Abstract. Exploiting the full computational power of current hierarchical multiprocessor machines requires a very careful distribution of threads and data among the underlying non-...
Applying Cloud computing techniques for analyzing large data sets has shown promise in many data-driven scientific applications. Our approach presented here is to use Cloud comput...
Kalpa Gunaratna, Paul Anderson, Ajith Ranabahu, Am...
As scientific data sets increase in size, dimensionality, and complexity, new high resolution, interactive, collaborative networked display systems are required to view them in re...
Tom Peterka, Daniel J. Sandin, Jinghua Ge, Javier ...
In this paper we address the problem of managing heterogeneous workloads in a virtualized data center. We consider two different workloads: transactional applications and long-ru...
David Carrera, Malgorzata Steinder, Ian Whalley, J...
An increasing number of science and engineering applications require distributed and parallel computing resources to satisfy user response-time requirements. Distributed science a...
Kenneth A. Hawick, Heath A. James, Craig J. Patten...