The Bioinformatics Training Platform (BTP) has been developed to provide access to the computational infrastructure required to deliver sophisticated hands-on bioinformatics training courses. The BTP is a cloud-based solution that is in active use for delivering next-generation sequencing training to Australian researchers at geographically dispersed locations. The BTP was built to provide an easy, accessible, consistent and cost-effective approach to delivering workshops at host universities and organizations with a high demand for bioinformatics training but lacking the dedicated bioinformatics training suites required. To support broad uptake of the BTP, the platform has been made compatible with multiple cloud infrastructures. The BTP is an open-source and open-access resource. To date, 20 training workshops have been delivered to over 700 trainees at over 10 venues across Australia using the BTP.

Robust scientific methods require the exploration of the parameter space of a system (some of which can be run in parallel on distributed resources), and may involve complete state space exploration, experimental design, or numerical optimization techniques. Many-Task Computing (MTC) provides a framework for performing robust design, because it supports the execution of a large number of otherwise independent processes. Further, scientific workflow engines facilitate the specification and execution of complex software pipelines, such as those found in real science and engineering design problems. However, most existing workflow engines do not support a wide range of experimentation techniques, nor do they support a large number of independent tasks. In this paper, we discuss Nimrod/K—a set of add in components and a new run time machine for a general workflow engine, Kepler. Nimrod/K provides an execution architecture based on the tagged dataflow concepts, developed in 1980s for highly parallel machines. This is embodied in a new Kepler "Director” that supports many-task computing by orchestrating execution of tasks on on clusters, Grids, and Clouds. Further, Nimrod/K provides a set of "Actors” that facilitate the various modes of parameter exploration discussed above. We demonstrate the power of Nimrod/K to solve real problems in cardiac science.

Volcanoes are a principal factor of hazard across the Pacific Rim, with their focus of interest mostly divided into pyroclastic flows and ash deposition. The latter has significantly more impact due to its widespread geographical reach and prolonged effects in human activities and health. TEPHRA is a volcanic ash dispersion model based on a simple version of the advection-diffusion Suzuki model, which has been revisited and modified for the Iraz\'u volcano in Costa Rica. A full parameter exploration is necessary in this particular case (albeit not sufficient) due to scarce observational data. We present in this paper the model, its assumptions and limitations as well as application lifecycle with resulting ash distribution graphics. The computational experimental settings are described, in particular the use of Nimrod/G with respect to non-homogeneous parameter sweeps and its impact on execution time. We also analyze the implementation of a new parameter discard mechanism common to e-Science experiments where sequential generation of new parameter sets has to be complemented with an early verification in order to avoid allocation of CPU time to non-valid scenarios. Finally four sample 100K-scenario runs are analyzed for both traditional HPC clustering and Cloud computing resources in the Amazon EC2 Cloud.

Most commercial microscopes are stand-alone instruments, controlled by dedicated computer systems. These provide limited storage and processing capabilities. Virtual microscopes, on the other hand, link the image capturing hardware and data analysis software into a wide area network of high performance computers, large storage devices and software systems. In this paper we discuss extensions to Grid workflow engines that allow them to execute scientific experiments on virtual microscopes. We demonstrate the utility of such a system in a biomedical case study concerning the imaging of cancer and antibody based therapeutics.

Determining the X-ray crystallographic structures of proteins using the technique of molecular replacement (MR) can be a time and labor-intensive trial-and-error process, involving evaluating tens to hundreds of possible solutions to this complex 3D jigsaw puzzle. For challenging cases indicators of success often do not appear until the later stages of structure refinement, meaning that weeks or even months could be wasted evaluating MR solutions that resist refinement and do not lead to a final structure. In order to improve the chances of success as well as decrease this timeframe, we have developed a novel grid computing approach that performs many MR calculations in parallel, speeding up the process of structure determination from weeks to hours. This high-throughput approach also allows parameter sweeps to be performed in parallel, improving the chances of MR success.

In the past decade Grid computing has matured considerably. A number of groups have built, operated, and expanded large testbed and production Grids. These Grids have inevitably been designed to meet the needs of a limited set of initial stakeholders, resulting in varying and sometimes ad-hoc specifications. As the use of e-Science becomes more common, this inconsistency is increasingly problematic for the growing set of applications requiring more resources than a single Grid can offer, as spanning these Grid islands is far from trivial. Thus, Grid interoperability is attracting much interest as researchers try to build bridges between separate Grids. Recently we ran a case study that tested interoperation between several Grids, during which we recorded and classified the issues that arose. In this paper we provide empirical evidence supporting existing interoperability efforts, and identify current and potential barriers to Grid interoperability.