High-performance computing centers (HPC) traditionally have far less restrictive privacy management policies than those encountered in healthcare. We show how an HPC can be re-engineered to accommodate clinical data while retaining its utility in computationally intensive tasks such as data mining, machine learning, and statistics. We also discuss deploying protected virtual machines. A critical planning step was to engage the university's information security operations and the information security and privacy office. Access to the environment requires a double authentication mechanism. The first level of authentication requires access to the university's virtual private network and the second requires that the users be listed in the HPC network information service directory. The physical hardware resides in a data center with controlled room access. All employees of the HPC and its users take the university's local Health Insurance Portability and Accountability Act training series. In the first 3 years, researcher count has increased from 6 to 58.
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Currently users of high performance computers are overwhelmed with non scalable tasks such as job submission and monitoring, a problem that gets compounded when trying to run complex scientific applications requiring the coordination of several interrelated programs. Digital Sherpa (DS) is a grid tool set for coordinating the execution of multiple jobs on separate HPC resources; DS automates non-scalable tasks such as job submission and monitoring, and includes recovery features such as resubmission of failed jobs and program restarting. DS has been used to develop a Grid enabled version, MGAC-CGA, of the Modified Genetic Algorithms for Crystals and Clusters (MGAC), a parallel distributed application for the prediction of the structures of atomic clusters and organic crystals using Genetic Algorithms (GA). MGAC-CGA has been successfully tested on the NSF TeraGrid and on several clusters at the University of Utah.
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