Leveraging Interactivity and MPI for Environmental Applications

This paper describes two different approaches to exploiting interactivity and MPI support available in the Interactive European Grid project.The first application is an air pollution simulation using Lagrangian trajectory model to simulate the spread of pollutant particles released into the atmosphere. The performance of the sequential implementation of the application was not satisfactory, therefore a parallelization was planned. The MPI programming model was used because of some previous experience with it and its support in the grid infrastructure to be used. Then the interactivity enabling the user to receive visualizations of simulation steps and to exercise control over the application running in the grid was added. The user interface for interacting with the application was implemented as a plug-in into the Migrating Desktop user interface client platform. The other application is an interactive workflow management system, which is a modification of a previously developed system for management of applications composed of web and grid services. It allows users to manage more complex jobs, composed of several program executions, in an interactive and comfortable manner. The system uses the interactive channel of the project to forward commands from a GUI to the on-site workflow manager, and to control the job during execution. This tool is able to visualize the inner workflow of the application. User has complete in-execution control over the job, can see its partial results, and can even alter it while it is running. This allows not only to accommodate the job workflow to the data it produces, extend or shorten it, but also to interactively debug and tune the job

LOGOS: Enabling Local Resource Managers for the Efficient Support of Data-Intensive Workflows within Grid Sites

In this study we discuss how to enable grid sites for the support of data-intensive workflows. Usually, within grid sites, tasks and resources are administrated by local resource managers (LRMs). Many of LRMs have been designed for managing compute-intensive applications. Therefore, data-intensive workflow applications might not perform well on such environments due to the number and size of data transfers between tasks. To improve the performance of such kind of applications it is necessary to redefine the scheduling policies integrated on LRMs. This paper proposes a novel scheme for efficiently supporting data-intensive workflows in LRMs within grid sites. Such scheme is partially implemented in our grid middleware LOGOS and used to improve the performance of a well known LRM: HTCondor. The core of LOGOS is a novel communication-aware scheduling algorithm (PPSA) capable of finding near-optimal solutions. Experiments conducted in this study showed that our approach leads to performance improvements up to 52 % in the management of data-intensive workflow applications