Carl Kesselman

TL;DR: This chapter discusses some grid experiences in solving the problem of generating large astronomical image mosaics by composing multiple small images, from the team that has developed Montage, a suite of software tools that was modified to execute in the grid environment.

TL;DR: Bayesian metamodeling as mentioned in this paper is a general approach to modeling complex systems by integrating a collection of heterogeneous input models, which can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity.

TL;DR: Bayesian metamodeling as discussed by the authors is a general approach to modeling complex systems by integrating a collection of heterogeneous input models, each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity.

TL;DR: These tools include data placement services for the reliable, high-performance, secure, and policy-driven placement of data within a distributed science environment; tools and techniques for the construction, operation, and provisioning of scalable science services; and tools for the detection and diagnosis of failures in end-to-end data placement and distributed application hosting configurations.

TL;DR: This paper investigates the fundamental limitations of MPMD communication using a case study of two parallel programming languages, Compositional C++ (CC++) and Split-C, that provide support for a global name space and suggests that RPC-based communication can be used effectively in many high-performance MPMD parallel applications.