Center for Subsurface Sensing Imaging Systems Overview of … · Center for Subsurface Sensing &...

Center forSubsurface Sensing & Imaging Systems

Center forSubsurface Sensing & Imaging Systems

NSF Year-2 Site VisitMay 21, 2002NSF Year-2 Site VisitMay 21, 2002

Overview of Research Thrust R3

R3 Fundamental Research Topics R3A Parallel Processing

• Programming Tools and Systems• FPGA Acceleration

R3B Solutionware Development• Image and Sensor Data Databases• Subsurface Toolboxes

R3 Fundamental Research Topics R3A Parallel Processing

• Programming Tools and Systems• FPGA Acceleration

R3B Solutionware Development• Image and Sensor Data Databases• Subsurface Toolboxes

David Kaeli, Northeastern UniversityDavid Kaeli, Northeastern University

! Supporting SSI research leveraging existing computing technologies! Developing new techniques in the areas of parallel processing/embedded and

databases to address CenSSIS barriers! Delivering software engineered products to enable IPLUS capabilities

ScopeScope

SubsurfaceSensing and

Modeling

Validation Testbeds

Environmental-CivilApplications

Bio-MedicalApplications

Image and DataInformationManagement

Physics-BasedSignal Processing andImage Understanding

I�PLUS

R1R1R2R2

R3R3L1L1

L2L2

L3L3

Context and Scope of R3 ResearchContext and Scope of R3 Research

EngineeredSystemEngineeredSystem

EnablingTechnologiesEnablingTechnologies

FundamentalScienceFundamentalScience

CenSSIS Barriers Addressed by R3 ProjectsCenSSIS Barriers Addressed by R3 Projects

Lack of Computationally Efficient, Realistic ModelsLack of Computationally Efficient, Realistic Models

Barrier 6Barrier 6 Lack of Rapid Processing and Management of Large Image DatabasesLack of Rapid Processing and Management of Large Image Databases

Barrier 7Barrier 7 Lack of Validated, Integrated Processing andComputational ToolsLack of Validated, Integrated Processing andComputational Tools

Barrier 4Barrier 4

R3 Year 2 Research ProjectsR3 Year 2 Research ProjectsR3A Programming Tools and Systems

• Parallel Programming Tools (G. Krapf (junior), M. Ashouei, W.Meleis, D. Kaeli, K. Tompko, D. Brooks, C. Dimarzio, C. Rappaport, M. El-Shenawee)• Cluster Development (C. Shaffer (soph), M. Dellaporta and D. Kaeli)

R3A Reconfigurable Embedded Computing• Vascular Tracing in Embedded Hardware (P. Belanovic, M. Leeser, B. Roysam)• FPGA Implementation of Backprojection for Rapid Tomographic Imaging (S. Molloy (junior), J. Noseworthy (junior), M. Leeser, E. Miller and Mercury )

R3B Image/Sensor Data Databases and Metadata• Content Searchable Image and Sensor Data Databases (R. Norum, B. Salzberg, H. Wu, D. Kaeli and E. Miller)

R3B Toolbox Development• The CenSSIS Tomography Toolbox (P. Edson, J. Black, Y. Wang, E. Yardimci, D. Kaeli, D. Brook, E. Miller and D. Boaz)

FundamentalScienceLevel

EngineeredSystemLevel

EnablingTechnology

Level

R1A Nonlinear and Dual Wave Probes

I-PLUS DevelopmentBio, Med, Soil, Sea (Real Problems)

Initial TestBED FacilitiesBio, Med, Soil, SeaBEDs

R3B Solutionware Tools

R3A Parallel Hardware Implementation forFast Subsurface Detection

R2D Image Understanding & Sensor FusionMethods

R2C MSD Methods

R2B LPM Methods

R2A MVT Methods

R1B Effective Forward Models

Relative Contribution to Outcomes

Important Outcomes

CenSSIS Research Areas Mul

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Accelerating Modeling and

Retinal Tracing Applications

R3 Research Projects � Year 2 ProgressR3 Research Projects � Year 2 Progress



EnablingTechnology

Level







R2C MSD Methods

R2B LPM Methods

R2A MVT Methods



Important Outcomes


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Tomography (MVT)Toolbox and Image DB




EnablingTechnology

Level







R2C MSD Methods

R2B LPM Methods

R2A MVT Methods



Important Outcomes


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AddressingBarriers 4, 6

and 7 Directly

NU, UCinn, RPI, UArk

NU, RPI, WHOI


Parallel Programming Tools and Systems

Year 2 Research Team: ! Geoff Krapf (NU-ECE)

! Craig Shaffer (NU-ECE)

! Maryam Ashouei (NU-ECE)

! Desheng Jiang (NU-ECE)

! David Kaeli (NU-ECE)

! Karen Topko (U. Cinn.)

! Waleed Meleis (NU-ECE)

R1-R2 Collaborators:! Dana Brooks (NU-ECE)

! Chuck Dimarzio (NU-ECE)

! Magda El-Shenawee (U. Ark.)

! Carey Rappaport (NU-ECE)

Parallel Processing ToolsParallel Processing Tools

! Parallelization using MPI – a software pathway to exploiting GRID-level resources (Mariner Center at BU)

! Profile-guided program optimization – reducing computational barriers

! Utilizing MPI-2 to address barriers in I/O performance

Impact on SSI applications:! Reduced the runtime of a single-body Steepest Descent Fast

Multipole Method (SDFMM) application by 74% on a 32-node Beowulf cluster• Hot-path parallelization• Data restructuring

! Reduced the runtime of a Monte Carloscattered light simulation by 98% on a 16-node SGI Origin 2000• Matlab-to-C compliation• Hot-path parallelization

! Parallelization using MPI – a software pathway to exploiting GRID-level resources (Mariner Center at BU)

! Profile-guided program optimization – reducing computational barriers

! Utilizing MPI-2 to address barriers in I/O performance

Impact on SSI applications:! Reduced the runtime of a single-body Steepest Descent Fast

Multipole Method (SDFMM) application by 74% on a 32-node Beowulf cluster• Hot-path parallelization• Data restructuring

! Reduced the runtime of a Monte Carloscattered light simulation by 98% on a 16-node SGI Origin 2000• Matlab-to-C compliation• Hot-path parallelization

Soil

Air

Mine

Scattered Light Simulation Speedup

1

10

100

1000

10000

100000Ru

ntim

e in

sec

onds

Original

Matlab-to-C

Hot pathparallelization

Techniques for Parallelizing MATLABTechniques for Parallelizing MATLAB

! Manage completely independent MATLAB processes distributed over

different processors

! Message passing within MATLAB (MultiMATLAB)

! MATLAB calls to parallel libraries (multi-threaded LAPACK, PLAPACK)

! Backend compilers can convert MATLAB to C, and automatically inserting MPI calls (RTExpress)

Multiple MATLAB sessions

A SingleMATLABsession

Our Approach for Parallelizing MATLABOur Approach for Parallelizing MATLAB

Convert MATLAB to C using

the MATLAB mcc compiler

Profile the C program to capture

both data flow and control flow

Parallelize the “hot” regions of the

the application using MPI

Convert array structs (generated by mcc)

to pointer-based structs where needed

Main0/2502

Tfqmr1/1604

3/35

Mult11/1602

Mult4/1599

Multfaflone4m523/6Multfaflone4

359/5

Multdifl706/0

0/0

0/0

0/0

18/0

16/0

0/0 0/0

11

1 1

273

273

273

273

273

273

273

54K2.4M

0.6M

FunctionSelf /Children Runtime

Number of Calls

State-of-the-art Parallel Processing ToolsState-of-the-art Parallel Processing Tools

CenSSIS Publications:“Profile-based Characterization and Tuning Subsurface Sensing Applications”, M.

Ashouei, D. Jiang, W. Meleis, D. Kaeli, M. El-Shenawee, E. Mizan, M. and C.Rappaport, invited to a special issue of the SCS Journal, to appear November 2002.

“Parallel Implementation of the Steepest Descent Fast Multipole Method (SDFMM) on a Beowulf Cluster for Subsurface Sensing Application”, D. Jiang, W. Meleis, M. El-Shenawee, E. Mizan, M. Ashouei, and C. Rappaport, IEEE Microwave and Wireless Components Letters, January 2002.

“Electromagnetics Computations Using MPI Parallel Implementation of the Steepest Descent Fast Multipole Method (SDFMM)”, M. El-Shenawee, C. Rappaport, D. Jiang, W. Meleis, and D. Kaeli, Applied Computational Electromagnetics Society Journal, to appear August 2002.

Other techniques being assessed in this work:! Globus Toolkit � NSF Middleware Initiative! MatlabMPI � MIT LL ! RTExpress - Parallelizing compiler for Matlab! Sparse matrix representations

CenSSIS Publications:“Profile-based Characterization and Tuning Subsurface Sensing Applications”, M.

Ashouei, D. Jiang, W. Meleis, D. Kaeli, M. El-Shenawee, E. Mizan, M. and C.Rappaport, invited to a special issue of the SCS Journal, to appear November 2002.

“Parallel Implementation of the Steepest Descent Fast Multipole Method (SDFMM) on a Beowulf Cluster for Subsurface Sensing Application”, D. Jiang, W. Meleis, M. El-Shenawee, E. Mizan, M. Ashouei, and C. Rappaport, IEEE Microwave and Wireless Components Letters, January 2002.

“Electromagnetics Computations Using MPI Parallel Implementation of the Steepest Descent Fast Multipole Method (SDFMM)”, M. El-Shenawee, C. Rappaport, D. Jiang, W. Meleis, and D. Kaeli, Applied Computational Electromagnetics Society Journal, to appear August 2002.

Other techniques being assessed in this work:! Globus Toolkit � NSF Middleware Initiative! MatlabMPI � MIT LL ! RTExpress - Parallelizing compiler for Matlab! Sparse matrix representations

Cluster Development:The Mercury RACE SystemCluster Development:The Mercury RACE System

8 � nodePPC 750�sRaceway

backplaneMC/OS

PASVSIPL

Ultra10 hostedweb-

accessible

Cluster Development:The Gigabit ClusterCluster Development:The Gigabit Cluster

8 � node Gigabit Cluster1.2 GHZ P31.5 GB dram45 GB disk

10/100/1000 switched ethernet

RedHat Linux 7.2MATLAB

MPI-2

Reconfigurable Hardware for Accelerated Vessel Enhancement in Retinal Fundus ImagesReconfigurable Hardware for Accelerated Vessel Enhancement in Retinal Fundus Images

What does the algorithm do?! Retinal vascular tracing; detection of blood vessels in

images of the retina ! Utilizes a matched filter to find blood vessels and traces

out structure

Where is the algorithm used?! Processing live video of the patient retina during laser

retinal surgery.! Data are 1024x1024 images at 30 frames/sec! Highlighting the vascular structure helps the surgeon avoid

damage

Research Team• Miriam Leeser � NU-ECE• Pavle Belanovic � NU-ECE• Badri Roysam � RPI-ECSE• Kenneth Fritzsche � RPI-ECSE


Why do we need to accelerate it?! Current implementation:

software on a general-purpose processor

! It takes about 400ms to process one 1024x1024 image

! To reach 30 frames/sec, the algorithm must be accelerated at least 12 times

FIREBIRD BOARD

FPGA

RE

GIS

TE

RS

DESIGN

MEMORY

IMAGE

HOST

Direction ofblood vessel

Location ofpixel toexamine

PCI BUS

RE

SPO

NSE

TEMPLATE_COMPARATOR

RE

SPO

NSE

RE

SPO

NSE

RE

SPO

NSE

RE

SPO

NSE

RE

SPO

NSE

RE

SPO

NSE

RE

SPO

NSE

TEMPLATE_COMPARATOR

TEMPLATE_COMPARATOR

TEMPLATE_COMPARATOR

TEMPLATE_COMPARATOR

TEMPLATE_COMPARATOR

TEMPLATE_COMPARATOR

INTERCONNECTION

RE

G0

RE

G1

RE

G2

RE

G3

RE

G4

RE

G5

RE

G6

RE

G7

RE

G8

RE

G9

RE

G10

RE

G11

RE

G12

RE

G13

RE

G14

RE

G15

000 001 010 011 100 101 110 111

RESPONSE TEMPLATE

Solution: Reconfigurable HardwareThe Firebird reconfigurable computingengine from Annapolis Micro Systems! 1 Xilinx VIRTEX E (XCV2000E) FPGA! 5 Memory banks (4 x 64-bit, 1 x 32-bit)! 5.4 Gbytes/sec of memory bandwidth! 66Mhz/64-bit PCI interface to host


Year 2 Progress! Partitioned the algorithm: template response calculations in

hardware! Determined mapping of templates onto the pixel neighborhood! Designed, simulated and synthesized modules for:

• partial template response,• template response,• comparison of template responses, and• full datapath

Year 3 Plan! Integration of the hardware implementation into

the algorithm (20x speedup anticipated)! Processing of retina images and live video! Apply similar techniques to identify sub-skin

tumors in tissue (MGH)

R3 Undergraduate InvolvementR3 Undergraduate Involvement

Creating unique undergraduate research

experiences for our students

www.censsis.neu.edu/Industry/IUROP/

! Craig Shaffer � (NSF - REU, IUROP - Mercury)! Geoff Krapf � (NSF - REU)! Seth Molloy � (IUROP � Mercury)! Josh Noseworthy � (IUROP � Mercury)



EnablingTechnology

Level

∆ = +1

Proposed Changes in Emphasis for Year Three Research ProgramProposed Changes in Emphasis for Year Three Research Program

Important Outcomes


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R2C MSD Methods

R2B LPM Methods

R2A MVT Methods



Libraries of VHDL and

parallelized SSI applications



EnablingTechnology

Level

∆ =+1


Important Outcomes


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R2C MSD Methods

R2B LPM Methods

R2A MVT Methods



Retinal scanningtestbed demo

CenSSIS Solutionware Year 2 GoalsCenSSIS Solutionware Year 2 Goals

Toolbox Development! Support the development of CenSSIS Solutionware that

demonstrates our “Diverse Problems – Similar Solutions” model

! Deliver a software-engineered Tomography MVT Toolbox, developed in OOMATLAB

! Identify new Toolbox candidates for Year 3! 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

Toolbox Development! Support the development of CenSSIS Solutionware that

demonstrates our “Diverse Problems – Similar Solutions” model

! Deliver a software-engineered Tomography MVT Toolbox, developed in OOMATLAB

! Identify new Toolbox candidates for Year 3! 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

Matlab 6

CenSSIS Subsurface Toolboxes

Year 2 Research Team: ! Patrick Edson (MathWorks)

! Jennifer Black (NU-ECE)

! Yijian Wang (NU-ECE)

! David Kaeli (NU-ECE)

! Dana Brooks (NU-ECE)

! Eric Miller (NU-ECE)

Collaborators:

! David Boaz (MGH)

New members in Year 3:! Chris Carothers (RPI-CS)

! Badri Roysam (RPI-ECSE)

! Luis Jimenez (UPRM-ECE)

Matlab 6

CenSSIS Toolbox Development

CenSSIS Applications written in MATLAB, C, C++, Java, MPI, VHDL and Verilog

MVT MSD LPM Modeling

! Identify classes of CenSSIS algorithms that can be more generally specified to extend applications to multiple research domains

! Utilize Software Engineering principles to produce reusable and extensible software toolboxes• Object-oriented design principles used• Exploratory Software Development Model• Library and revision control to support IPLUS infrastructure

! Identify classes of CenSSIS algorithms that can be more generally specified to extend applications to multiple research domains

! Utilize Software Engineering principles to produce reusable and extensible software toolboxes• Object-oriented design principles used• Exploratory Software Development Model• Library and revision control to support IPLUS infrastructure

The CenSSIS Multi-View Tomography (MVT) ToolboxThe CenSSIS Multi-View Tomography (MVT) Toolbox

Diffuse Optical Tomography

3 Packages

8166 lines of OOMATLAB

22 MATLAB classes/126

methods

Generic noise classesTomography Reconstruction

Matlab 6

EIT

ERT

DOT

MVT

Year 3 Goals for Toolbox DevelopmentYear 3 Goals for Toolbox Development

! Release Tomography (MVT) Toolbox as part of the MathWorks Connections program

! Evaluate the level of effort needed to address diverse problems using the Tomography Toolbox to address problems in EIT and ERT (INEEL)

! Extend our model to three new Toolbox efforts! Registration (LPM) Toolbox � RPI and WHOI! Hyperspectral (MSD) Toolbox � UPRM! Data Modeling Toolbox � NU

! Release Tomography (MVT) Toolbox as part of the MathWorks Connections program

! Evaluate the level of effort needed to address diverse problems using the Tomography Toolbox to address problems in EIT and ERT (INEEL)

! Extend our model to three new Toolbox efforts! Registration (LPM) Toolbox � RPI and WHOI! Hyperspectral (MSD) Toolbox � UPRM! Data Modeling Toolbox � NU

Matlab 6MSD LPM Modeling

Year 2 Research Team:

• David Kaeli (NU-ECE)

• Eric Miller (NU-ECE)

• Huanmei Wu (NU-CCS)

• Betty Salzberg (NU-CCS)

• Becky Norum (NU-CenSSIS)

Collaborators:

• Patrick Muraca (Clinomics)

• Hanu Singh (WHOI)

New Members in Year 3:

• Manuel Rodriguez (UPRM-ECE)

The CenSSIS Image and Sensor Data DatabaseThe CenSSIS Image and Sensor Data Database

" Deliver an web-accessible database forCenSSIS that enables efficient searching and querying of images, sensor data, metadata and image content

" Database Characteristics:

• Relational complex queries (Oracle8i)

• Data security, reliability and layered user privileges

• Efficient search and query of image content and metadata

• Content-based image tagging using XML adopting MPEG-7 standards

• Easy upload and registration of user images and metadata

• Indexing algorithms (2D, 3D and 4D) and partitioning of

the database for better performance

• Explore object relational technology to handle collections

Goals for Year 2Goals for Year 2

12

34

nucleus

<Image><CellInformation><ID> 9 </ID><ClinomicsID> 931175495 </ClinomicsID><DOB> 2/7/30 </DOB><SEX> F </SEX><COLL_DATE> 11/2/1993 </COLL_DATE><Primary_site> Breast </Primary_site><INITIAL> II </INITIAL><GRADE> POORLY DIFFERENTIATED </GRADE><HISTOLOGY> UNKNOWN </HISTOLOGY><PRIM_SITE2> NONE </PRIM_SITE2><PRIM_DATE> 4/1/1992 </PRIM_DATE><MET1_SITE> NONE </MET1_SITE><MET1_DATE> NONE </MET1_DATE><TUBE_TYPE> p </TUBE_TYPE>

</CellInformation>

<StillRegion id="IMG0001"><MediaProfile><MediaFormat>

<FileFormat>jpeg</FileFormat><System>PAL</System><Medium>CD</Medium><Color>color</Color><FileSize>332.228</FileSize>

</MediaFormat>. . . . . . . .

</MediaCoding><MediaInstance>

<Identifier IdOrganization='Clinomics' IdName='BreastCancerCell'>BreastCancerCell// image0001

</Identifier><Locator>

<MediaURL>file://D:/Breast/cells/imag0001.jpg</MediaURL></Locator></MediaInstance>

</MediaProfile>

<StructuredAnnotation><Who>Patient239</Who><whatObject>Human primary breast tumor

cells</whatObject><WhatAction> growing in a NASA Bioreactor

</WhatAction><where> St. Mary’s Hospital </where><When> 09/25/2000 </When><why> Investigate tumor cells behaviour on microcarrier

beads </why><TextAnnotation xml:lang='en-us'>

Higher magnification of view illustrating breast cancer cells with intercellular boundaries on bead surface

</TextAnnotation></StructuredAnnotation>

</StillRegion></Image>

General ImageInfo

Image SourceInfo

Image FormatInfo

Image FileLocation Info

Image DonorInfo

Image FeatureInfo

R3 Research Projects � Year 3 PlansR3 Research Projects � Year 3 Plans







R2C MSD Methods

R2B LPM Methods

R2A MVT Methods



Important Outcomes


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∆=+1


EnablingTechnology

Level

∆ = +1∆ = +2

ModelingToolbox

>500 Datasets Online, 3D and 4D indexing,

3 new CenSSISToolboxes



EnablingTechnology

Level

∆ = +1∆ =+1








R2C MSD Methods

R2B LPM Methods

R2A MVT Methods



Important Outcomes


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New Toolboxes, 3-D FusionMicroscope

Database Support

R3 Interaction with IndustryR3 Interaction with Industry

! Clinomics BioSciences• Microarray provider• Joint SBIR submissions

! Mercury Computer• Profile-guided compilation � DARPA proposal• Back projection acceleration with FPGAs• IUROPs

! Integrated Sensors• RTExpress - parallel MATLAB

! MathWorks• Leadership role in Solutionware development• Object-oriented MATLAB training

! Future partners• EMC - storage array proposal to the NSF-MRI program

! Clinomics BioSciences• Microarray provider• Joint SBIR submissions

! Mercury Computer• Profile-guided compilation � DARPA proposal• Back projection acceleration with FPGAs• IUROPs

! Integrated Sensors• RTExpress - parallel MATLAB

! MathWorks• Leadership role in Solutionware development• Object-oriented MATLAB training

! Future partners• EMC - storage array proposal to the NSF-MRI program

R3 Education PlanR3 Education Plan

! Year 2• Industrial/Research Meeting featured a

workshop on Computer-aided Medical Image Analysis

• RTExpress courses on-site to support the CenSSIS computing community

! Year 3• Development of CenSSIS Software Engineering

class under development, centered around OO MATLAB and C++

• Online tutorial for CenSSIS database users will be available in 2Q02

• Short course on MATLAB parallelizationstrategies will be provided at next industry meeting

! Year 2• Industrial/Research Meeting featured a

workshop on Computer-aided Medical Image Analysis

• RTExpress courses on-site to support the CenSSIS computing community

! Year 3• Development of CenSSIS Software Engineering

class under development, centered around OO MATLAB and C++

• Online tutorial for CenSSIS database users will be available in 2Q02

• Short course on MATLAB parallelizationstrategies will be provided at next industry meeting

CenSSIS

R3 Research Thrust SummaryR3 Research Thrust Summary

! Providing both SSI-related computing research expertise and supporting CenSSIS infrastructure needs

! Addressing key research barriers in computational efficiency, embedded computing and image/sensor data management

! Will play a key role in laying the foundation for IPLUS products• Subsurface Toolboxes• Image/Sensor Data Databases• Parallel Computing Solutions

! Providing both SSI-related computing research expertise and supporting CenSSIS infrastructure needs

! Addressing key research barriers in computational efficiency, embedded computing and image/sensor data management

! Will play a key role in laying the foundation for IPLUS products• Subsurface Toolboxes• Image/Sensor Data Databases• Parallel Computing Solutions

Center for Subsurface Sensing Imaging Systems Overview of … · Center for Subsurface Sensing &...

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