radar problem real-life problem - tau
TRANSCRIPT
1
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Fast Algorithms and Parallel Computing: Solution of Extremely Large Real-Life Problems
in Electromagnetics
Levent Gürel CEO, ABAKUS Computing Technologies
Professor Emeritus, Bilkent University, Turkey Founder, Computational Electromagnetics Research Center (BiLCEM)
Adjunct Professor, Dept. of ECE, University of Illinois at Urbana-Champaign
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
!"#$""%&'
(")**+',+*-%.%/*"'
,0*#*"12'3+4&#%5&'
6$#%7%#$+1%5&'
8%)%+'94&#$7&'
:-/2%5''(7%.1".''94&#$7&'
;1*5*.12%5'9#+<2#<+$&'
=8;3>'
?1+$5$&&'3*77<"12%/*"&'
@ABC8%".$'31+2<1#&'
!"#$%&'(")*$"+,-./#01"2+3)4-.5$"/3)6,)7'8"-"1,)9-":;"1+'"3)
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Radar Problem
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Real-Life Problem
Electromagnetic Modeling of Microcircuits
2
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Real-Life Problem
Nano-Optical Imaging Systems
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
20 GHz
30 GHz
Photonic crystals
!"#$%&'()*+),-'&'.*/-.)0/+1&'$()
Ö. Ergül, T. Malas, and L. Gürel, Analysis of dielectric photonic-crystal problems with MLFMA and Schur- complement preconditioners, J. Lightwave Technol., vol. 29, no. 6, pp. 888–897, Mar. 2011.
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Radiation into Living Organisms
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Rx Antenna Rx Antenna
Rx Antenna
Rx Antenna
Tx Antenna
Tx Antenna
Tx Antenna
Tx Antenna
Microwave Imaging
3
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Double-Ridged Horn
Fractal Spiral
Log-Periodic
Vivaldi
Conical-Corrugated Horn
Archimedian
Patch+SRRs
Conical Spiral
Patch
Antennas
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Mobile Device Antennas
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Satellite Antennas
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Antennas Mounted on Platforms
•! Interaction of multiple antennas •! Characteristics of mounted antennas (different from isolated antennas) •! Optimization of the placement of the antennas
4
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 2-$3+5)!56-/+5$'5*)
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Maxwell s Equations
!"E(r ,t) =#
$$t
B(r ,t)
!"H(r ,t) =
##t
D(r ,t)+J (r ,t)
!"B(r ,t) = 0
!"D(r ,t) = !(r ,t)
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Surface Integral Equations
CFIE = !EFIE + (1! !)MFIE
•! Electric-Field Integral Equation (EFIE):
•! Magnetic-Field Integral Equation (MFIE):
•! Combined-Field Integral Equation (CFIE):
•! Hybrid-Field Integral Equation (HFIE):
HFIE = !(r)EFIE + 1! !(r)"
#$%&'MFIE
J(r)! n̂(r)" d #r J( #r )" #$ g r, #r( )#S% = n̂(r)"H
inc(r)
!t̂(r) " ik d #r I ! $#$
k2
%
&''''
(
)****g r, #r( ) "J( #r )
#S+ =
1!t̂(r) "E inc(r)
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Geometry Discretization
Stealth Antennas
Airborne
5
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Discretization
ZmnE = dr tm(r)
Sm
! " d #r G r, #r( ) "bn( #r )Sn
!
ZmnM = dr tm(r)
Sm
! "bn(r)# dr tm(r)Sm
! " n̂ $ d %r bn( %r )$ %& g r, %r( )Sn
!
Zmnan =n=1
N
! vm, m = 1,2,…N
•! Matrix equations:
•! Matrix elements:
Zmn
C = !ZmnE + (1! !)
ik
ZmnM
Basis functions
Testing functions
J(r) = an bn(r)
n=1
N
!
Number of unknowns
Z
mn
H = !mZ
mn
E + (1! !m
)i
kZ
mn
M
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Matrix Elements…
…are electromagnetic interactions
ZmnE = dr tm(r)
Sm
! " d #r G r, #r( ) "bn( #r )Sn
!
ZmnE an =
n=1
N
! vmE , m = 1,2,…N
Basis functions
Testing functions
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Geometry Discretization
Modeling with millions of triangles.
Mesh Size: !/10
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Matrix Equation
System of Linear Equations
A!x=b
Z !a=v
6
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Iterative Solutions
Z !a = v
x
Z !x = y y
M ! z = w
w
z
•! Large matrix equations
a
•! CG and CGS •! BiCG and BiCGStab •! QMR and TFQMR •! GMRES •! LSQR
Parallel Multilevel Fast Multipole Algorithm (MLFMA)
Matrix-Vector Multiplications Iterative
Algorithm
•! BDP •! NFP •! Filtered NFP •! ILU and ILUT •! SAI •! Nested PCs
Preconditioner
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Fast and Faster Solvers
MOM FMM MLFMA
Parallel
MLFMA
1.5 GHz
2 GB hafıza
8000 bilinmeyen
3 GHz
2 GB hafıza
30.000 bilinmeyen
6 GHz
1,4 GB hafıza
130.000 bilinmeyen
12 GHz
650 MB hafıza
520.000 bilinmeyen
8 bilgisayar
0,3m yarıçaplı küre için
frequency
Higher frequency
Larger problems
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Fast Multipole Method
Aggregation
Translation
Disaggregation
Cluster of basis functions
F !G
(k̂) = Fn !G
(k̂) an
n" !G#
F
GT(k̂) = T
L(k,D)
!G "N (G)# F !G
(k̂)
Z
mna
nn=1
N
! =14!
d 2k̂ FmGC (k̂) "F
GT(k̂)#
Cluster of testing functions
Complexity: O(N3/2)
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm <;$2$"="$)9#3,)<;$2>.$")6$0.-',?/
7
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
7#8-#*'8))+/)-5.+$-59):'&8()
O(N logN )!!);+$%&'"-*<=)
>/'')(*/3.*3/')7'.3/(-6').&3(*'/-59)
;&3(*'/()
?3&4&'6'&)@#(*)?3&4%+&')A&9+/-*B$)
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Reduced-Complexity Solutions
Astrophysics (Gravitational Force) Acoustics
Electrodynamics (Helmholtz s Equation)
Molecular Dynamics
Quantum Mechanics (Schroedinger s Equation)
Electrostatics (Laplace s Equation)
Fast Multipole Methods
Structural Mechanics Fluid Dynamics
Heat Equation
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Books
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Parallel Computing
NODES
NETWORK SWITCH
SERVERCONNECTED TO INTERNET
SLAVE HOST
Constructing a Parallel Computer Cluster
8
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Parallel Computers
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Parallel Computing
Constructing Parallel Computer Clusters
8 nodes
Per node: Two Intel Xeon 5345 2.33 GHz Quad-Core Processors
Total of 64 cores
Per node: 32 GB DDR2-667 533 MHz RAM
Total of 256 GB of RAM
Network: Infiniband
17 nodes
Per node: Two Intel Xeon 5472 3 GHz Quad-Core Processors
Total of 136 cores
Per node: 32 GB DDR2-667 533 MHz RAM
Total of 544 GB of RAM
Network: Infiniband
136-Core Cluster 64-Core Cluster
Not in www.top500.org!!!
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
What is the Main Source of Efficiency?
N Unknowns
O(N3) Gaussian Elimination
O(N2) Iterative MOM
(MVM)
O(N3/2) Single-Level
FMM
O(N logN) Multi-Level FMM
1000 1 s 2 s 4 s 8 s
106 32 years 23 days 35 h 7 h
107 32 K years 6.3 years 46 days 89 h
108 32 M years 630 years 4 years 46 days
109 32 G years 63 K years 127 years 1.5 years
(555 days)
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 53 Million Unknowns
Sphere with radius of 120! and diameter of 240! November 2007
120!
53,112,384 Unknowns
9
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 69 Million Unknowns
Sphere with radius of 150! and diameter of 300! December 2007
150!
69,177,600 Unknowns
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 85 Million Unknowns
Sphere with radius of 150! and diameter of 300!
January 2008
150!
85,148,160 Unknowns
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
How Large are Large Matrix Equations?
Each element of the matrix is a complex number with real and imaginary parts
If we could fit each element of the matrix in a square of 1 in by 1 in…
1 in
1 in 7.654321E+02 + j 1.234567E-03
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 85 Million " 85 Million Matrix
10
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Intel Pamphlet on the World Record
Available at www.cem.bilkent.edu.tr
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 135 Million Unknowns
Sphere with radius of 180! and diameter of 360!
August 2008
135,164,928 Unknowns
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
180!
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 167 Million Unknowns
Sphere with radius of 190! and diameter of 380!
August 2008
167,534,592 Unknowns
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
190!
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 205 Million Unknowns
Sphere with radius of 210! and diameter of 420!
September 2008
204,823,296 Unknowns
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
210!
11
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Parallelization
Work load in the tree structure
Field samples
Clusters www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Parallelization
Work load in the tree structure
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Parallelization
Field Sampling
Coarse Sampled Data Big Data!
Fine Sampled Data
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Parallelization
Work load in the tree structure
Field samples
Clusters
12
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Partitioning of the Tree Structure In the parallelization of MLFMA, the main work is partitioning the tree structure
Tree structure
Field samples
Clusters
Simple Partitioning
-! Clusters are distributed in all levels
-! Inefficient for high levels (poor load-balance)
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LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Hybrid Partitioning of the Tree Structure
(Chew et al.) Hybrid Partitioning (improves load-balance for higher levels)
Distributed levels:
Shared levels: Translations are communication-free Aggregations & disaggregations require communications
Translations require communications Aggregations & disaggregations are communication-free
Problem: There are some levels, where neither distributing fields nor clusters is efficient
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Partitioning of the Tree Structure
Hierarchical Partitioning
Fields and clusters are “perfectly” distributed
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Partitioning of Multilevel Tree •! Hierarchical partitioning
Fields and clusters are “perfectly” distributed
•! Define intermediate levels
Level 2
Level 3
Level 2.5
Modify partitioning
Aggregation/Disaggregation
12
34
56
78
1
2
3
4
5
6
7
8
1324
5768
13
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Timeline of Large Problems
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 307 Million Unknowns
Sphere with radius of 260! and diameter of 520!
December 2009
307,531,008 Unknowns
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
260!
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 375 Million Unknowns
Sphere with radius of 280! and diameter of 560!
December 2009
374,490,624 Unknowns
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
280!
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 540 Million Unknowns
Sphere with radius of 340! and diameter of 680!
September 2010
540,659,712 Unknowns
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
340!
14
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 670 Million Unknowns
Sphere with radius of 340! and diameter of 680!
2013
670 Million Unknowns Scattering results are compared to analytical values to demonstrate the accuracy of the numerical
solution.
340!
340!
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 850 Million Unknowns
Sphere with radius of 440! and diameter of 880!
2014
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
880!
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm 1.1 Billion Unknowns
Sphere with radius of 500! and diameter of 1000!
2014
Scattering results are compared to analytical values to demonstrate the accuracy of the numerical solution.
1000!
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Metamaterials
Split-Ring Resonators
15
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
SRR Walls dB
dB dB
1 Layer
4 Layer 2 Layer
Pow
er T
rans
mis
sion
(dB
)
Frequency (GHz)
Meta Property: Stop band in frequency!
No Pass
Pass
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Special Configurations
Closed-Ring Resonators (CRRs)
Thin Wire Array (TWA)
All pass! No pass!
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Composite Metamaterial (CMM)
Split-Ring Resonators (SRRs)
Thin Wire Array (TWA)
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
CMM Walls dB
dB dB
1 Layer
4 Layer 2 Layer
Pow
er T
rans
mis
sion
(dB
)
Frequency (GHz)
Meta Property: Pass band in frequency!
No Pass
Pass
16
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Large MM Problem
20 x 51 x 29 SRR Block
2,425,560 Unknowns
MLFMA parallelized into 64 process on a cluster of Intel-Xeon processors connected via Infiniband network
85 GHz
100 GHz
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm @*ABC<6!DEAF)
A6#-&')C/+$)DDDE#1#F3(E.+$%3459E*'.B5+&+9<)
2.#G'/-59)C/+$)(%B'/')H/#8-3(=)IJ! K)LMJ!)!N'1K1#('8)#%%&-.#4+5=)O%&+#8)*B').+$%3*#4+5#&)/'(3&*()#58)9'*)*B')'//+/)D-*B)/'(%'.*)*+)#5#&<4.#&)?-'K('/-'()(+&34+5(E)
2.#G'/-59)C/+$)PA2A)A&$+58)H(-Q'=)RM! K)STSM!)!N'1K1#('8)#%%&-.#4+5=)O%&+#8)*B').+$%3*#4+5#&)/'(3&*()#58)9'*)*B')'//+/)D-*B)/'(%'.*)*+)+3/)/'(3&*(E))
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm G*@%@6H*7)@*ABC<6!DEAF)IJJ&)
N'1(-*')358'/).+5(*/3.4+5U)N-&&)1')#6#-&')C/+$)
DDDE.'$E1-&F'5*E'83E*/)
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
64/26
Red Blood Cells (RBCs) Ordinary (Healthy)
Red Blood Cell Macrocyte
(Macrocytosis) Microcyte
(Microcytosis)
Spherocyte (Spherocytosis) Sickle Cell
(Sickle Cell Anemia)
17
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II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Flow Cytometry
Blood sample
Forward scattering
Side scattering Backscattering
Source: http://nirfriedmanlab.blogspot.in/2010_04_01_archive.html
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm Scattering from and Imaging of
Red Blood Cells Electromagnetic fields
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Forward Scattering
No Detection
Detection of an Extra-Ordinary Cell
Detection of an Extra-Ordinary Cell
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Forward Scattering
No Detection
Detection of an Extra-Ordinary Cell
Detection of an Extra-Ordinary Cell
An extra-ordinary cell may have normal
SCS values
An extra-ordinary cell can be detected for some orientations
An extra-ordinary cell can be detected for all orientations
18
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
Decision Chart
www.abakus.computing.technology
II EE EE EE AA PP Distinguished Lecturer
LLeevveenntt Gürel AACCCC,, TTeell AAvviivv UUnniivveerrssiittyy AAnntteennnnaass MMiinnii--SSyymmppoossiiuumm
SOLUTION OF LARGE
COMPUTATIONAL ELECTROMAGNETICS
PROBLEMS
Mathematics Integral Equations Numerical Analysis
Linear Algebra Iterative Solvers Preconditioners
Computer Engineering Parallel Architectures Multicore Processors
Computer Science Fast Algorithms
Parallel Programming
Geometry Modelling 3-D CAD Modelling
Meshing
Physics Electromagnetic Theory Radiation and Scattering
Multi-Disciplinary Approach