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Application Specific Massive Parallelism Systolic and Instruction Systolic Computing Heiko...
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Application Specific Massive ParallelismApplication Specific Massive Parallelism
Systolic and Instruction Systolic Computing
Heiko Schröder, 2003
LOCOMAP
Heiko Schröder, 2003 Parallel control structures 2
Flynn’s taxonomy ...Flynn’s taxonomy ...
• SA --- Systolic Array
• SIMD --- Single Instruction Multiple Data
• ISA --- Instruction Systolic Array
• MIMD --- Multiple Instruction Multiple Data
• SPMD --- Single Program MD
Heiko Schröder, 2003 Parallel control structures 3
parallel mergeparallel merge
initial situation:
1.) sort columns
(odd-even-transposition sort)
2.) sort rows
(odd-even-transposition sort)
sorted !!!!
x1 x2 x3 x4 x5 x6
x7
x17 x18
y1 y2 y3 y4 y5 y6
y7
y17 y18
...
...
...
...
Heiko Schröder, 2003 Parallel control structures 4
0-1 principle0-1 principle
• The 0-1 principle states that if all sequences of 0 and 1 are sorted properly than this is a correct sorter.
• The sorter must be based on moving data.
initially
0s
0s
1s
after verticalsort
0s
1s
after horizontalsort
0s
1s
Heiko Schröder, 2003 Parallel control structures 6
systolic mergesystolic merge
1 3 3 45 5 6 79 8 8 74 4 3 2
Heiko Schröder, 2003 Parallel control structures 7
systolic mergesystolic merge
1 3 3 45 5 6 79 8 8 74 4 3 2
Heiko Schröder, 2003 Parallel control structures 8
systolic mergesystolic merge
1 3 3 45 5 6 79 8 8 74 4 3 2
Heiko Schröder, 2003 Parallel control structures 9
systolic mergesystolic merge
1 3 3 45 5 6 79 8 8 74 4 3 2
Heiko Schröder, 2003 Parallel control structures 10
systolic mergesystolic merge
1 3 3 45 5 6 79 8 8 74 4 3 2
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1 3 3 45 5 6 74 4 3 29 8 8 7
systolic mergesystolic merge
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systolic mergesystolic merge
1 3 3 45 5 6 74 4 3 29 8 8 7
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systolic mergesystolic merge
1 3 3 44 4 3 25 5 6 79 8 8 7
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systolic mergesystolic merge
1 3 3 44 4 3 25 5 6 79 8 8 7
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systolic mergesystolic merge
1 3 3 24 4 3 45 5 6 79 8 8 7
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systolic mergesystolic merge
1 3 3 24 4 3 45 5 6 79 8 8 7
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systolic mergesystolic merge
1 3 3 24 4 3 45 5 6 79 8 8 7
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systolic mergesystolic merge
1 3 2 34 3 4 45 5 6 79 8 8 7
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1 3 2 34 3 4 45 5 6 79 8 8 7
systolic mergesystolic merge
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1 2 3 33 4 4 45 5 6 79 8 8 7
systolic mergesystolic merge
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1 2 3 33 4 4 45 5 6 79 8 8 7
systolic mergesystolic merge
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1 2 3 33 4 4 45 5 6 79 8 8 7
systolic mergesystolic merge
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1 2 3 33 4 4 45 5 6 78 9 7 8
systolic mergesystolic merge
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1 2 3 33 4 4 45 5 6 78 9 7 8
systolic mergesystolic merge
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1 2 3 33 4 4 45 5 6 78 7 9 8
systolic mergesystolic merge
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systolic mergesystolic merge1 2 3 33 4 4 45 5 6 78 7 9 8
Heiko Schröder, 2003 Parallel control structures 27
• sorted !!!
systolic mergesystolic merge1 2 3 33 4 4 45 5 6 77 8 8 9
Time: 4n-3Period: nArea: 3n-2
Heiko Schröder, 2003 Parallel control structures 28
Systolic convolutionSystolic convolution
w6 w5 w4 w3 w2 w1* x3 * x2 * x1x6 * x5 * x4
* x3 * x2 * x1x6 * x5 * x4
* x3 * x2 * x1x6 * x5 * x4
* x3 * x2 * x1x6 * x5 * x4
* x3 * x2 * x1x6 * x5 * x4
* x3 * x2 * x1x6 * x5 * x4
* x3 * x2 * x1x6 * x5 * x4
time
Result stream(s)
Heiko Schröder, 2003 Parallel control structures 29
Characteristics of SAsCharacteristics of SAs
Extremely high cost-performanceno flexibility -- long development time
Suitable for special signal processing tasks ???
Heiko Schröder, 2003 Parallel control structures 30
ISA mergeISA merge
x1x8
y1y8
...
...
Period: 4n-4Time: 6n-6
48Heiko Schröder, 2003 Parallel control structures 48
Interface Processors
Interface Processors Interface Processors NorthNorth
Interface Interface Processors WestProcessors West
ISA
. . . ..
. .
.
49Heiko Schröder, 2003 Parallel control structures 49
Architecture of Systola 1024
Interface processors
ISA
RAM NORTH
program memory
host computer bus
Controller
RAM WEST
Heiko Schröder, 2003 Parallel control structures 50
Hough transform on the ISAHough transform on the ISA
• good line detection method
shear
Fast tomographyfast cryptography
Heiko Schröder, 2003 Parallel control structures 51
robot visionrobot vision
projectorCCD CCD
• stereo vision
Heiko Schröder, 2003 Parallel control structures 52
1. measuring a set of parallel rays
Computerized Tomography: Parallel Projections
1.2.
2. for a number of different angles
3.
3. reconstruction of the 2D-picture
4.
4. 3D-reconstruction out of many 2D-pictures
High-Speed Implementation on the ISA High-Speed Implementation on the ISA
Heiko Schröder, 2003 Parallel control structures 53
Radon Transform
Image Domain
Radon Tr.Radon Tr.
Radon Domain
Inverse Radon Tr.Inverse Radon Tr.
High-Speed Implementation on the ISA High-Speed Implementation on the ISA
Heiko Schröder, 2003 Parallel control structures 54
g( , )x y
t
g( cos sin , )x yt t t
Implementation of Backprojection
Implementation of Backprojection
Heiko Schröder, 2003 Parallel control structures 55
ISA mergeISA merge
1 3 3 45 5 6 79 8 8 74 4 3 2
C:=min{C, CE}
C:=max{C, CW}
Period: 4n-4Time: 6n-6
Heiko Schröder, 2003 Parallel control structures 86
Use of the ISAUse of the ISA
Areas of application for ISA:automatic optical quality control
real time signal processingcomputer graphics /visualizationlinear equationsCryptography --> Tele-medicine ?Bio-informatics
Special features:fast aggregate functions (sum, carry)fast local communicationno local memorytypical improvement over PC: Factor 20-30
Heiko Schröder, 2003 Parallel control structures 87
Future of massively parallel architectures
Future of massively parallel architectures
Amdahl’s Law:Amdahl’s Law:
Special purpose --- general purpose ?
Flexibility:Flexibility:
SIMD --- ISA --- MIMD
Physical laws:Physical laws:
Mesh/torus --- hypercube et.al.
Hybrid-networks !
Reconfigurable tori !
Optical computing !