A GPU Accelerated High Performance Cloud Computing Infrastructure for Grid Computing Based Virtual Environmental Laboratory

Numerical models play a main role in the earth sciences, filling in the gap between experimental and theoretical approach. Nowadays, the computational approach is widely recognized as the complement to the scientific analysis. Meanwhile, the huge amount of observed/modelled data, and the need to store, process, and refine them, often makes the use of high performance parallel computing the only effective solution to ensure the effective usability of numerical applications, as in the field of atmospheric /oceanographic science, where the development of the Earth Simulator supercomputer [65] is just the edge. Grid Computing [38] is a key technology in all the computational sciences, allowing the use of inhomogeneous and geographically spread computational resources, shared across a virtual laboratory. Moreover, this technology offers several invaluable tools in ensuring security, performance, and availability of the applications. A large amount of simulation models have been successfully developed in the past, but a lot of them are poorly engineered and have been designed following a monolithic programming approach, unsuitable for a distributed computing environment or to be accelerated by GPGPUs [53]. The use of the grid computing technologies is often limited to computer science specialists, because of the complexity of grid itself and of its middleware. Another source of complexity resides on the use of coupled models, as, for example, in the case of atmosphere/seawave/ocean dynamics. The grid enabling approach could be hampered by the grid software and hardware infrastructure complexity. In this context, the build-up of a grid-aware virtual laboratory for environmental applications is a topical challenge for computer scientists. The term “e-Science” is usually referred to computationally enhanced science. With the rise of cloud computing technology and on-demand resource allocation, the meaning of eScience could straightforwardly change to elastic-Science. The aim of our virtual laboratory is to bridge the gap between the technology push of the high performance cloud computing and the pull of a wide range of scientific experimental applications. It provides generic functionalities supporting a wide class of specific e-Science application environments and