The availability of HPC infrastructure and applications on the vScaler Cloud Platform opens up the possibility for new HPC users to either augment their current computing capabilities or take the initial plunge and try HPC technology without investing huge sums of money on an internal HPC infrastructure.

Our finely tuned application-specific stack, enables users to deploy HPC clusters of any scale, either on premise, in the cloud or across both in a hybrid model. The HPC-on-Demand product provides a compelling solution for many challenges of delivering flexible infrastructure for scientific research computing.


One such challenge this addresses is enabling a collaborative research approach across a broad range of research facilities. As we enter a new era of big data, one requirement we see time and time again is the orchestration of team science, where not one university or research group can solve all the complex challenges we are faced with. HPC centers wish to augment their capabilities either through external devices or via collaboration with other research facilities in order to leverage their resources.  Cloud offers that flexibility in order to address multiple questions within a research project.

You will have a cloud-like infrastructure for research and HPC, and then you will have users that want to dip in and out of using cloud technology’ comments David Power, CTO vScaler. One example of using the cloud as a tool to facilitate varied research infrastructure is the emedLab facility. The eMedLab data centre was setup as a co-located data centre that supports a consortium of seven UK universities and research institutes including University College London (UCL), Francis Crick Institute, Kings College London, London School of Hygiene and Tropical Medicine, Queen Mary University of London, Welcome Trust Sanger Institute, and the EMBL European Bioinformatics Institute.

Cloud platforms such as vScaler, offer the flexibility to suit varied workload and facilitates the sharing of data between multiple organizations and can be used to co-locate facilitates and data, further reducing costs as multiple copies of large data sets are unnecessary.

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