© World Scientific Publishing Company ON THE CONVERGENCE OF COMPUTATIONAL AND DATA GRIDS

Great advances in high-performance computing have given rise to scientific applications that place large demands on software and hardware infrastructures for both computational and data services. With these trends the necessity has emerged for distributed systems developers that once distinguished between these elements to acknowledge that indeed computational and data services are tightly coupled and need to be addressed simultaneously. In this article, we compile and discuss several strategies and techniques, like co-scheduling and co-allocation of computational and data services, dynamic storage capabilities, and quality-of-service, that can be used to help resolve some of the aforementioned issues. We present our interactions with a distributed computing system, NetSolve, and a Distributed Storage Infrastructure, IBP, as a case study of how some of these techniques can be effectively deployed and offer experimental evidence from early prototypes that validate our motivation and direction

Broadway: A Software Architecture for Scientific Computing

Scientific programs rely heavily on software libraries. This paper describes the limitations of this reliance and shows how it degrades software quality. We offer a solution that uses a compiler to automatically optimize library implementations and the application programs that use them. Using examples and experiments with the PLAPACK parallel linear algebra library and the MPI message passing interface, we present our solution, which includes a simple declarative annotation language that describes certain aspects of a library's implementation. We also show how our approach can yield simpler scientific programs that are easier to understand, modify and maintain