MASSACHUSETTS GENERAL HOSPITAL, THE

This proposal requests funding to support a new, state-of-the-art, high performance storage area network (SAN) for high speed and robust access to large quantities of structural and functional imaging data. Specifically, we propose to obtain a multi-terabyte SAN running a parallel filesystem. This type of high-throughput storage solution is designed to work synergistically with large-scale computational clusters such as our current 340 CPU supercomputer that was funded through a previous shared instrument grant (1S10RR019307-01 A Computeserver For Structural & Functional Image Analysis ). Currently, the Martinos Center's storage is based on traditional single-host systems that run a Network File System (NFS). We have found these systems and NFS unable to scale efficiently to support the hundreds of simultaneous jobs that are frequently run on our computational cluster. In our current architecture, a handful of jobs accessing the same NFS server can cause other jobs to timeout and fail to access the data on that server, resulting in significant difficulties in large-scale neuroimaging studies. We have identified four broad areas of users that will realize dramatic benefit from the proposed instrument: a) morphometry studies (to increase throughput on large N morphometry studies), b)fMRI studies (to increase the throughput of surface analysis and statistical characterization, c) multi-modal integration studies (to facilitate complex forward and inverse modeling used in EEG/MEG/fMRI and diffuse optical tomography), and d) algorithm development users (to allow large parameter spaces to be explored in parallel without requiring multiple copies of data). Each of these classes of users now use operational solutions that depend on ad hoc and lengthy delays between the execution time of sequential jobs, which can dramatically decrease efficiency while offering no guarantee of success. The proposed system is expected to deliver robust and rapid access to storage and obviate the need for tedious and time-consuming rerunning of jobs that fail due to network errors. The broad-based user community, which spans 5 institutions (MGH, Harvard, BWH, MIT, and BU) and many departments within these institutions will benefit greatly from a more advanced storage system and the increased capabilities it engenders. This instrument will enhance the performance capabilities of two Regional Resources, enabling these essential facilities to deliver robust, rapid, and dependable access to data for their users. PUBLIC HEALTH RELEVANCE: The proposed instrument will represent significant enhancement to our current ability to efficiently process large quantities of neuroimaging data. With the large number of imaging studies at the Martinos Center that center on clinical populations for instance, studies of Alzheimer's disease, schizophrenia, Huntington's disease, healthy aging such an instrument thus holds great potential for clear and significant public health benefit. The ability to process large studies in parallel, without engendering failures in file access, will enhance our capacity to detect the subtle, early effects of these types of disorders, a critical capability for the development of proper clinical interventions, which are likely most effective in the early stages of neurodegenerative disorders, before widespread cell death occurs.

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