March 2008
Urgent Computing: Exploring Supercomputing's New Role
Steven Manos
Stefan Zasada
Peter V. Coveney, Centre for Computational Science, Chemistry Department, University College London


Systems such as HARC and SPRUCE were initially conceived to support the submission of very infrequent on-demand jobs, for example climate models, that typically are only run in emergency situations, such when a hurricane is looming; running simulations at this frequency on general purpose compute resources, such as those available on the TeraGrid, have a negligible effect on the users of the system as a whole. In the case of an urgent simulation using SPRUCE, a limited set of users who had their jobs pre-empted would not notice anything different. Patient-specific medical simulations are of a different nature; a successful patient-specific simulation technique will likely have thousands, or even tens of thousands, of possible patients that it could be performed for. The possible level of compute time required will dwarf the current urgent-computing policies and resources in place.

Patient-specific medical simulation raises several moral, ethical and policy questions that need to be answered before the methodologies can be put to widespread use. Firstly there is the question of the availability of resources to perform such simulations. The compute power currently made available through general purpose scientific grids, such as the TeraGrid or UK NGS, is not enough to satisfy the potential demand of medical simulation. The scarcity of resources raises the question of how such resources will be allocated. Which patients will benefit from medical simulations? Will it be based on the ability to pay? Secondly there is the question of data privacy. Sensitive clinical information is often kept on highly secure hospital networks, and the owners and administrators of such networks are often loath to let any data move from it onto networks over which they have no control, which is necessary if the data is to be shipped to a remote site and used in a simulation. Using such data on ‘public’ grid resources requires it to be suitably anonymised, so that even if it were to fall into the wrong hands it could not be traced back to the patient it was taken from.

We believe that as such tasks become more widespread and embedded in the clinical process, the market will start to address the first question raised above. Already, many companies are starting to provide utility compute services, such as Amazon’s Elastic Compute Cloud 15, which allows the public to purchase computational cycles. If a market was created for running medical simulations on demand, then we believe it likely that utility compute providers will move to supply the necessary compute services. Although it is uncertain how a pricing model will work in reality, it is likely that the utility compute model will drive down the costs of such simulations, and where the performance of simulations is shown to make a treatment regime more efficient, it is likely that the cost could be met from the money saved. The second question needs to be addressed by medical data managers and government regulators. Once enabling policies have been developed, the process of routinely anonymising data and shipping it from a hospital network or storage facility will become routine. Such a system of anonymisation is being implemented in the neurovascular project discussed, involving discussions with technical network administrators and management from the UK National Health Service (NHS).

It is essential that a dialogue is joined between governments, researchers, health professionals and business into how the infrastructure needed to perform patient specific medical simulations can be performed on a routine basis. The benefits of performing such simulations are too great to be ignored and, in addition to the case studies presented, we believe that computational simulation will be used in more and more medical scenarios. In the vision that patient-specific medical simulations become a day to day reality in the treatment of patients, vast quantities of simulation data will be available alongside traditional medical data. With parallel advances in data warehousing, data-mining and computational grids, the enhancement of medical practice using simulation will one day become a reality.

Acknowledgments We acknowledge the work of Kashif Sadiq and Marco Mazzeo, based at the UCL Centre for Computational Science, for their work on the HIV and cerebral bloodflow case studies. We are grateful to EPSRC for funding much of this research through RealityGrid and RealityGrid Platform grants (GR/R67699 and EP/C536452), the GENIUS project (EP/F00561X), and also Integrative Biology (GR/S72023). Our work was partially supported by the National Science Foundation under MRAC award DMR070014N, which provided access to numerous computational resources on the US TeraGrid. This research has also been partially supported by the EU-funded ViroLab project (IST-027446) and has benefitted greatly through involvement of PVC in the EU-funded STEP Coordination Action (no. 027642).
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Reference this article
Manos, S., Zasada, S., Coveney, P. V. "Life or Death Decision-making: The Medical Case for Large-scale, On-demand Grid Computing," CTWatch Quarterly, Volume 4, Number 1, March 2008. http://www.ctwatch.org/quarterly/articles/2008/03/life-or-death-decision-making-the-medical-case-for-large-scale-on-demand-grid-computing/

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