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Center for Subsurface Sensing & Imaging Systems Overview of Image and Data Information Management in CenSSIS David Kaeli Northeastern University Boston, MA [email protected] Overview of the Strategic Research Plan Bio-Med L3 Enviro-Civil S2 S3 S1 S4 S5 Validating L2 TestBEDs R2 L1 Fundamental Science R3 R1 Image and Data Information Management R3 Research Thrust Overview Utilize enabling hardware and software technologies to address CenSSIS barriers Pursue research in enabling technologies Develop a common set of tools and techniques to address SSI problems: Hardware parallelization and acceleration Software toolboxes Image database management and tools Toolboxes CenSSIS Middleware Tools Parallelization of MATLAB, C/C++ and Fortran codes using Message Passing Interface (MPI) – a software pathway to exploiting GRID-level resources Utilizing MPI-2 to address barriers in I/O performance Building on existing Grid Middleware such as Globus Toolkit, MPICH-G2 and GridPort Presently illustrating the impact of the GRID on system level projects (tomosynthesis reconstruction) MPI MATLAB C/C++ Fortran Parallelization MPICH-G2 UPC Air Impact on CenSSIS Applications Mine Soil Reduced the runtime of a single-body Steepest Descent Fast Multipole Method (SDFMM) application by 74% on a 32-node Beowulf cluster Scattered Light Simulation • Hot-path parallelization • Data restructuring • Obtained superlinear speedup of Ellipsoid Algorithm run on a 16-node IBM SP2 • Matlab-to-C compliation • Hot-path parallelization Runtime in seconds • Matlab-to-C compliation • Hot-path parallelization 100000 Original 10000 1000 Matlab-to-C 100 Hot path parallelization 10 1 Ellipsoid Algorithm Speedup (versus serial C version) Speedup Reduced the runtime of a Monte Carlo scattered light simulation by 98% on a 16-node Silicon Graphics Origin 2000 Speedup 20 15 10 5 0 1 2 4 8 16 Number of Nodes 64-vector 1024-vector 256-vector linear speedup Tomographic mammography 3D image reconstruction from x-ray projections Used to detect and diagnose breast cancer Based on well developed mammography techniques Exposes tissue structure using multiple projections from different angles Advantages Accuracy: provides at least as much useful information than x-ray film Flexibility: digital image manipulation, digital storage Structural information: using layered images Safe: low-dose x-ray Lower cost: compared to MRI Image acquisition/reconstruction process Acquisition: 11 uniform angular samples along Y-axis X-ray projection: breast tissue density absorption radiograph Algorithm: constrained non-linear convergence and iterative process Uses a Maximum Likelihood Estimation Y X-ray source Initialization Set 3D volume Forward Compute projections 3D volume Backward Correct 3D volume Exit Yes Y No Satisfied ? Z X-ray projections X detector Parallelization approaches Reduce communication data Segmentation along Y-axis Using redundant computation to replace communication Segmenting along x-ray beam First approach: Non inter-communication (more computation, less communication) Overlap area Second approach: Overlap with inter-communication exchange data Third approach: Non-overlap with inter-communication (less computation, more communication) Tomosynthesis Acceleration •Input data set: phantom 1600x2034x45 Phantom data test results using nonoverlap method on 32 CPUs • Serial implementations runs in 23 hours on a P4 machine 350 • Platforms: – SGI Altix system – UIUC NCSA Titan cluster 250 – P4 cluster at MGH • Number of processors: 32 Computation: SGI Altix with Itanium 2 processor outperforms the other CPUs Currently moving this work to the GRID and the Pittsburgh Supercomputer Center Prototype running on our GRID system at NU Time (sec) – UIUC NCSA IBM p690 – UMich Hypnos cluster File IO Collect Inter-comm Sync Backward Forward 300 200 150 100 50 0 P4 cluster Hypnos cluster Titan cluster Platform IBM p690 SGI Altix Field Programmable Gate Arrays for Subsurface Imaging Backprojection for Computed Tomography image reconstruction Sponsored by Mercury Computer Finite Difference Time Domain (FDTD) in hardware Collaboration with Humanitarian Demining project Retinal Vascular Tracing in real time Collaboration with Real-time Retinal Imaging project Phase Unwrapping Collaboration with 3-D Fusion Microscope project Diverse problems, similar solutions: FPGAs are particularly well suited for accelerating image processing and image understanding algorithms Retinal Vascular Tracing: Register 2-D Image to 3-D in Real Time Objective To accelerate an existing retinal vascular tracing (RVT) algorithm by implementing computation of template responses in reconfigurable hardware PCI PCI BUS BUS RESULTS MEMORY1 DESIGN BLOCK RAM Direction of of Direction blood vessel blood vessel IMAGE MEMORY0 FPGA HOST “Smart Camera” FIREBIRD BOARD Some Recent Publications on Parallelization • “Execution-Driven Simulation of Network Storage Systems,” Y. Wang and D. Kaeli, Proceedings of the 12th ACM/IEEE International Symposium on Modeling, Analysis of Computer and Telecommunication Systems, October 2004, pp. 604-611. • “Profile-guided File Paritioning on Beowulf Clusters,” Y. Wang and D. Kaeli, Journal o f Cluster Computing, Special Issue on Parallel I/O, to appear, • “An Object-oriented Parallel Library,” C. Oaurrauri and D. Kaeli, International Journal of High Performance of Computing and Networking, to appear. • “Digital Tomosynthesis Mammography using a Parallelized Maximum Likelihood Reconstruction Method,” T. Wu, R. Moore, E. Rafferty, D. Koppans, J. Zhang, W. Meleis and D. Kaeli, Medical Imaging, 5368, February 2004. • “Mapping and characterization of applications in Heterogeneous Distributed Systems,” J. Yeckle and W. Rivera , To appear in Proceed. of the 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI2003). • “Profile-Guided I/O Partitioning,” Y. Wang and D. Kaeli, Proceedings of the 17th ACM International Symposium on Supercomputing, June 2003, pp. 252-260. • “Source-Level Transformations to Apply I/O Data Partitioning,” Y. Wang and D. Kaeli, Proceedings of the IEEE Workshop on Storage Network Architecture and Parallel IO, Oct. 2003, pp. 12-21. Held again in 2004 CenSSIS Solutionware – UPRM/NU/RPI Toolbox Development Toolboxes Support the development of CenSSIS Solutionware that demonstrates our “Diverse Problems – Similar Solutions” model Develop Toolboxes that support research and education Establish software development and testing standards for CenSSIS Image and Sensor Data Database Develop an web-accessible image database for CenSSIS that enables efficient searching and querying of images, metadata and image content Develop image feature tagging capabilities Status of the CenSSIS Toolboxes Hyperspectral Image Analysis Toolbox (HIAT) HIAT October 2004 Multiview Tomography Toolbox (MVT) fddlib: January 2003 (v. 1.0) July 2003 (v. 1.1) MVT mvt: October 2004 Rensselaer Generalized Registration Library (RGRL) RGRL September 2004 New toolbox: Improving the quality of radiation oncology @ MGH Developed a 4D (3D + including time) visualization browser tool kit Visualize Computed Tomography (CT) images, organ outlines (wire contours) and the isodose lines (treatment dosage) Present all this information in a user friendly interface 4-D Visualization of Lung Tumors Dosage 4-D Visualization The Future for CenSSIS Toolboxes SCIRun Collaboration with the University of Utah CenSSIS Image Database System Deliver an web-accessible database for CenSSIS that enables efficient searching and querying of images, sensor data, metadata and image content More that 4000 metadata-rich images/datasets presently available online (> 10,000 by 2006) Database Characteristics: • Relational complex queries (Oracle9i) • Data security, reliability and layered user privileges mouse embryo • Efficient search and query of image content and metadata • Content-based image tagging using XML • Indexing algorithms (2D, 3D, and 4D) • Explore object relational technology to handle collections 3 4 2 1 CenSSIS Image Database System CenSSIS Image Database System CenSSIS Image Database System Utilize Machine Learning algorithms to improve query view CenSSIS Image Database System Provides data description associated with initial collection, but does not allow for further elaboration or annotation. Image Annotation Provide the ability to markup image with searchable features Enable image database to be more effectively datamined <xml version=“1.0” encoding=“UTF-8”> <embryo> <description> Embryo developmental stages</description> <feature label=“1” xPos1=“29” yPos1=“33” xPos2=“48” yPos2=“50”> 1 cell embryo </feature> <feature label=“2” xPos1=“50” yPos1=“28” xPos8=“70” yPos2=“40”> 2 cell embryo </feature> <feature label=“3” xPos1= “5” yPos1= “5” xPos2=“25 yPos2=“20”> 4 cell embryo </feature> </embryo> XML and Java • XML (Extensible Markup Language) • Provides maximum flexibility and portability • Well-supported standard • Powerful querying tools available in Oracle • The Java2 Platform • Cross-platform compatibility • Standard web-browser interface • Native XML support Image Tagging A raw image file from the CenSSIS Database • QUERY: I want to be able to add to this image textual annotations, providing my medical team with questions about particular ROIs: • Difficult to describe regions in an image • Difficult to pinpoint specific features in images • Global image metadata too coarse to facilitate low-level tagging Image Tagging Image with tags • Metadata associated with specific areas • Query for specific image features The Image Tagging Interface Drawing Tools Tag Options View Options Tags and XML <feature type="Ellipse" label="4 Cell Stage"> <ellipse> <xCenter> 101 </xCenter> <yCenter> 58 </yCenter> <xRadius> 79 </xRadius> <yRadius> 46 </yRadius> </ellipse> <note> [custom XML tags go here] </note> <annotator> awilliam </annotator> </feature> The Future Role of Image Annotation Provide a vehicle for natural collaboration • A richer set of metadata to enable more detailed queries • Potential to perform extensive data mining on image content • An eye toward content-based image retrieval Tumor tracking paper recently accepted to SIGMOD 2005 The CenSSIS Image Database System Hosts the image and sensor data of CenSSIS (>500 images online) http://censsis-db1.ece.neu.edu/ Provides metadata indexed image searching Uses XML tags to allow for easy information interchange Evolved into a project-based management system, allowing users to organize their data hierarchically Key issue: how do we develop collaboration tools that increase the value of data stored in the database? Presently exploring how best to integrate both visualization and image annotation into the existing framework (NIH proposal) CenSSIS Image and Data Information Management Addressing key research barriers in computational efficiency, embedded computing and image/sensor data management Exploiting Grid resources to enable new discovery in SSI applications Producing a image/data repository and software-engineered Subsurface Sensing and Imaging Toolsets Developing enabling tools targeting system-level projects • Near real-time reconstruction and visualization • Visualization of complex motion • Predicting motion in image data using database indexing techniques MVT