high-performance computing
Efficient
clouds reduce the time to science
Clouds can be flexible but still have to be used efficiently, according to
Dr Bruno Silva, who believes that new
technologies will make the cloud even more important to scientific computing
S
cientific Computing World recently reported on two new cloud initiatives being developed to further scientific research, asking
whether the cloud will change scientific computing. In the USA, researchers are attempting, over the next five years, to federate the private academic clouds of three, geographically dispersed universities into the Aristotle Cloud Federation. In the UK, eMedLab is a biomedical data analytics cloud. It is primarily a
4 SCIENTIFIC COMPUTING WORLD
high-performance computing system with high data throughput characteristics. Its design was proposed by OCF, a UK-based systems integrator, and has been improved and refined through close collaboration with the eMedLab operations and support team, spanning the Francis Crick Institute, University College London, Queen Mary University of London, London School for Hygiene and Tropical Medicine, EMBL- EBI, the Sanger Institute, and more recently King’s College London, led by me at the Francis Crick Institute.
Use private clouds efficiently Although eMedLab is a cloud, the fact that it is private makes it fundamentally different from the public, commercial variety – well beyond the simple fact that in the latter there is a pay-as-you go model. Resources in a private cloud are finite. Users of a private cloud, unless given a fixed, time-bound amount of resource, cannot simply fire some jobs to a system like this, and expect it to run with the most efficient resource allocation possible. In fact, the onus falls squarely
on the researcher to utilise this resource efficiently, which introduces problems in terms of efficient utilisation and return on investment. Cloud is sometimes thought to be
useful for specific types of workloads where efficiency and utilisation do not matter so much, as this is offset by higher
RESOURCES IN A
PRIVATE CLOUD ARE FINITE
customisation – low efficiency and utilisation being the ultimate price for flexibility. Interestingly, there is a divide between scientific computing infrastructure managers; some believe that resource utilisation is of the utmost importance while others disagree: noting that most research computing hardware is generic. What matters in their view is time-to science and not time spent in optimisation. Tere is a perception, oſten repeated, that
these two views are conflicting – that there is a conflict between time-to-science and
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