We present a synopsis of the Grid Application Toolkit, under development in the EU GridLab project, along with some of the new application scenarios which it will enable.

The Testbed and Applications working group of the European Grid Forum (EGrid) is actively building and experimenting with a grid infrastructure connecting several research-based supercomputing sites located in Europe. The paper reports on our first feasibility study: running a self-migrating version of the Cactus simulation code across the European grid testbed, including "live" remote data visualization and steering from different demonstration booths at Supercomputing 2000, in Dallas, TX. We report on the problems that had to be resolved for this endeavour and identify open research challenges for building production-grade grid environments.

We discuss a set of novel techniques we are developing, which build on standard tools, to make distributed computing for large-scale simulations across multiple machines (even scattered across different continents) a reality. With these techniques we demonstrate that we are able to scale a tightly coupled scientific application in metacomputing environments. Such research and development in metacomputing will lead the way to routine, straightforward and efficient use of distributed computing resources anywhere around the world. This work applies not only to the large-scale simulations in astrophysics which provide the motivation for this work, but also opens the way for new, innovative application scenarios.

Improvements in the performance of processors and networks make it both feasible and interesting to treat collections of workstations, servers, clusters, and supercomputers as integrated computational resources, or Grids. However, the highly heterogeneous and dynamic nature of such Grids can make application development difficult. Here we describe an architecture and prototype implementation for a Grid-enabled computational framework based on Cactus, the MPICH-G2 Grid-enabled message-passing library, and a variety of specialized features to support efficient execution in Grid environments. We have used this framework to perform record-setting computations in numerical relativity, running across four supercomputers and achieving scaling of 88% (1140 CPU's) and 63% (1500 CPUs). The problem size we were able to compute was about five times larger than any other previous run. Further, we introduce and demonstrate adaptive methods that automatically adjust computational parameters during run time, to increase dramatically the efficiency of a distributed Grid simulation, without modification of the application and without any knowledge of the underlying network connecting the distributed computers.