s p a z i o a finmeccanica company imas-d project 1 imas-d project overview - quadrics ltd. imas-d...

S P A Z I O A Finmeccanica Company

IMAS-D Project 1

IMAS-D Project Overview - Quadrics Ltd.

IMAS-DImage Management System

Demonstrator

Project Overview(extracts from presentations given at the official project meetings)

M. Verola

[email protected]


IMAS-D Project 2


Anatomy of the Project


IMAS-D Project 3


Application Scenario

Remote Sensing (RS) data and related products have proven

they can be exploited not only in scientific and academic

communities, but also in governmental institutions, in national

defense bodies and in commercial enterprises, thanks to the

continuous increasing of spatial and spectral data resolution. In

particular SAR, panchromatic and hyperspectral sensors are

becoming more and more a source of information of

paramount importance for a variety of critical applications such

as environmental protection, urban and regional planning and

monitoring, agricultural census, military tactical and strategic

reconnaissance and surveillance.



IMAS-D Project 4


Need for HPC

As higher resolution ( 1 meter) data become available and

the provision rate of data increases, due to the availability of

satellite constellation with high revisit frequency, an

enormous amount of data will have to be processed and

stored. The most effective way to face to this huge amount

of information is to manage them by using high performance

computing facilities and appropriate parallel algorithms.



IMAS-D Project 5


Project ObjectivesMain Goal: validate and accelerate the application of High Performance

Computing (HPC) to command, control, communication and intelligence systems, in particular in the field of Image Management and Processing systems.

Technical Objective: realize an Imagery Management and Processing System able to demonstrate its relevance by means of:

- the provision of advanced image processing functionality, and, in particular, of parallel algorithms

- the management (storage, retrieval, exploitation) of imagery data, linearly scalable to a potential unlimited size without performance degradation

- the integration of a multi-tier software system based on both commercial packages and specialized newly developed modules, where a powerful parallel processing engine, a state-of-the-art image visualization environment, an innovative DBMS and a Web-based product dissemination gateway cooperate together for building up an end-to-end RS data management system



IMAS-D Project 6


Project ExploitationThe main benefit of combining the power of cluster computing with the advanced functionality of state-of-the-art commercial software packages is the opportunity for the end user to greatly expand the set of problems that can be solved, both in terms of computational domain size than in terms of the quality of the implemented algorithms and techniques.

The prospected product resulting from the evolution of IMAS-D will match the ever increasing computing power and memory resource demands that new-generation RS sensors, especially in the hyperspectral field, will require for an effective exploitation of the produced raw data.



IMAS-D Project 7


Technical Risks"The challenge of the IMAS-D project is to create a framework

where stable commercial components live together with innovative components, thus several technical risks are implicit in the design of the system."

• SW integration complexity

• HPC system using DBMS services

• HPC system providing multiuser access via Web

• Bulky data management (DBMS, RAM requirements)

• Synchronization and cooperation of different programming models and environments (OO programming, ODBMS, message passing, Web technologies, Java)

• Effective deployment of new SW environments (ENVI+IDL, FastObjects)



IMAS-D Project 8


System Architecture


IMAS-D Project 9


System Architecture (1)

The system architecture has been designed in order to meet the following goals:

•fast and scalable processing performance

•application-oriented DBMS services, optimized for handling bulky data

•user-friendly and reliable end-to-end operations

The final result is an integrated HW and SW solution, built around a Quadrics Linux Cluster and organized into three major subsystems:

•Image Processing Subsystem

•Storage Management Subsystem

•Collection and Dissemination Subsystem

System Architecture


IMAS-D Project 10


System Architecture (2)The Image Processing Subsystem extends the single-node computing capabilities of ENVI (by Research System), the most advanced remote sensing software available on the market, to the scalable cluster computing solution. The ENVI GUI has been enriched with hooks and handles to activate parallel functions, which will dramatically speed-up the image processing tasks.

Furthermore the Storage Management Subsystem integrates the innovative object-oriented DBMS, FastObjects (by Poet Software), into the input/output ENVI menu, providing sophisticated imagery data query and management functions.

The Collection and Dissemination Subsystem makes IMAS-D a real end-to-end system, suitable for the automatic collection and archiving of incoming satellite images and for delivering via Web the processed products.

System Architecture


IMAS-D Project 11


HW Architecture

RAID StorageSCSI

QsNet links

Ethernet Hub

Dell 2 PIII node 0

Dell 2 PIII node 1

Dell 2 PIII node 2

Dell 2 PIII node 3

Dell 2 PIII node 4

Dell 2 PIII node 5

Dell 2 PIII node 6

Dell 2 PIII node 7

QsNet Switch

Intranet

User PC

User PC

Color Printer

Firewall

Internet

System Architecture


IMAS-D Project 12


Real HW Picture

Elite Switch

RAID StorageTerminal

Concentrator

Ethernet Hub

Quadrics Linux

Cluster

Another QLC ! (-based)

Dual PIII computing nodes

System Architecture

IMAS-D System


IMAS-D Project 13


System Operation Definition

FastObjects

OODBMS API

Lev. 1RAW DATA

in ENVI format

Lev. 2PRE-PROCESSED

DATA

Lev. 3PROCESSED

DATA

OFF

IMASD - GUI

HPC

Data

C

ollecti

on

Pre

-pro

cessin

g

Pro

cessin

g

RAWIMAGERY

DATAstandard sequentialimage processing

IMASD-SHAREDMEMORY LIBRARY

SHAREDMEMORY

C + MPI

Proc 1 Proc 2 Proc N

ON

StandardDissemination

Offline products delivered via traditional channels

HPCON

InternetDissemination WEB Site

System Architecture


IMAS-D Project 14


DELL LIB(based on

Linux shmem)

IMASD-DB Client Classes

DBAPI LIB(based on JNI)

Intranet

Local Web Client

IMAS-D GUI

SW Actors

IDL LIB

ENVI LIB

ENVI GUI

Library

Executable

Java Appl.

Java Archive

IDL Library

Proj. Partner

3rd Party SW

Quadrics

Legenda

IDL LIB

ENVI LIB

IMAS-D DB Server

Image Transfer Server

Fast Objects Classes

IMAS-D DB Server Classes

Fast Objects Server

IMAS-D DB

Imagery Data

and Metadata

Internet

Remote Web Client

FirewallIMAS-D

WebServer

IQLC LIB(based on

Quadrics MPI)

AIPB Server(parallel proc.

server)

System Architecture


IMAS-D Project 15


Data Flow between SW Modules DELL

Data Exchange Local Library

PGIVParallel GUI for Image

Visualization

AIPBAdvanced Image

Processing Broker

IQLCImagery Qualified Library for Cluster

ODBMS APIC/Java API for FastObjects

WEB SERVERDissemination

Gateway

AADCAdvanced Automatic

Data Collection

proc. request +image data +ancillary data

processed image +response info

processed image data blocks

image data blocks + ancillary data

insert/query command

search statistics + retrieved image

raw image data

START / STOP

START /STOP

proc. request +image data +ancillary data

processed image +response info

query request

retrieved images

System Architecture


IMAS-D Project 16


SW Module Dependencies

WEB SERVERDissemination Gateway

Responsibilities-- provide Web services for product dissemination

Data Base

Parallel Processing

Visualization

Web Server

RT Data Collection

ODBMS APIC/Java API for FastObjects

Responsibilities-- API for accessing IMASD imagery DB

IQLCImagery Qualified Library for Cluster

Responsibilities-- provide parallel image processing algorithms

AIPBAdvanced Image Processing Broker

Responsibilities-- handle requests to execute parallel image processing algorithms

DELLData Exchange Local Library

Responsibilities-- handle the communication between the sequential interactive IMASD GUI (PGIV) and the parallel processing engine

PGIVParallel GUI for Image Visualization

Responsibilities-- extend the functionality and performance of ENVI GUI with hooks and handles to parallel processing functions

AADCAdvanced Automatic Data Collection

Responsibilities-- simulate the automatic data collection procedure-- provide a communication library for transferring imagery data files

System Architecture


IMAS-D Project 17


Parallel ProcessingInteraction Diagram

[7] return data subset result

sendimage subset [4]

ENVI+IDL

library

ENVI+IDL

library

ENVI widgets

IDLsequential

code

shared mem C communication

library[10] visualize results

activateprocessing

[1]

<------------------------- master computing node --------------------------> <-- slave computing nodes -->

[9]receive

response

sendrequest

[2]

IDL to C interface

DELL communication library

System Architecture

C+MPIparallel

programming<master node>[8]

sendresponse

receiverequest

[3]

[7] return data subset result

[4] sendimage subset

MPI library

C+MPIparallel

programming<slave node>

C+MPIparallel

programming<slave node>

C to IDL interfaces

[6]return

call[5]

[5]call

return[6]


IMAS-D Project 18


Development tools

Visualization and processing

Linux Red Hat Linux distribution, installed on QLC ENVI GUI + library for remote sensing applicationsIDL interpreted programming languages for

image processingC/C++ compiler compilers for sequential/parallel C/C++

programsMPI Message Passing Interface library, optimized

for QsNetmake support tool for SW compilationCVS Concurrent Version System, used for SW

development

System Architecture


IMAS-D Project 19


Performance Results


IMAS-D Project 20


WP6 Progress Status

Image name DC_1M_LARGE300Dimensions 7849 x 3107 x 1Image In Size 24 MBImage Out Size 48 MB

Total Computing Elapsed Time Parallel Computing TimeCPUs CPUs (s) (s)

2 1 381 376,573 2 195 189,974 3 132 127,758 7 62 56,43

16 15 47 41,61

Adaptive Filter - Frost 5x5

0

100

200

300

400

500

0 2 4 6 8 10 12 14 16

Number of Computing CPUs

Tim

e (

s)

ElapsedTime

ParallelComputingTime

Parallel Performance


IMAS-D Project 21


WP6 Progress Status


2 1 187 178,133 2 100 90,514 3 72 63,398 7 38 28,66

16 15 35 23,65


Unsupervised Classification(5 classes, 5 Iterations)

0

50

100

150

200

0 2 4 6 8 10 12 14 16


Tim

e (

s)

ElapsedTime




IMAS-D Project 22


WP6 Progress Status


2 1 41 39,733 2 22 20,64 3 15 14,228 7 8 7,09

16 15 6 5,06

Image name rome_tmDimensions 1893 x 1825 x 7Image In Size 24 MBImage Out Size 3,5 MB

Maximum Likelihood Classification(5 classes)

0

10

20

30

40

50

0 2 4 6 8 10 12 14 16


Tim

e (

s)

ElapsedTime




IMAS-D Project 23


WP6 Progress Status


2 1 13 13,523 2 8 7,874 3 6 5,88 7 5 3,9

Image name rome_tmDimensions 1893 x 1825 x 7Image In Size 24 MBImage Out Size 24 MB

Principal Component Rotation

0

5

10

15

0 2 4 6 8


Tim

e (

s)

ElapsedTime




IMAS-D Project 24


WP6 Progress Status

Fast Fourier Transform

0

10

20

30

40

50

60

0 2 4 6 8 10 12 14 16


Tim

e (

s)

ElapsedTime




2 2 54 47,623 3 40 33,934 4 33 26,716 6 26 19,358 8 23 17,01

16 16 19 12,51



IMAS-D Project 25


Demo Session


IMAS-D Project 26


IMASD


IMAS-D Project 27


Main MenuMain Menu

Tools for IMASD Cluster Management

Highlights operationsrelated to the IMASD Cluster


IMAS-D Project 28


Overview of IMASD ToolsOverview of IMASD Tools

Data Collection &Pre-Processing

Tools

Interactive use of Processing functionalities

Cluster Management

Data Base Interface

Help


IMAS-D Project 29


Cluster ManagementCluster Management

•Select the number of CPUs to be allocated for parallel computing

•Start and stop the parallel computing server

•Show CPUs allocation

•Check the Cluster status


IMAS-D Project 30


Cluster Control Cluster StatusCluster Control Cluster Status

•Reserve a set of CPUs to be used for parallel computing

•Graphical display of CPUs status

•Check the status of the Cluster partitions

Running 8 CPUsRunning 16 CPUs

•Start the parallel server

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