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A Grid Architecture for Medical Applications

A Grid Architecture for Medical Applications. Anca Bucur, Rene Kootstra Philips Research Eindhoven. Robert Belleman University of Amsterdam. The GAMA Research Goals.

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A Grid Architecture for Medical Applications

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  1. A Grid Architecture for Medical Applications Anca Bucur, Rene Kootstra Philips Research Eindhoven Robert Belleman University of Amsterdam

  2. The GAMA Research Goals • Design an adaptive architecture that would enable several relevant compute-intensive medical applications to use Grid technologies, for • improved performance • cost-effective access to large numbers of various resources (computational, data, information, special instruments, etc.) • Study the common and differentiating characteristics of the applications to which this architecture applies, in order to identify classes of healthcare applications fitting the architecture

  3. Our Approach • Investigated relevant computationally challenging medical applications suited for parallelization through decomposition • Identified patterns of compute-intensive applications, based on their decomposition paradigm • Designed an adaptive architecture for solving compute-intensive medical applications (fitting at least one of the patterns) using Grid technology • Chose several medical applications fitting these patterns for which to develop Grid-enabled prototypes complying to the defined architecture

  4. A B A B C D C D The Application Patterns Three patterns of compute-intensive applications Domain decomposition Computational decomposition Functional decomposition

  5. The GAMA Architecture (I) • Targets computationally challenging applications • Suitable for applications with a large degree of spatial and time locality • Adaptive to applications fitting the decomposition patterns • Simultaneously provides different sets of services to multiple users and applications • Uses Grid resources for improved performance • Minimally invasive: Running on Grid as an alternative, easy fall back to local versions

  6. The GAMA Architecture (II)

  7. Client Gateway LAN LAN DutchGrid Resources WindowsIDL, Pride LinuxGlobus The GAMA architecture (III) • Windows-based interface in the hospital, the compute-intensive part is placed in the Unix-based Grid environment • Uses Grid resources and technology (e.g. Globus) • Client-server architecture, single interface solution between client(s) and server • One access point (GAP), sends requests from client(s) to server and returns results

  8. The GAMA Case-Study Functional Brain Imaging and White Matter Fiber Tractography • Uses Diffusion Weighted MRI • Fibers visualize anisotropic diffusion of water molecules in brain: • White matter tracts, areas active for different tasks • Connecting pathways between brain structures • ROIs used to select areas of interest • Clinical relevance: surgical planning, stroke detection, psychiatry

  9. The Fiber Tracking Application (I) • Performance gain from distributing the computational part over Grid resources, computational decomposition • Runtime may depend on: the number of starting points, the algorithm, the size of the data set, the number of ROIs • Quick Fiber Tracking: Starting points in the ROIs • Full Volume Fiber Tracking: • Starting points evenly distributed in the entire domain • Detects splitting and crossing fibers, large number of fibers • May result in a clogged image • High computational needs

  10. The Fiber Tracking Application (II) • FVFT amounts to over 10 hours for small voxels • Too few starting points - missing relevant fibers • Too many starting points - crowded image Solution: • Improved throughput: parallelization • Improved accuracy : careful selection of the ROIs. Grid solution: FVFT with good throughput and good accuracy

  11. The FT Architecture

  12. The DAS-2 System Experiments performed on: • DAS-2 (Distributed ASCI Supercomputer), a wide-area distributed cluster system designed by the Advanced School for Computing and Imaging (ASCI). • The DAS-2 is used for research on parallel and distributed computing. • Five clusters, located at five universities. One with 72 nodes, the other four with 32 nodes • 200 nodes with 400 CPUs in total. The system was built by IBM.

  13. Scalability Results (I) • Tracking long fibers takes much longer, fibers are grouped in bundles • Round Robin distribution, slices of width equal to voxel size • For FVFT the number of ROIs has little influence on performance 1ROI: NGFT 448.69s (Tw = 20ms) Tc32=2.1s, Tc16=1.9, Tc8=1.2s, Tc4=0.9s 4 ROI: NGFT 461.34s • Experiments for large step of tracking fibers • Improved throughput

  14. Scalability Results (II) (almost) linear speed-up, improved performance for up 32 processors • Figures compare two interpolation steps • Limit the speed-up (large influence): • The interpolation algorithm • The execution time for tracking the longest fiber, the communication time • The distribution of starting points • Implement a workpool-based solution

  15. Future Work • Identify other relevant medical applications fitting the decomposition patterns • Apply the GAMA architecture to applications fitting domain and functional decomposition • Investigate other medical applications with different decomposition characteristics

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