A Biomedical Imaging Acceleration Testbed:
NSF Award Number 0946463

Investigators

David Kaeli, William Karl, Homer Pien, Badrinath Roysam, Nayda Santiago, Miriam Leeser

Contributors

  • Rodrigo Dominguez - Graduate Student - NEU
  • Byunghyun Jang - Graduate Student - NEU
  • Perhaad Mistry - Graduate Student - NEU
  • Richard Moore - Director of Breast Imaging Research, Massachusetts General Hospital, Boston
  • Dana Schaa - Graduate Student - NEU
  • Matthew Sellito - Graduate Student - NEU
  • Justin White - Undergraduate Student - NEU

Description

This project will develop the methodology for rapid parallelization of biomedical imaging applications which will result in a testbed that will provide the capability to "right-size" a multi-GPU system to best meet the goals of any biomedical imaging application. This testbed will utilize a web-based framework that will allow a larger community to leverage the technology available and it will also contain a rich library of parallelized biomedical imaging codes.

Systems

The Medusa Cluster:
  • 4 Server Nodes with 4 quad-core AMD Opteron CPUs in each node.
  • Two NVIDIA Tesla S1070 Units
  • Four ATI 5800 GPUs.
  • Two storage nodes with Infiniband connectivity and quad core CPUs in each node

The Tesla system:

  • 2 Server Nodes with dual-core Intel Xeon CPUs
    • 2 Server Nodes with dual-core Intel Xeon CPUs
    • 2 Server Nodes with dual-core Intel Xeon CPUs
    • NVIDIA Tesla S870 Units
    Other GPU Hardware:
    • 2 GeForce GTX 285
    • 1 GeForce GTX 9800
    • 4 GeForce GTX 8800
    • 1 ATI Radeon HD 5800
    • 1 AMD FireStream 9270

    Applications

  • Microscopy

    Plans for the Microscopy domain have have been formulated as follows.

    Step 1: Multiscale Multi-dimensional Image Pre-processor (MMiPP): Enable near-interactive exploration of scale space characteristics of images, rapid identification of optimal scale space settings & orientation diversity at different scales, and image reconstruction methods for denoising, generic smoothing, texture analysis, enhancement, and specialized enhancement (with a focus on stick, plate, and blob segmentation).

    Step 2: Basic Differential geometry package: First and second order derivates, Jacobian, Hessian, and Weingarten matrices, curvature, ridges, distance maps, plateness, ballness, and stickness fields.

    Step 3: Flexible tensor voting package: (i) voxel based (ii) object based. Both extended to handle ball, plate, and tube classes, leveraging Stage 2.

    Step 4: GPU implementation of time-critical parts of existing FARSIGHT segmentation codes for blobs, network of tubes (thin/thick/hollow), leveraging codes from previous 3 stages. Focus on rapid parameter selection methods.

    Available Software

    An open source repository (gpubiomed) has been created on Google Code

    Recent Talks and Tutorials

  • Dana Schaa and Perhaad Mistry have developed a OpenCL lecture series.
  • Rodrigo Dominguez presented a CUDA tutorial at Spelman College.
    Tutorial slides can be found here.

    Related Publications

    1. Perhaad Mistry, Dana Schaa, Byunghyun Jang, David Kaeli, Albert Dvornik, and Dwight Meglan, Data Structures and Transformations for Physically Based Simulation on a GPU, Computer Engineering, Vol. 6449, 2011, Pages 162-171-171, [Mendeley]
    2. Dana Schaa, Benjamin Brown, Byunghyun Jang, Perhaad Mistry, Rodrigo Dominguez, David Kaeli, Richard Moore and Daniel B. Kopans, GPU Acceleration of Iterative Digital Breast Tomosynthesis, GPU Computing Gems, 2011, Pages 647-657 [Science Direct]
    3. David Kaeli, Byunghyun Jang, Perhaad Mistry, Dana Schaa, Profile-guided optimization of critical medical imaging algorithms, Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging From Nano to Macro, 2009, Pages 1293-1293, [ACM Digital Library]
    4. Byunghyun Jang, Dana Schaa, Perhaad Mistry, David Kaeli, Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures, IEEE Transactions on Parallel and Distributed Systems, Volume 22, Issue 1, 2011, Pages 105-118 [IEEE Xplore]
    5. Byunghyun Jang, David Kaeli, Synho Do, and Homer Pien, Multi GPU Implementation of Iterative Tomographic Reconstruction Algorithms, [ece.neu.edu]