Cloud computing is emerging as an alternative computing platform to bridge the gap between scientists' growing computational demands and their computing capabilities. A scientist who wants to run HPC applications can obtain massive computing resources 'in the cloud' quickly (in minutes), as opposed to days or weeks it normally takes under traditional business processes. Due to the popularity of Amazon EC2, most HPC-in-the-cloud research has been conducted using EC2 as a target platform. Previous work has not investigated how results might depend upon the cloud platform used. In this paper, we extend previous research to three public cloud computing platforms. In addition to running classical benchmarks, we also port a 'full-size' NASA climate prediction application into the cloud, and compare our results with that from dedicated HPC systems. Our results show that 1) virtualization technology, which is widely used by cloud computing, adds little performance overhead; 2) most current public clouds are not designed for running scientific applications primarily due to their poor networking capabilities. However, a cloud with moderately better network (vs. EC2) will deliver a significant performance improvement. Our observations will help to quantify the improvement of using fast networks for running HPC-in-the-cloud, and indicate a promising trend of HPC capability in future private science clouds. We also discuss techniques that will help scientists to best utilize public cloud platforms despite current deficiencies.

As part of the development of the National Virtual Observatory (NVO), a Data Grid for astronomy, we have developed a prototype science application to explore the dynamical history of galaxy clusters by analyzing the galaxies' morphologies. The purpose of the prototype is to investigate how Grid-based technologies can be used to provide specialized computational services within the NVO environment. In this paper we focus on the key enabling technology components, particularly Chimera and Pegasus which are used to create and manage the computational workflow that must be present to deal with the challenging application requirements. We illustrate how the components interplay with each other and can be driven from a special purpose application portal.