex parameter-study workflows. Alternatively, using a toolkit of mediator components that integrate system-component capabilities into application code would allow to build a system like SEGL from existing, more generally applicable components, simplifying its implementation and maintenance. In this paper, we present the given design of the SEGL PSE, analyze the provided functionality, and identify a set of mediator components that can generalize the functionality required by this challenging application category.

GriCoL: A Language for Grid Computing

The development of the grid has opened new possibilities for scientists and engineers to execute large-scale modeling experiments. This has stimulated the generation and development of tools for the creation and management of complex computing experiments in the grid. Among these, tools for the automation of the programming of experiments play a significant role. In this paper we present GriCoL, which we propose as a simple and efficient language for the description of complex grid experiments

SEGL: A Problem Solving Environment for the Design and Execution of Complex Scientific Grid Applications

The design and execution of complex scientific applications in the Grid is a difficult and work-intensive process which can be simplified and optimized by the use of an appropriate tool for the creation and management of the experiment. We propose SEGL (Science Experimental Grid Laboratory) as a problem solving environment for the optimized design and execution of complex scientific Grid applications. SEGL utilizes GriCoL (Grid Concurrent Language), a simple and efficient language for the description of complex Grid experiments.

Analysis of Component Model Extensions to Support the GriCoL Language

Nowadays, programming grid applications is still a major challenge. Several systems, tools and environments have appeared to allow end-users to describe applications without dealing with the complexity of the grid infrastructure. An application description in such environments is done through high level languages such as the Grid Concurrent Language (Gricol). Independently of the application domain, this language enables the description of highly complex scientific experiments. While such a high level language is offered to end-users, the question of how to implement it is raised. The contribution of this paper is to analyze the support of a Gricol application within component models, in particular the support of its temporal composition represented by a control flow construction.

GRID Superscalar and GriCoL: Integrating Different Programming Approaches

One way to ease the development of Grid applications is to specify and design an Integrated Toolkit which will enable the development of Grid-unaware applications i.e. applications where the Grid is transparent to them but that are able to exploit its resources. Achieving this vision of an Integrated Toolkit requires the investigation and definition of integration between different systems. This paper studies the integration possibilities of GriCoL, a language for the description of complex Grid experiments, and GRID superscalar, a run-time environment which automatically converts sequential program code and deploys it for execution on a Grid. GriCoL operates on a multi-layer paradigm, using both a control flow layer and a data flow layer. We propose integration with GRID superscalar at each of these layers, concluded that integration at the control flow level is difficult to achieve but at the data flow level is possible.

Efficient Calculation of Multi-dimensional Potential Energy Surfaces of Molecules and Molecular Clusters

Highly accurate multi-dimensional potential energy surfaces have been computed in a fully automated fashion using newly implemented grid computing capabilities, which allow for the use of an unlimited number of cores. This new feature, which has been interfaced to our potential energy surface generator, allows for the accurate investigation of molecular systems, which are significantly larger than reported in the recent literature. Multi-dimensional potential energy surfaces at the coupled-cluster level were generated for systems of up to 16 atoms, which were used to compute accurate anharmonic vibrational spectra, which can directly be compared with experimental data.

A tool for complex parameter studies in grid environments: SGM-Lab

This paper presents the design and implementation of the Science Grid Modeling Laboratory (SGM-Lab), an automated parametric modeling system for performing complex dynamically-controlled parameter studies. Nowadays, simulation programs are used not only in research but also during the development of products, often to optimize their quality. Typically, this involves repeated execution of the simulation codes, whereby for each run some of the input data is varied. As a result, many different jobs have to be launched and a huge amount of output data has to be administered. A grid environment can provide, and enable the exploitation of the necessary resources for this computation. However, in order to be able to use a grid environment effectively, tool support is required to automatically generate the parameter sets, issue jobs, control the successful operation and termination of jobs, and collect results. Support is also needed to generate new parameter sets based on previous results in order to obtain a functional optimum, after which the parameter study should terminate. The SGM-Lab software described in this paper offers a unified framework for such large-scale optimization problems.

The Role of Supercomputing in Industrial Combustion Modeling

Currently, numerical simulation using automated parameter studies is already a key tool in discovering functional optima in complex systems such as biochemical drug design and car crash analysis. In the future, such studies of complex systems will be extremely important for the purpose of steering simulations. One such example is the optimum design and steering of computation equipment for power plants. The performance of today’s high performance computers enables simulation studies with results that are comparable to those obtained from physical experimentation. Recently, Grid technology has supported this development by providing uniform and secure access to computing resources over wide area networks (WANs), making it possible for industries to investigate large numbers of parameter sets using sophisticated optimization simulations. However, the large scale of such studies requires organized support for the submission, monitoring, and termination of jobs, as well as mechanisms for the collection of results, and the dynamic generation of new parameter sets in order to intelligently approach an optimum. In this paper, we describe a solution to these problems which we call Science Experimental Grid Laboratory (SEGL). The system defines complex workflows which can be executed in the Grid environment, and supports the dynamic generation of parameter sets.