Center for Subsurface Sensing & Imaging Systems Overview of Research Thrust R3 R3 Fundamental...

Center forSubsurface Sensing & Imaging Systems


Overview of Research Thrust R3

R3 Fundamental Research Topics

R3A Parallel Processing

• Middleware/Parallelization Tools

• FPGA Acceleration

R3B Solutionware Development

• Subsurface Toolboxes

• Image and Sensor Data Databases

R3 Fundamental Research Topics

R3A Parallel Processing

• Middleware/Parallelization Tools

• FPGA Acceleration

R3B Solutionware Development

• Subsurface Toolboxes


David Kaeli - NUMiriam Leeser - NU

Wilson Rivera - UPRM

David Kaeli - NUMiriam Leeser - NU

Wilson Rivera - UPRM

R1

R2

Overview of the Strategic Research PlanOverview of the Strategic Research Plan

FundamentalScienceFundamentalScience

ValidatingTestBEDsValidatingTestBEDs

L1L1

L2L2

L3L3

R3Image and Data

InformationManagement

S1 S4 S5S3S2

Bio-Med Enviro-Civil

CenSSIS Barriers Addressed by R3 ProjectsCenSSIS Barriers Addressed by R3 Projects

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

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

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

Barrier 4 Barrier 4



Middleware andParallelization Tools

David Kaeli – NU Wilson Rivera – UPRM

Carmen Carvajal - UPRMMagda El-Shenawee - U Arkansas

Geoff Krapf – NU (undergrad)Waleed Meleis – NU

Craig Shaffer – NU (undergrad)Karen Tompko – U Cincinnati

Yijian Wang - NUJuemin Zhang - NU

David Kaeli – NU Wilson Rivera – UPRM

Carmen Carvajal - UPRMMagda El-Shenawee - U Arkansas

Geoff Krapf – NU (undergrad)Waleed Meleis – NU

Craig Shaffer – NU (undergrad)Karen Tompko – U Cincinnati

Yijian Wang - NUJuemin Zhang - NU

CenSSIS Middleware Tools CenSSIS Middleware Tools

Parallelization of MATLAB, C/C++ and Fortran codes using Message Passing Interface (MPI) – a software pathway to exploiting GRID-level resources

Presently utilizing local Beowulf clusters, NCSA resources (Mariner Center at BU) and Internet-2

Profile-guided program instrumentation/optimization Utilizing MPI-2 to address barriers in I/O performance Building on existing Grid Middleware such as Globus

Toolkit, MPICH-G2 and GridPort

Parallelization of MATLAB, C/C++ and Fortran codes using Message Passing Interface (MPI) – a software pathway to exploiting GRID-level resources

Presently utilizing local Beowulf clusters, NCSA resources (Mariner Center at BU) and Internet-2

Profile-guided program instrumentation/optimization Utilizing MPI-2 to address barriers in I/O performance Building on existing Grid Middleware such as Globus

Toolkit, MPICH-G2 and GridPort

MATLAB

C/C++

Fortran

Parallelization

MPI

MPICH-G2

UPC

Impact on CenSSIS ApplicationsImpact on CenSSIS 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 Silicon Graphics Origin 2000• Matlab-to-C compliation• Hot-path parallelization

• Obtained superlinear speedup of Ellipsoid Algorithm run on a 16-node IBM

Super-Parallel (SP2) system• Matlab-to-C compliation• Hot-path parallelization

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 Silicon Graphics Origin 2000• Matlab-to-C compliation• Hot-path parallelization

• Obtained superlinear speedup of Ellipsoid Algorithm run on a 16-node IBM

Super-Parallel (SP2) system• Matlab-to-C compliation• Hot-path parallelization

Soil

Air

Mine

Scattered Light Simulation Speedup

1

10

100

1000

10000

100000

Ru

nti

me

in s

ec

on

ds

Original

Matlab-to-C

Hot pathparallelization

Ellipsoid Algorithm Speedup(versus serial C version)

05

101520

1 2 4 8 16

Number of Nodes

Sp

ee

du

p

64-vector 256-vector1024-vector linear speedup

Techniques for Parallelizing MATLABTechniques for Parallelizing MATLAB

Manage completely independent MATLAB processes distributed over different processors

Message passing within MATLAB (e.g., MultiMATLAB)

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

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

Multiple MATLAB sessions

A Single MATLAB session

Matlab Code C Code Parallel CodeMatlab C compiler Use MPI

Our Approach

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

Eliminating I/O Barriers in ParallelSubsurface ApplicationsEliminating I/O Barriers in ParallelSubsurface Applications

Many SSI applications tend to be file bound or memory bound (or both)

While we can use MPI to parallelize processing and use MPI collective-I/O to accelerate I/O, we still are limited to accessing a file on a single disk

Our present work looks at parallelizing I/O by partitioning files associated with MPI processes

We attempt to utilize, slower and commodity (IDE) local secondary storage

Many SSI applications tend to be file bound or memory bound (or both)

While we can use MPI to parallelize processing and use MPI collective-I/O to accelerate I/O, we still are limited to accessing a file on a single disk

Our present work looks at parallelizing I/O by partitioning files associated with MPI processes

We attempt to utilize, slower and commodity (IDE) local secondary storage

Eliminating I/O Barriers in ParallelSSI ApplicationsEliminating I/O Barriers in ParallelSSI Applications

0

500

1000

1500

2000

2500

3000

3500

NPB/BT SPEChpc MPI-Tile Perf MandelbrotT

ota

l E

xecu

tio

n T

ime

(sec

on

ds)

MPI-IO

Partitioned

Parallelize computation using MPI

Profile chunk access frequencies and temporal access patterns on a per process basis

Use profile to guide partitioning to reduce overall execution time by 27%-82%

Presently targeting both file-bound and memory-bound applications

Some Recent PublicationsSome Recent 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, Special issue of the SCS Journal, 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, August 2002.

“An efficient parallel algorithm for solving unsteady nonlinear equations”, W. Rivera, J. Zhu, and D. Huddleston, Proc. International Conference on Parallel Processing, IEEE Computer Society, 2002.

“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, Submitted to ICS’03.

“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, Special issue of the SCS Journal, 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, August 2002.

“An efficient parallel algorithm for solving unsteady nonlinear equations”, W. Rivera, J. Zhu, and D. Huddleston, Proc. International Conference on Parallel Processing, IEEE Computer Society, 2002.

“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, Submitted to ICS’03.

Grid ComputingGrid Computing

Solving Subsurfacing Barriers using Grid Computing Deployment of distributed CenSSIS

applications Development of adaptive middleware

Profile-guided parallelization/optimization

Multi-language support

Profile-guided I/O partitioning

Interaction with distributed image database resources

CenSSIS/HP Industrial Relations Strong links with Latin American Universities interested

in Grid Computing Student and/or faculty interchange program with

CenSSIS schools Leadership in leading IEEE/ACM GRID Computing

Workshops

Solving Subsurfacing Barriers using Grid Computing Deployment of distributed CenSSIS

applications Development of adaptive middleware

Profile-guided parallelization/optimization

Multi-language support

Profile-guided I/O partitioning

Interaction with distributed image database resources

CenSSIS/HP Industrial Relations Strong links with Latin American Universities interested

in Grid Computing Student and/or faculty interchange program with

CenSSIS schools Leadership in leading IEEE/ACM GRID Computing

Workshops

SVDA = 100 X 20,

special functions

A\b A = 10002; dense

A\bA = (106)2; sparse

Operation

low

High

250 hours

20-50

yes

FDFDDifference Eq

high

medium

150 minutes

8

yes

SDFMMIntegral Eq

N. A.Points per wavelength

mediumCoding complexity

medium

10 minutes

smooth boundary

SAMMModal Expansion

Computational storage

Computational speed

Any shape target

FEATURE

GRID Resources are needed for key CenSSIS modeling applicationsGRID Resources are needed for key CenSSIS modeling applications

computable on parallel systemstargeted for GRID systems

Grid Computing: Experimental Grid @ UPRMGrid Computing: Experimental Grid @ UPRM

ΧΧ

Sensors

Campus Backbone

Internet 2

IA64 Cluster

Storage

Application Layer

Middleware Layer

Common Infrastruc. Layer

Resource Layer

Grid Community Model

Grid Computing: Pattern CategorizationGrid Computing: Pattern Categorization

generatecombinations

generatecovariance matrixfrom the spectral

image

Load spectralimage

calculate eigenvalues and eigenvectors from the

covariance matrix

using main eigenvector get initialmeans for the

C-Means

C-meansclassifier usingonly one band

Calculatecovariance matrix

average for allimage

combinations

Selectcombination with

the largestaverage distancebetween classes

Model of An Intrusion/ Attack

Node b

Node h

Node g

Node e Node d

Node a

Node f

Node c

Tab,

Tae, Tea

Tce, Tec

TebTbd

Tdg

TegTef

Thg

T fh

Source of A ttack

Node Under Attack

Hyperspectral Images

Computational methods for ensembles of nonparametric supervised classifiers

Feedback algorithm Parallelization (Matlab to MPI/C++)

Intrusion Detection & Countermeasure design problems

Grid Computing: LATAM Task ForceGrid Computing: LATAM Task Force

Create a LATAM Task Force on Grid Computing.

Universidad de Chile, ChileRicardo Baeza, PhD in Computer Science, University of Waterloo

Universidad de los Andes, VenezuelaHerbert Hoeger, PhD in Computer Science, University of Iowa

Universidad de Sau Paulo, BrasilMarcio Lobo, PhD in Computer Science, TUD, Germany

Instituto Tecnologico de Monterrey, MexicoCesar Vargas, PhD in Electrical Engineering, Louisiana State University

Universidad del Valle, ColombiaAngel Garcia, PhD in Telecommunications, UPV-Spain.

Hold a Grid Workshop for these researchers/educators at UPRM, and invite both CenSSIS and HP people to serve as reviewers and panelists (slated for Nov. 2003).

Provide tutorials and short courses on Grid-level computing.

We will utilize CenSSIS problems as the motivating examples that will be parallelized. Implementations will be prototyped at UPRM, NU and BU.

Create a LATAM Task Force on Grid Computing.

Universidad de Chile, ChileRicardo Baeza, PhD in Computer Science, University of Waterloo

Universidad de los Andes, VenezuelaHerbert Hoeger, PhD in Computer Science, University of Iowa

Universidad de Sau Paulo, BrasilMarcio Lobo, PhD in Computer Science, TUD, Germany

Instituto Tecnologico de Monterrey, MexicoCesar Vargas, PhD in Electrical Engineering, Louisiana State University

Universidad del Valle, ColombiaAngel Garcia, PhD in Telecommunications, UPV-Spain.

Hold a Grid Workshop for these researchers/educators at UPRM, and invite both CenSSIS and HP people to serve as reviewers and panelists (slated for Nov. 2003).

Provide tutorials and short courses on Grid-level computing.

We will utilize CenSSIS problems as the motivating examples that will be parallelized. Implementations will be prototyped at UPRM, NU and BU.



Field Programmable Gate Arrays For

Subsurface Imaging

Miriam Leeser – NUWang Chen - NUSrdjan Coric - NUShawn Miller - NU

Seth Molloy – NU (undergrad) Josh Noseworthy – NU (undergrad)

Haiqian Yu - NU

Miriam Leeser – NUWang Chen - NUSrdjan Coric - NUShawn Miller - NU

Seth Molloy – NU (undergrad) Josh Noseworthy – NU (undergrad)

Haiqian Yu - NU

Field Programmable Gate Arrays for Subsurface ImagingField Programmable Gate Arrays for Subsurface Imaging

Backprojection for Computed Tomography image reconstruction Sponsored by Mercury Computer

Accelerating Finite Difference Time Domain (FDTD) in hardware Collaboration with Carey Rappaport, NU

Retinal Vascular Tracing in real time Collaboration with Badri Roysam and Chuck Stewart, RPI

Diverse problems, similar solutions:

FPGAs are particularly well suited for accelerating image processing algorithms

Backprojection for Computed Tomography image reconstruction Sponsored by Mercury Computer

Accelerating Finite Difference Time Domain (FDTD) in hardware Collaboration with Carey Rappaport, NU

Retinal Vascular Tracing in real time Collaboration with Badri Roysam and Chuck Stewart, RPI

Diverse problems, similar solutions:

FPGAs are particularly well suited for accelerating image processing algorithms

BackprojectionBackprojection

Backprojection algorithm used in medical imaging Traditionally performed by custom hardware

Application specific integrated circuits and/or custom board designs

New systems require greater flexibility Algorithms under development for 3D

reconstruction Application specific integrated circuits viewed as costly both in time and NRE

FPGA implementation offers significant advantages Algorithm flexibility and re-use

Fixed point and quantization effects matter Difference between fixed and floating point must

be small

Backprojection algorithm used in medical imaging Traditionally performed by custom hardware

Application specific integrated circuits and/or custom board designs

New systems require greater flexibility Algorithms under development for 3D

reconstruction Application specific integrated circuits viewed as costly both in time and NRE

FPGA implementation offers significant advantages Algorithm flexibility and re-use

Fixed point and quantization effects matter Difference between fixed and floating point must

be small

Projection Parallelism for PerformanceProjection Parallelism for Performance

Projections

Imagecolumns

Data dependency for backprojection processing

Imagerows

ProjectionsImage

columns

Parallelism implemented in FireBird (Max 16-way parallel)

Imagerows

ProjectionsImage

columns

1024 projections x 1024 samples/projection Each used to reconstruct a 512 x 512 image

Backprojection Speedup Due to Parallelism - Expandable to n-way parallelBackprojection Speedup Due to Parallelism - Expandable to n-way parallel

SU

B

ROUND

ROUND

ROUND

SU

BS

UB

LUT 3.1

LUT 3.2

LUT 2.1

LUT 2.2

LUT 1.1

LUT 1.2

LUT 4.1

LUT 4.2

EVENWRITE

COUNTERODD

WRITECOUNTER

ODDRAMODD

RAM

EVENRAMEVEN

RAM

ODDRAMODD

RAM

EVENRAMEVEN

RAM

ODDRAMODD

RAM

EVENRAMEVEN

RAM

ODDRAMODD

RAM

EVENRAMEVEN

RAM

SU

B

SU

BS

UB

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BS

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UL

TM

UL

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UL

TM

UL

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AD

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PROJECTIONCOUNTER

MU

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LUT 1.3

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DLUT 2.3

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LUT 3.3

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LOCALRAM

15

16

2525

2525

25

17

5 4

9

9

10

9

9

10

14

1315

16

17

25

25

9

9

8

LEFTMEZZANINE

RAM

RIGHTMEZZANINE

RAM

4 9

4 9

Quality of Results are HighQuality of Results are High

Software reconstruction(Floating Point)

Hardware reconstruction(Fixed Point)

Relative Error

Sinogram quantization: 9 bits

Interpolation factor: 3 bits

Relative Error: 0.001295%

FPGA Hardware Provides 100x Speedup Over Software on 1GHz PentiumFPGA Hardware Provides 100x Speedup Over Software on 1GHz Pentium

A: Software - Floating point - 450 MHz Pentium : ~ 240 sB: Software - Floating point - 1 GHz Dual Pentium : ~ 94 sC: Software - Fixed point - 450 MHz Pentium : ~ 50 sD: Software - Fixed point - 1 GHz Dual Pentium : ~ 28 sE: Hardware (Wildstar, simple) - 50 MHz : ~ 5.4 sF: Hardware (Wildstar, 4-way) - 50 MHz : ~ 1.3 sG: Hardware (Firebird, 8-way) - 65 MHz : ~ 0.5sH: Hardware (Firebird, 16-way) - 65 MHz : ~ 0.25s

0

50

100

150

200

250

A B C D E F G H

Parameters: 1024 projections

1024 samples per projection

512 x 512 pixels image

9-bit sinogram data

3-bit interpolation factor

FDTD Equations Discretize Maxwell’s Equations – GPR ModelingFDTD Equations Discretize Maxwell’s Equations – GPR Modeling

• Update each space cell's electric and magnetic field by using previous values of this cell and its neighbors cells around it

• Extremely computationally expensive• Benefits from hardware acceleration

3-D Buried Object Detection Forward Model3-D Buried Object Detection Forward Model

Mine X

Y

Z

AntennaReceiver

Simplify

The Quantized Fixed-point SimulationThe Quantized Fixed-point Simulation

Detailed Architecture of 2-D FDTD Implementation (BlockRam interface and Pipeline updates for one time step)Detailed Architecture of 2-D FDTD Implementation (BlockRam interface and Pipeline updates for one time step)

FPGA CHIP

DESIGN

Ele

ctri

cal

Fie

ldM

odule

Pip

elined

Magneti

cFie

ldM

odule

Pip

elined

ME

MO

RY

ME

MO

RY

BlockRAM

1EYS HZSHXS

2EYS HZSHXS

3EYS HZSHXS

0EYS HZSHXS

1EYS HZSHXS

2EYS HZSHXS

3EYS HZSHXS

0EYS HZSHXS

1EYS HZSHXS

2EYS HZSHXS

3EYS HZSHXS

0EYS HZSHXS

1EYS HZSHXS

2EYS HZSHXS

3EYS HZSHXS

0EYS HZSHXS

1EYS HZSHXS

2EYS HZSHXS

3EYS HZSHXS

0EYS HZSHXS

1EYS HZSHXS

2EYS HZSHXS

3EYS HZSHXS

0EYS HZSHXS

1EYS HZSHXS

2EYS HZSHXS

3EYS HZSHXS

0EYS HZSHXS

1EYS HZSHXS

2EYS HZSHXS

3EYS HZSHXS

0EYS HZSHXS

D D

CC

B

B

A A

Result

BlockRAM

Bo

un

da

ry

Vertical BoundaryHorizontalBoundary

Retinal Vascular Tracing: Register 2-D Image to 3-D in Real TimeRetinal Vascular Tracing: Register 2-D Image to 3-D in Real Time

Feature extraction Registration: image pairs Registration: montages Registration: real-time /

on-line

Software is too slow Use FPGAs to accelerate

to video frame rate Image guided surgery

Feature extraction Registration: image pairs Registration: montages Registration: real-time /

on-line

Software is too slow Use FPGAs to accelerate

to video frame rate Image guided surgery

Retinal Vascular Tracing: Register 2-D Image to 3-D in Real TimeRetinal Vascular Tracing: Register 2-D Image to 3-D in Real Time

FIREBIRD BOARD

HOST

Direction ofblood vessel

PCI BUS

ObjectiveTo accelerate an existing retinalvascular tracing (RVT) algorithm byimplementing computation of templateresponses in reconfigurable hardware

FPGA

BL

OC

KR

AM

DESIGN

MEMORY0

IMAGEMEMORY1

RESULTS

“Smart Camera”

Direction of blood vessel

PCI BUS

Developing Embedded SolutionwareDeveloping Embedded Solutionware

* All Three Projects Use Same Reconfigurable Hardware, Same Design Flow

* Result is Considerable Processing Speedup, Moving Processing Closer to Sensors

Firebird PCI board fromAnnapolis Microsystems



Solutionware Development• Subsurface Toolboxes


Solutionware Development• Subsurface Toolboxes

• Image and Sensor Data DatabasesDavid Kaeli – NU

Chuck Stewart – RPIEmmanuel Arzuaga – UPRM

Jennifer Black – NUKyle Guilbert – NU (undergrad)

Matthew Kowalski – NU (undergrad)Chakib Ouarraoui – NU

Amitha Perera - RPIBecky Norum – NU

Derek Uluski – NU (undergrad)

David Kaeli – NUChuck Stewart – RPI

Emmanuel Arzuaga – UPRMJennifer Black – NU

Kyle Guilbert – NU (undergrad)Matthew Kowalski – NU (undergrad)

Chakib Ouarraoui – NUAmitha Perera - RPIBecky Norum – NU

Derek Uluski – NU (undergrad)

CenSSIS Solutionware – UPRM/NU/RPICenSSIS Solutionware – UPRM/NU/RPI

Toolbox Development Support the development of CenSSIS Solutionware that demonstrates

our “Diverse Problems – Similar Solutions” model Delivered a software-engineered Multi-View Tomography Toolbox,

developed in OOMATLAB Developing three new CenSSIS Toolboxes

Registration – RPI/WHOI Hyperspectral Imaging – UPRM 3-D Modeling - NEU

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 Delivered a software-engineered Multi-View Tomography Toolbox,

developed in OOMATLAB Developing three new CenSSIS Toolboxes

Registration – RPI/WHOI Hyperspectral Imaging – UPRM 3-D Modeling - NEU

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

Current/Future Toolbox DevelopmentCurrent/Future Toolbox Development

Development of multi-language toolboxes – C, Fortran, C++, Java, MATLAB and OO-MATLAB

Delivered the MVT Toolbox – open source Presently working on three additional toolbox efforts Developing a parallelized version of the MVT Toolbox Adopted Software Engineering Institute Capability Maturing

Model (CMM) Level 3 standards Software library and bug tracking being developed (CVS and

Bugzilla) Software Engineers on staff at NU, UPRM and RPI

Development of multi-language toolboxes – C, Fortran, C++, Java, MATLAB and OO-MATLAB

Delivered the MVT Toolbox – open source Presently working on three additional toolbox efforts Developing a parallelized version of the MVT Toolbox Adopted Software Engineering Institute Capability Maturing

Model (CMM) Level 3 standards Software library and bug tracking being developed (CVS and

Bugzilla) Software Engineers on staff at NU, UPRM and RPI

Matlab 6

MVT MSD LPM Modeling

Detectors

Sources

Object

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