HPCS Productivity
Benchmarks Working Group
SSCA #3
Sensor Processing
Knowledge Formation
and Data I/O
Serial v1.0
MIT Lincoln Laboratory
January 4, 2007
999999-1
XYZ 4/8/2015
MIT Lincoln Laboratory
Outline
• Scalable Synthetic Compact Applications
• SSCA #3
– Overview
– Quick Recipe Data I/O Mode
• Implementation and Results
MIT Lincoln Laboratory
4/8/2015
Scalable Synthetic Compact
Applications Goals
•
APP SIZE/COMPLEXITY
NextGen
Apps
Building on a motivation
slide from Fred Johnson
(15 January 2004)
Full
Apps
HPCS
Compact
Apps
Micro
BMKs
Identify which dimensions that
must be examined at full
complexity and which dimensions
that can be examined at reduced
scale while providing
understanding of both full
applications today and future
applications
SYSTEM
SIZE/
COMPLEXITY
MIT Lincoln Laboratory
4/8/2015
HPCS Benchmark Spectrum
SSCA #3
Execution and
Development
Performance Indicators
Data Generator
System Bounds
1.
2. Kernel Optimal
3. Kernel Pattern
4. Kernel Matching
HPCchallenge
Benchmarks
4. Kernel
HPCS
Spanning
Set of
Kernels
Discrete
Math
…
Graph
Analysis
…
Linear
Solvers
…
Signal
Processing
…
Simulation
…
I/O
Data Generator
1. Kernel
3.
2. KernelSimulation
3. Kernel NWCHEM
4. Kernel
Data Generator
4.
Simulation
2. Kernel
NAS PB AU
1. Kernel
3. Kernel
4. Kernel
Data Generator
Existing Applications
Kernels
Emerging Applications
Global
Linpack
PTRANS
RandomAccess
1D FFT
2.
Graph
3. Kernel Analysis
2. Kernel
Future Applications
Local
DGEMM
STREAM
RandomAccess
1D FFT
1. Kernel
Current
UM2000
GAMESS
OVERFLOW
LBMHD
RFCTH
HYCOM
Near-Future
NWChem
ALEGRA
CCSM
5.
Simulation
2. Kernel
Multi-Physics
1. Kernel
3. Kernel
4. Kernel
Micro &
Commercial
CommercialApplications
Applications Kernel
Medical
MedicalImaging
Imaging Benchmarks
Astronomical
Image
Astronomical ImageProcessing
Processing
Environmental
Monitoring
Environmental Monitoring
Data Generator
1. Image Formation
2. Image Storage
3. Image Retrieval
4. Target ID
6.
Signal
Processing
Knowledge
Formation
Scalable Synthetic
Compact Applications
Mission Partner
Application
Benchmarks
Simulation
Execution
Performance
Bounds
Data Generator
Reconnaissance
Execution
Performance
Indicators
Intelligence
1. Kernel
MIT Lincoln Laboratory
4/8/2015
Outline
• The Vision
• SSCA #3
– Overview
– Quick Recipe Data I/O Mode
• Implementation and Results
MIT Lincoln Laboratory
4/8/2015
Overview
• SSCA #3 Focuses on two stages:
– Front end image processing and storage (Stage 1)
– Back end image retrieval and knowledge formation (Stage 2)
• It is representative of many areas:
– Medical imaging (e.g.: tumor growth)
Image many patients daily
Later compare images of same patient over time
– Astronomical image processing (e.g.: monitor supernovae)
Image many regions of the sky daily
Later compare images of a region over time
– Reconnaissance monitoring (e.g.: enemy movement)
Image many areas daily
Later compare images of a given region over time
MIT Lincoln Laboratory
4/8/2015
Overview
• Benchmark stresses computation, communication, and data I/O
• Can be run in 3 modes:
– System Mode: A combination of Compute & Data I/O Modes
– Compute Mode (minimized Data I/O Mode)
– Data I/O Mode (minimized Compute Mode)
• Principal performance goal is throughput
–
–
–
–
Maximize rate at which answers are generated
May overlap operation of data I/O and compute kernels
Data I/O and compute kernels may run on different systems
Some data is required to be contiguous
MIT Lincoln Laboratory
4/8/2015
SSCA #3 – System Mode
Stage 1: Front-End Sensor Processing
Kernel #1
Data Read
and Image
Formation
Scalable Data
and Template
Generator
SAR
Image
Template
Insertion
Kernel #2
Image
Storage
SAR
Image
Templates
Raw
Data
Coeffs,
Group of
Templates
Coeffs
Raw
Data
Image
Template
Positional
Indices
Coeffs
Computation
Raw
Complex
Data
Image
Pair
Community has
traditionally
focused on
Computation …
Kernel #3
Image
Retrieval
Template
Indices
Group of
Templates
Groups of
Templates
Indices,
Group of
Templates
Image Pair
Detection
Sub-Images
Data I/O
Grid of
Images
Detection
Sub-Images
Kernel #4
Detection
Templates &
Indices
Group of
Templates
Detections,
Template
Indices
Validation
… but Data I/O
performance is
increasingly
important
Stage 2: Back-End Knowledge Formation
MIT Lincoln Laboratory
4/8/2015
SSCA #3 – Compute Mode
Sensor Processing
Scalable Data
and Template
Generator
Raw
SAR
Kernel #1
Image
Formation
SAR
Image
Kernel #2
Image
Storage
SAR
Image
Templates
Templates
Raw
SAR
File
Template
Insertion
Raw
SAR
File
Groups of
Template
Files
Raw SAR
Data Files
SAR
Image
File
Template
Files
Sub-Image
Detection
Files
Groups of
Template
Files
SAR
Image
File
Template
Files
Kernel #3
Image
Retrieval
SAR
Image Pair
Image
Files
Template
Files
Detection
File
Kernel #4
Detection
Detections
Validation
Templates
Knowledge Formation
MIT Lincoln Laboratory
4/8/2015
SSCA #3: Compute Mode Challenges
Back-End
Knowledge Formation
Front-End
Sensor Processing
Scalable Data
and Template
Generator
Raw
SAR
Templates
• Scalable synthetic
data generation
Kernel #1
Image
Formation
SAR
Image
Template
Insertion
SAR
Image
Kernel #4
Detection
Detections
Validation
Templates
Templates
• Pulse compression
• Polar Interpolation
• FFT, IFFT (corner turn)
• Sequential store
• Non-sequential
retrieve
• Large & small I/O
• Large Images
difference &
Threshold
• Many small
correlations on
selected pieces
of a large image
MIT Lincoln Laboratory
4/8/2015
SSCA #3 – Data I/O Mode
Stage 1: Front-End
Kernel #1
Data Read
and Image
Formation
Scalable Data
and Template
Generator
Large
Data
Large
Data
Groups of
Small Data
Large
Complex
Data
Image
Image
Groups of
Small Data
Sub-Images
Groups of
Small Data
Group of
Small Data
Image
Pair
Kernel #3
Image
Retrieval
Kernel #2
Image
Storage
Image Pair
Grid of
Images
Sub-Images
Kernel #4
Stage 2: Back-End
MIT Lincoln Laboratory
4/8/2015
Outline
• The Vision
• SSCA #3
– Overview
– Quick Recipe Data I/O Mode
• Implementation and Results
MIT Lincoln Laboratory
4/8/2015
Ingredients
To run Data I/O Mode, the user only needs set:
1) SCALE, 2) N_SDG_GROUPS, and 3) grid
Where:
SCALE = a parameter that sets the size of raw input data, and image.
It should be set so that these are a significant fraction of a single
processor’s memory.
•
N_SDG_GROUPS = number of raw input data and templates groups. It
should be set large enough to avoid disk cache effects.
•
And the number of images in the grid is:
GRID_SIDE_SIZE x GRID_SIDE_SIZE x AV_GRID_DEPTH
GRID_SIDE_SIZE
•
GRID_SIDE_SIZE
MIT Lincoln Laboratory
4/8/2015
Ingredients
Parameters to Code:
•
PICTURE_SIZE = GRID_SIDE_SIZE2
is the number of images in a picture
•
EST_TOT_GRID_SIZE = PICTURE_SIZE x AV_GRID_DEPTH
is the total number of times that the input data will be retrieved,
and the total number of images stored to the grid
•
mc x n = is the size of the raw complex valued input data
mc = 2 x ceil(80 x SCALE)
n = 2 x ceil(158.496 x SCALE + 60)
•
ROTATION_STEP is the templates’ rotation angle increment in degrees
•
nDistinctLetters x nDistinctRotations is total number of pixelated templates
nDistinctLetters = number of least correlated letters in alphabet (21)
nDistinctRotations = num of ROTATION_STEP angles between 0 and 360 degs
•
FONT_SIZE x FONT_SIZE = size of a single template in pixels
MIT Lincoln Laboratory
4/8/2015
Ingredients
Parameters to Code (Cont.):
•
m x nx = size of an image
m = 2*ceil(mc/0.8405246)
k1n = 8.3776 x (1.5 -1/n)
kxmin = sqrt(70.1841812-6.3165469 x (m/mc)2)
kxmax = sqrt((4 x k1n.^2)-25.2661877 x (1/mc)2)
nx = 2 x ceil(20 x SCALE*(kxmax-kxmin)/pi) + 20
•
nSubImages = floor( pOccupancy x p2ndNot1st
x (m /(SARLOBE_DISTANCE x FONT_SIZE))
x (nx/(SARLOBE_DISTANCE x FONT_SIZE)) )
= number of smaller images to be stored (by the last kernel), where:
pOccupancy = 0.5
p2ndNot1st = 0.5
is the probability of template occupancy, and
is the probability that a template appear in
the second image but not in the first
Total memory required, in bytes =
N_SDG_GROUPS x (8 x mc x n + 4 x nDistinctLetters x nDistinctRotations x FONT_SIZE2)
+ EST_TOT_GRID_SIZE x (4 x m x nx + 4*nSubImages x (4 x FONT_SIZE)2)
+ (coefficients, support and verification parameters; stored once)
•
Grows with SCALE2
MIT Lincoln Laboratory
4/8/2015
Directions
SDG
•
•
Create a group
– Create a random single precision complex valued (large) mc x n matrix
– Store the data
– Create a random real valued (small) FONT_SIZE x FONT_SIZE matrix
– Store small matrix nDistinctLetters x nDistinctRotations times
Copy the above group N_SDG_GROUPS times
STAGE 1
for iImage = 1 to EST_TOT_GRID_SIZE
KERNEL 1
– Randomly pick and retrieve one of the N_SDG_GROUPS groups
– Create a random single precision real valued m x nx matrix
KERNEL 2
– Randomly select i and j values in the range [1, GRID_SIDE_SIZE] and use
these to create a filename.
– Store the image matrix
end
MIT Lincoln Laboratory
4/8/2015
Directions
STAGE 2
for iImageSeq = 1 to PICTURE_SIZE
– Randomly select i and j values in the range [1, GRID_SIDE_SIZE]
– Find the grid depth at this particular point
for k = 1 to gridPointDepth-2
KERNEL 3
– Retrieve a pair of images, and an SDG group of templates
KERNEL 4
for l = 1 to nSubImages
– Create a random (4 x FONT_SIZE) x (4 x FONT_SIZE) matrix
– Store the sub image
end
end
end
MIT Lincoln Laboratory
4/8/2015
Outline
• The Vision
• SSCA #3
– Overview
– Quick Recipe Data I/O Mode
• Implementation and Results
MIT Lincoln Laboratory
4/8/2015
SSCA #3 Serial Release v1.0
Types of Data I/O Implemented:
•
FWRITE, binary, IEEE floating point with appropriate big or littleendian byte ordering and 32-bit data type
•
HDF5, HDF5 32 bit float format
Modes:
• System Mode
•
•
–
Includes both Compute (SAR Processing), and Data I/O Modes.
Compute Mode
–
Dials the smallest possible Grid of 2 images, thus minimizing data I/O.
Data I/O Mode
–
Generates random data, thus foregoing SAR processing.
Outputs metrics at each level in the system’s hierarchy –
Kernels, Stages, and Overall SSCA #3:
–
Bytes, seconds, bandwidth (bytes/sec)
MIT Lincoln Laboratory
4/8/2015
SSCA #3 Serial Release v1.0
•
One of many possible implementations
•
Over 2200 lines of well commented MATLAB code. Carefully picked functional
breakdown, data structures, variable names, and comments
•
Coding standard: Modified “Programming in C++, Rules and
Recommendations” by Mats Henricson and Erik Nyquist of Ellemtel
Telecommunication System Laboratories, 1990-1992
•
Development tools used
– MATLAB Version 7.1.0.246 (R14) Service Pack 3 (version required)
– Octave Version 2.9.5
– Pentium® 4 2.66GHz CPU with 1.00GB of RAM, and 2.5GB of virtual RAM,
running on MS Windows XP Professional Version 2002 Service Pack 1
– On a dedicated dual processor hyperthreaded P4 Xeon, 2.8 GHz, ½ MB
cache, GNU/Linux 2.4.20-28.9 (Redhat 9)
•
Accompanying documentation:
– Written Specification, and these slides
– MANIFEST.txt – list of files with brief description
– README.txt – installation and run time instructions; code overview
– RELEASE_NOTES.txt – known outstanding issues in current release
MIT Lincoln Laboratory
4/8/2015
SSCA #3 Release v1.0a
MIT Lincoln Laboratory
4/8/2015
Summary
Challenges:
•
Large scale parallel two-dimensional (2D) Inverse Fast Fourier Transform (IFFT); may require a ‘corner turn’ or a
‘gather scatter’ (depending on architecture), with large quantities of data. Polar interpolation is known to be
even more computationally intense than IFFT (Kernel 1).
•
Streaming image data storage to a data I/O device (write) may involve large block data transfers, storing one
large image after another (Kernel 2).
•
Random location image sequence retrieval from a data I/O device (read) also involving large quantities of data,
with possibly stressful spatial or temporal memory access patterns, and locality issues (Kernel 3).
•
Small data I/O in all four kernels. Large data I/O in three of the four kernels.
•
Many small convolutions on random pieces of a large image (Kernel 4).
Status:
•
Written and Matlab Executable Specification v1.0 released June 22, 2006
•
Architecture of Data I/O Mode – Martha Bancroft of Shomo Tech Systems, and Jeremy Kepner
•
Works with Octave 2.9.5
•
Written Specification – SAR Editor – Glenn Schrader, MIT Lincoln Laboratory
•
C version based on release v1.0a (unofficial) – Meng-Ju of UMD, and Janice Onanian McMahon
of USC/ISI
MIT Lincoln Laboratory
4/8/2015
SSCA #3
Backup Slides
MIT Lincoln Laboratory
4/8/2015
SSCA #3 Specification
• Intent
• Overview
• Compute Mode Main Components
–
–
–
–
–
Synthetic Scalable Data Generator
Kernel 1 — SAR Image Formation
Template Insertion
Kernel 4 — Detection
Validation
–
–
–
–
–
Kernel 1 — Large & Small Data Retrieval
Image Grid
Kernel 2 — Image Storage
Kernel 3 — Image Retrieval
Kernel 4 — Small Image Storage
• Data I/O Mode Main Components
MIT Lincoln Laboratory
4/8/2015
The Vision ― Scalable Synthetic
Compact Applications
• Bridge the gap between scalable synthetic kernel
•
benchmarks and (non-scalable) real applications, and
become an important benchmarking tool
Is representative of real application workloads while not
being numerically rigorous
– memory access characteristics
– communications characteristics
– I/O characteristics
• Multi-processor compact application, designed to be easily
•
•
scalable and verifiable
No limits on the distribution to vendors and universities
SSCAs represent a wide spectrum of potential HPCS
Mission Partner applications
MIT Lincoln Laboratory
4/8/2015
Executable Specification
What is an Executable Specification:
•
•
•
•
•
It implements the Written Specification, illustrating all specified
properties; it is just one of many possible implementations
It provides developers further insight into the corresponding Written
Specification
It is a tool for developers with which to validate their own work
It includes a serial version, and may include one or more approaches to a
parallel version
It must be easily readable and intelligible, through its choice of functional
structure, variable names, comments, and supporting documentation
Structure:
•
•
•
•
Scalable Data Generator
– Creates synthetic data that can be scaled to stress any computer
from a single workstation to a petascale multiprocessor
Kernels – timed computational algorithms
Verification – checks the correctness of select results
Validation – validates the resulting solution
MIT Lincoln Laboratory
4/8/2015
SSCA #3 Specification
• Intent
• Overview
• Compute Mode Main Components
–
–
–
–
–
Synthetic Scalable Data Generator
Kernel 1 — SAR Image Formation
Template Insertion
Kernel 4 — Detection
Validation
–
–
–
–
–
Kernel 1 — Large & Small Data Retrieval
Image Grid
Kernel 2 — Image Storage
Kernel 3 — Image Retrieval
Kernel 4 — Small Image Storage
• Data I/O Mode Main Components
MIT Lincoln Laboratory
4/8/2015
SSCA #3 – Compute Only Mode
Sensor Processing
Scalable Data
and Template
Generator
Raw
SAR
Kernel #1
Image
Formation
SAR
Image
Kernel #2
Image
Storage
SAR
Image
Templates
Templates
Raw
SAR
File
Template
Insertion
Raw
SAR
File
Groups of
Template
Files
Raw SAR
Data Files
SAR
Image
File
Template
Files
Sub-Image
Detection
Files
Groups of
Template
Files
SAR
Image
File
Template
Files
Kernel #3
Image
Retrieval
SAR
Image Pair
Image
Files
Template
Files
Detection
File
Kernel #4
Detection
Detections
Validation
Templates
Knowledge Formation
MIT Lincoln Laboratory
4/8/2015
Spotlight SAR
MIT Lincoln Laboratory
4/8/2015
Compute Mode - SAR Overview
• Radar captures echo returns from a
‘swath’ on the ground
• Notional linear FM chirp pulse train,
plus two ideally non-overlapping
echoes returned from different
positions on the swath
Synthetic Aperture, L
Fixed to Broadside
...
• Summation and scaling of echo
returns realizes a challengingly long
antenna aperture along the flight path
Range,
X = 2X0
delayed transmitted
SAR waveform
s(t , u) 
  (n, m) pt  (n, m))
pulses swath
received
‘raw’ SAR
reflection coefficient scale factor, different
for each return from the swath
Cross-Range, Y = 2Y0
MIT Lincoln Laboratory
4/8/2015
Scalable Synthetic Data Generator
• Generates synthetic raw SAR
complex data
• Data size is scalable to enable
rigorous testing of high performance
computing systems
Spotlight SAR Returns
• Generates ‘templates’ that consist of
rotated and pixelated capitalized
letters
Range
– User defined scale factor determines
the size of images generated
Cross-Range
MIT Lincoln Laboratory
4/8/2015
Kernel 1 — SAR Image Formation
Spatial Frequency Domain Interpolation
s*0(w,ku)
s(t,u)
Fourier
s(w,ku)
Transform
(t,u)B(w,ku)
Matched
Filtering
Interpolation
kx = sqrt(4k2 –ku2)
ky = ku
Inverse
f(x,y)
Fourier Transform
F(kx,ky) (kx,ky) B (x,y)
Cross-Range, Pixels
Spotlight SAR Reconstruction
ky
o
Received
Samples
Fit a Polar
Swath
kx
Range, Pixels
Processed
Samples
Fit a
Rectangular
Swath
f
MIT Lincoln Laboratory
4/8/2015
Template Insertion
( not timed)
• Inserts rotated pixelated capital letter
templates into each SAR image
– Non-overlapping locations and rotations
– Randomly selects 50%
– Used as ideal detection targets in Kernel 4
Image Inserted with only
%50-Random Templates
Y Pixels
Y Pixels
Hypothetical
%100 Insertion of Templates
X Pixels
X Pixels
MIT Lincoln Laboratory
4/8/2015
Kernel 4 — Detection
• Detects targets in SAR images
1.
2.
3.
4.
Image difference
Threshold
Sub-regions
Correlate with every template
 max is target ID
•
– Many small correlations over
random pieces of a large image
•
Image A
Image Difference
Computationally difficult
•
Requires 100% recognition and
no false alarms including objects
Thresholded
that cross distributed
memory boundaries
Sub-region
Correlated
Image B
MIT Lincoln Laboratory
4/8/2015
Computational Challenges
Back-End
Knowledge Formation
Front-End
Sensor Processing
Scalable Data
and Template
Generator
Raw
SAR
Templates
• Scalable synthetic
data generation
Kernel #1
Image
Formation
SAR
Image
Template
Insertion
SAR
Image
Kernel #4
Detection
Detections
Validation
Templates
Templates
• Pulse compression
• Polar Interpolation
• FFT, IFFT (corner turn)
• Sequential store
• Non-sequential
retrieve
• Large & small IO
• Large Images
difference &
Threshold
• Many small
correlations on
selected pieces
of a large image
MIT Lincoln Laboratory
4/8/2015
SSCA #3 Specification
• Intent
• Overview
• Compute Mode Main Components
–
–
–
–
–
Synthetic Scalable Data Generator
Kernel 1 — SAR Image Formation
Template Insertion
Kernel 4 — Detection
Validation
–
–
–
–
–
Kernel 1 — Large & Small Data Retrieval
Image Grid
Kernel 2 — Image Storage
Kernel 3 — Image Retrieval
Kernel 4 — Small Image Storage
• Data I/O Mode Main Components
MIT Lincoln Laboratory
4/8/2015
SSCA #3 – Data I/O Mode
Stage 1: Front-End
Kernel #1
Data Read
and Image
Formation
Scalable Data
and Template
Generator
Large
Data
Large
Data
Groups of
Small Data
Large
Complex
Data
Image
Image
Groups of
Small Data
Sub-Images
Groups of
Small Data
Group of
Small Data
Image
Pair
Kernel #3
Image
Retrieval
Kernel #2
Image
Storage
Image Pair
Grid of
Images
Sub-Images
Kernel #4
Stage 2: Back-End
MIT Lincoln Laboratory
4/8/2015
Scalable Synthetic Data Generator
Scalable Data
Generator
Large
Data
Groups
of Small
Data
• Generates large complex data, and groups of
small data.
Kernel
#1
• Writes a ‘dialed’ number of large complex data to
external memory.
• For each large data, it writes a group of small data
to external memory.
• Single precision
Large
Complex
Data
Associated
Groups of
Small Data
• Not timed
MIT Lincoln Laboratory
4/8/2015
Kernel 1 — Data Retrieval
Stage 1: Front-End
Kernel #1
Data Read
Large
Data
Image
Small
Data
• Randomly reads one large complex data from
external memory, at each Stage 1 pass.
• Also reads associated group of small data from
external memory, at each Stage 1 pass.
• Generates a single precision random image (of the
size dialed by SCALE).
• I/O is timed
Large
Complex
Data
Associated
Groups of
Small Data
MIT Lincoln Laboratory
4/8/2015
Image Grid
•
External memory image Grid is accessed by
Kernels 2 & 3.
•
It is scalable by image size, number of
images.
•
Image size requires a non-trivial amount of
memory.
•
Intended for dealing with enormous quantity
of data, with simultaneous reads and writes.
GRID_SIDE_SIZE
Image
Grid
GRID_SIDE_SIZE
Image grid, shown scaled to 80 images
MIT Lincoln Laboratory
4/8/2015
Kernel 2 — Image Storage
Stage 1: Front-End
Image
Kernel #2
Image
Storage
•
Writes a different image to a random location in
the external memory on the Grid at each Stage 1
pass.
•
Images may be stored together, or in separate
pieces (to allow simultaneous reading/writing of
the same image).
•
I/O is timed
Image
Images
in Grid
•
Computes filenames and addresses, and writes
streaming data to random locations on Grid at
each Stage 1 Front-End processing pass.
MIT Lincoln Laboratory
4/8/2015
Kernel 3 — Image Retrieval
Images
In Grid
Templates
Image
Kernel #3
Image
Retrieval
Group
of small
data
N_image
x
N_image
N_grid
x
N_grid
Image Pair
Stage 2: Back-End
•
From a random location in the Grid, it computes the
address of an image sequence and reads a pair of its
images until it reaches its full depth, at each Stage 2
pass.
•
An image sequence is read through its entire Grid’s
Depth.
•
Also reads a group of small data at each Stage 2 pass.
•
I/O is timed
Image Grid
MIT Lincoln Laboratory
4/8/2015
Kernels 2 and 3
Additional notes:
•
If an optimal scheme is picked for data storage, it may not be optimal for data
retrieval, and vice versa.
•
“Read behind Write” is allowed.
Kernel 2
Image Output
Kernel 3
Image Pair
Input
MIT Lincoln Laboratory
4/8/2015
Kernel 4 — Small Image
•
Writes labeled sub-images. This is
repeated for each image pair, at each grid
point, at each Stage 2 pass.
•
I/O is timed
Sub-Images
Sub-Image
Image pair
Kernel #4
Small Image
Output
Stage 2: Back-End
MIT Lincoln Laboratory
4/8/2015
References
•
Carrara, Walter G., Ron S. Goodman and Ronald M. Majewski, Spotlight
Synthetic Aperture Radar: Signal Processing Algorithms. Boston: Artech
House, 1995.
•
Corlander, John C. and Robert N. McDonough, Synthetic Aperture Radar:
Systems and Signal Processing. New York: Wiley, 1991.
•
Haney, R., Meuse T., Kepner, J., and Lebak, J., The HPEC Challenge
Benchmark Suite, High Performance Embedded Computing Conference,
Lexington, MA 2005.
•
Jakowatz, Charles V., Jr., et al., Spotlight-Mode Synthetic Aperture Radar:
A Signal Processing Approach. Boston Kluwer Academic Publishers,1996.
•
Rihaczek, August W., Principles of High-Resolution Radar. Boston: Artech
House 1996. Originally published: New York: McGraw-Hill, 1969.
•
Stimson, George W., III, Introduction to Airborne Radar Second Edition.
World Color Book Services, 1998.
MIT Lincoln Laboratory
4/8/2015