Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems

The advent of Grid computing has enhanced our capabilities to model and simulate complex systems arising in scientific, engineering, and commercial applications. The premise of Grid computing has been "on-demand" availability of computational resources to an application as needed, in the same manner as electricity is provided readily through electrical power grids. The computational grid (or simply the "Grid") entails ubiquitous access to resources (local or remote), such as computation and communication resources, as well as access to storage systems and visualization systems. As Grid computing technologies mature, it behooves to look beyond the current capabilities, into more advanced future environments. The environments of interest here are the enhanced capabilities that can be created by the paradigm of dynamic data driven applications systems (DDDAS). DDDAS entails the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. The DDDAS concept offers the promise of improving application models and methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. Enabling this synergistic feedback and control loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms stable under dynamic data injection and steering conditions, and new systems software and computational infrastructure capabilities. Recent advances in complex applications and the advent of Grid computing and sensor systems are some of the technologies that make it timely to embark in developing DDDAS capabilities. DDDAS environments extend the current notion of Grid infrastructure to also include the measurement systems in an integrated and synergistic way. DDDAS environments require support and services that go beyond the current Grid services in terms of t     

The Next Generation Software Program

The papers in this issue provide an overview of the research fostered by the NSF Next Generation Software (NGS) Program², and some representative projects funded under the program. The NGS Program was announced in October of 1998, and with several calls for proposals between 1998 and 2004 has supported research in two broad technical thrusts. One program component has supported research for developing Technology for Performance Engineered Systems (TPES) for the Design, Management and Runtime Support of Computing Systems and Applications. The second program component, Complex Application Development and runtime Support Systems (CADSS) has sought to create new systems’ software technology, including enhanced compiler capabilities, and tools for the development, runtime support and dynamic composition of complex applications executing on complex computing platforms, such as Computational Grids, assemblies of embedded systems and sensor systems, as well as high-end platforms (Grids-in-a-Box) and special purpose processing systems. Work along the directions of the NGS Program presently continues under the successor program, the NSF Computer Systems Research Program.     

Multipurpose Parallelism for VLSI Cad on the RP3

VLSI CAD application developers need the performance of parallel processing in as general a form as possible. The RP3, which is being developed at IBM's Research Division, provides such generality. Several CAD applications are among the more than 30 applications that have been written in the Epex parallel environment for porting to RP3 when the hardware is complete. Placement by simulated annealing is used here as a significant, deliberately difficult example: its theoretical basis requires serial execution. In the parallel technique used, deviation from the serial algorithm and temporary errors are allowed for more efficient exploitation of parallelism. The result is a convergence rate as good as the original algorithm, with the possibility of efficient execution on hundreds of processors.