MAPLD 98 - Poster PCD

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Making a Case for Distributed Adaptive Computing
in Remote Sensing Science Data Processing
Clay Gloster Department of Electrical Computer
Engineering NC State University, Raleigh,
NC email gloster_at_eos.ncsu.edu Marco
Figueiredo marco_at_fpga.gsfc.nasa.gov SGT Inc./NASA
Goddard Space Flight Center Greenbelt, MD, USA
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Outline

• Problem Statement
• Potential Solutions
• Application Image Classification
• An Overview of Adaptive Computing
• Research Goals/ Vision
• Summer 97/98 Accomplishments
• Experimental Results
• Conclusions Future Work

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Generic Challenge Scientific Data Processing

• Problem Statement
• Given an application that requires an excessive
  number of scientific computations
• Design a system that can perform these
  computations with the following constraints.
• The system must provide improved performance over
  current state of the art.
• System cost cannot be excessive!!
• The system must be flexible and easy to adapt to
  new applications.
• The system development time must be small.
• Technology developed during system development
  must be easily transferred to many potential users

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NASA Challenge Remote Sensing Science Data
Processing

• Large data input/output requirements (Data
  Intensive)
• The MODIS instrument, to be launched on the
  EOS-AM1 satellite, has average daily data volumes
  of 530MB.
• Large data processing requirements (Compute
  Intensive)
• The MODIS instrument, to be launched on the
  EOS-AM1 satellite, has data processing
  requirement of 5.7 GFLOPs .
• Algorithms can change even after the instrument
  is in orbit
• Enhancements made to algorithms
• Errors found in algorithms
• Correction for errors introduced by instrument
  fatigue

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Potential Solutions

• General-purpose processors (Software)
• Personal Computers (400MHZ), computer
  workstations, Supercomputers, etc.
• Application-specific processors (Hardware)
• ICs designed to solve a particular problem
• Special-purpose processors (Hybrid)
• Digital Signal Processors, Math Coprocessors,
  etc.
• Adaptive Computers (A New Paradigm)
• Consists of a low cost, high performance,
  software programmable general purpose processor
  with one or more low cost, high performance
  hardware programmable coprocessors.

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Cost Analysis
Cost(X)
S (
W
)(X
)
i
i
i1, ..., 4
W
0.25
i
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Application Image Classification
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Previous Results
Adaptive Computing has been shown to provide
several orders of magnitude speedup for select
applications.
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Adaptive Computing

• With the advent of programmable hardware devices
  called Field Programmable Gate Arrays (FPGAs), we
  can now reprogram hardware.
• With todays technology, we can download a new
  function into an FPGA in time on the order of 200
  microseconds.
• Current devices also allow partial configuration
  of the device while other portions of the device
  continue to function.

Time B
Time A
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Adaptive Computing for Space Applications
Satellite with Adaptive Computer
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Relevant Features of Java Release 1.1

• Object Oriented Programming Language
• The notion of hardware objects is easily
  implemented
• Threads
• Applets
• Native Methods (interfaces Java to existing
  code.)
• Remote Method Invocation (RMI)
• The notion of relocatable hardware objects is
  easily implemented.
• Java Security (Digital Signatures, secure RMI)

JAVA is a good programming language for this
project!!!!
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Proof of Concept (Summer 1997)

• Develop an understanding of the ASDP project and
  identify where NC State could make a contribution
• Use the Java language to implement a simple
  addition program that can be executed remotely.
• Accomplishments
• Implementation of a simple addition program
  controlled from the local host in Java.
• Implementation of a simple addition program
  controlled from a remote host in Java.

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Proof of Concept (Summer 1998)

• Use the Java language to implement a new version
  of the PNN algorithm that can be executed
  remotely.
• Accomplishments
• Implementation of the Pnn Algorithm controlled
  from the local host in Java.
• Implementation of a Pnn Algorithm controlled from
  a remote host in Java.
• New implementation of Pnn Algorithm that
  processes a block of data rather than a single
  pixel or a row.

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Current Research Projects

• A generic design methodology for the
  implementation of high performance remote sensing
  scientific data processing applications that can
  drastically improve development time.
• A partial implementation of the PNN algorithm for
  image classification using floating-point units
  to evaluate the feasibility of using floating
  point as opposed to fixed point units in
  state-of-the-art reconfigurable computing
  environments.
• A distributed library of generic floating point
  arithmetic design modules that are fast, modular,
  and can easily scale.
• A distributed reconfigurable computing
  implementation of the PNN algorithm for image
  classification that improves performance by an
  order of magnitude over the software
  implementation.
• A prototype implementation of the hardware
  resource allocation system that manages
  reconfigurable computing hardware.

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A Generic Design Methodology

• The Von-Neumann Paradigm
• Given a typical microprocessor/CPU with a fixed
  architecture
• Given an application
• Todays scientists are trained to use an existing
  design methodology to map the application onto
  the given processor.
• The New Paradigm
• Given a typical reconfigurable computer with an
  adaptive architecture
• Given an application
• Develop a new design methodology for future
  scientists to be trained in.

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Pnn A Case Study

• Use Pnn and other applications to develop a
  generic design methodology
• Use Pnn and other applications to reveal the
  limitations of current reconfigurable computing
  architectures/systems
• The result of this study will be an environment
• Scientist can enter the concept in enough detail
  for an engineer to implement.
• Tasks developed as a part of the methodology will
  be evaluated for potential automation.
• Parallelism/distributed processing should be
  exploited in this environment whenever possible.

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A Feasibility Study Floating Point Units and
Reconfigurable Computing

• Develop Parameterized VHDL Models for addition,
  subtraction, multiplication, and division
• Investigate the feasibility of implementing
  modular floating point units
• Assess the current state of the art to identify
  the capacity of current FPGAs for various
  floating point units.
• Make recommendations toward migrating from fixed
  point FPGA implementations to floating point.
• Estimate the time frame when FPGAs will be able
  to support application development using floating
  point units.

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Fixed Point Versus Floating Point
Area
Floating Point
Fixed Point
Pipeline Depth/Number of Units
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Hardware Resource Allocation System

• Single Adaptive Computing Resource Allocation
  System
• Multiple Adaptive Computing Resource Allocation
  System
• Preemptive Hardware Resource Allocation System

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Single RC Resource Allocation System
I
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1
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2
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• N sites distributed over the Internet
• 1 adaptive computer resource
• M1 hardware programmable modules

S
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N
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Multiple RC Resource Allocation System
Internet
Site 1
µp
2
CP
CP
CP
2
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Site 2
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Site N
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• N sites distributed over the Internet
• m adaptive computer resources
• SMi , i 1,m, hardware programmable modules

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Experimental Results Pnn

• PNN Image Classification run on a 512x512 image,
  4 bands, 5 classes.
• Experiments run on
• A Pentium 166MHz with 64MB of memory, running
  Windows NT
• Pixel and block based versions of the algorithm
  were evaluated.
• One Block 6 rows x 512 pixels/row x 4
  bytes/pixel 12,228 bytes
• Local and remote versions of the algorithm were
  evaluated.
• In the remote experiments, client and server were
  the same machine.

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Local/Remote Image Classification

• Both the client and the server contain adaptive
  computing resources.
• Both the client and the server contain software
  (Java C) versions of the classification
  algorithm.
• Local Classification is executed on the client.
• Remote Classification is executed on the server
  via a request from the client.

NC State Univ.
gloster.cacc..ncsu.edu
NASA GSFC
classic.gsfc.nasa.gov
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Remote Image Classification (Pixel Based)
Description Avg Row Total Software
(Java) 14.83 7597.79 Software
(C) 15.79 8087.24 Hardware (Single)
4.16 2128.38 Hardware (Multiple)
4.25 2156.02
Times reported in CPU seconds
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Remote Image Classification (Block Based)
Description Avg Row Total Software (Java)
2.65 1358.91 Software (C)
3.61 1847.33 Hardware (Single) 0.35
178.217 Hardware (Multiple) 0.35 180.15
Times reported in CPU seconds
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Local Image Classification (Pixel Based)
Description Avg Row Total Software (Java)
2.57 1317.31 Software (C)
3.65 1871.36 Hardware (Single) 0.27
141.10 Hardware (Multiple) 0.14 77.45
Times reported in CPU seconds
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Local Image Classification (Block Based)
Description Avg Row Total Software (Java)
2.56 1309.65 Software (C)
3.69 1889.63 Hardware (Single) 0.28
143.05 Hardware (Multiple) 0.28 142.57
Times reported in CPU seconds
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An Interesting Result

• Using REMOTE HARDWARE is faster than LOCAL
  SOFTWARE
• Remote Single Module Classification 178.22s
• Local Java Classification 1309.65s
• What does this infer?
• One site should develop many applications using
  the proposed resource allocation system.
• Applications should be served from centers of
  excellence, i.e. Distributed Active Archive
  Centers (DAACs)
• Alternatively, applications should be
  given/licensed to users that have adaptive
  computing resources in-house.

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Conclusions/Future Work

• The Java programming language is a good language
  for this project.
• Distributed Adaptive Computing Implementations
  can provide better performance over local
  software implementations
• Floating point implementations may be feasible
  for remote sensing science data processing
  applications.
• An adaptive computing hardware resource
  allocation system can be beneficial.
• Portions of a generic design methodology can be
  automated reducing development time for
  reconfigurable computing implementations.