(ISCAS-2011)
Reiner
Hartenstein
IEEE fellow
Aiming at the Natural Equilibrium
of Planet Earth
Requires to Reinvent Computing
1
TU Kaiserslautern
(Preface) Without Computers?
(Business Information System)
2
2011, [email protected]
© 2010,
Lufthansa anno 1960
http://hartenstein.de
TU Kaiserslautern
(Preface) very important
future Applications
The World Economic Forum:
replacing bureaucracies
by mass collaboration
http://www.macrowikinomics.com
other applications: see Cyber-Physical Systems
© 2010, [email protected]
3
http://hartenstein.de
Preface
TU Kaiserslautern
• Enormous Trouble in Computing:
– Longterm Programming Crisis
– Keynotes and Panel Discussions booming
– Excessive Power Consumption
2011, [email protected]
© 2010,
4
http://hartenstein.de
Outline (1)
TU Kaiserslautern
•Energy consumption of Computers
•Toward Exascale Computing
•The von Neumann Syndrome
•We need to Reinvent Computing
•Conclusions
2011, [email protected]
© 2010,
5
http://hartenstein.de
TU Kaiserslautern
Beyond peak oil
„6 more Saudi Arabias needed [Fatih Birol, Chief Economist IEA].
for demand predicted for 2030“ https://www.theoildrum.com/
© 2010, [email protected]
6
http://hartenstein.de
Saudi Arabia
TU Kaiserslautern
2011 [email protected]
[email protected]
2011,
©©2010,
7
7
http://hartenstein.de
8
TU Kaiserslautern
How many
more
Saudi
Arabias
needed?
Rio de Janeiro
2011, [email protected]
© 2010,
http://hartenstein.de
Power Consumption of the Internet
TU Kaiserslautern
9
Power consumption by internet:
x30 til 2030 if trends continue
soon 8 billion smart
wireless devices
G. Fettweis, E. Zimmermann: ICT Energy Consumption - Trends
and Challenges; WPMC'08, Lapland, Finland, 8 –11 Sep 2008
[Randy Katz: IEEE Spectrum, Febr. 2009]
Google Data Ccenter at Columbia River
2011, [email protected]
© 2010,
http://hartenstein.de
10
TU Kaiserslautern
More Google Data Centers
[datacenterknowledge.com]
Google causing 2% electricity consumption worldwide
?
http://hartenstein.de
10
© 2011 [email protected]
2011, [email protected]
© 2010,
Electricity Bill: a Key Issue
TU Kaiserslautern
Google going to sell electricity
Patent for water-based data centers
• Already in 2005, Google’s electricity
bill higher than value of its equipment.
Cost of a Google data center dominated only by monthly power bill
„The possibility of computer equipment power consumption
spiraling out of control could have serious consequences
for the overall affordability of computing.”
[L. A. Barroso, Google]
2011, [email protected]
© 2010,
11
http://hartenstein.de
The World's largest Data Center
TU Kaiserslautern
2011, [email protected]
© 2010,
12
http://hartenstein.de
[datacenterknowledge.com]
Microsoft Data Center at Quincey
TU Kaiserslautern
[datacenterknowledge.com]
2011, [email protected]
© 2010,
13
http://hartenstein.de
About 2000 datacenters world-wide
[datacenterknowledge.com]
TU Kaiserslautern
2011, [email protected]
© 2010,
14
http://hartenstein.de
Outline (2)
TU Kaiserslautern
•Energy consumption of Computers
•Toward Exascale Computing
•The von Neumann syndrome
•We need to Reinvent Computing
•Conclusions
2011, [email protected]
© 2010,
15
http://hartenstein.de
Multicore: Break-through or Breakdown?
TU Kaiserslautern
relative
performance
94
96
David Callahan, Microsoft
distinghuished endineer
begin of the
multicore era
16
„forcing a historic transition to a parallel
programming model yet to be invented“
98
00
02
year
04
2011, [email protected]
© 2010,
06
08 10
12
14
16
18
20
22
24
26
28
30
http://hartenstein.de
17
TU Kaiserslautern
„ intel has thrown
a Hail Mary Pass“
Dave
Patterson
2011, [email protected]
© 2010,
http://hartenstein.de
18
TU Kaiserslautern
John
Hennessy
„ … I would be panicking …“
2011, [email protected]
© 2010,
http://hartenstein.de
TU Kaiserslautern
Exascale affordable ?
Exa-scale: (1018 computations/second)
expected by 2018;
[several sources]
Power estimated (single supercomputer):
250 MW – 10 GW (2x NY City: 16 million people)
© 2010, [email protected]
19
http://hartenstein.de
Supercomputers:
TU Kaiserslautern
no Computers?
In my opinion, the largest
supercomputers at any time,
including the first exaflops,
should not be thought of as
computers. …
[Andrew Jones, vice president Numerical Algorithms Group]
© 2010, [email protected]
20
http://hartenstein.de
TU Kaiserslautern
Supercomputers as
Scientific Instruments
…Their usage patterns and
scientific impact are closer to
major research facilities such
as CERN, ITER, or Hubble.
[Andrew Jones, vice president Numerical Algorithms Group]
no reason to solve the power problem ?
© 2010, [email protected]
21
http://hartenstein.de
22
TU Kaiserslautern
2011, [email protected]
© 2010,
CERN (1)
http://hartenstein.de
23
TU Kaiserslautern
2011, [email protected]
© 2010,
CERN (2)
http://hartenstein.de
24
TU Kaiserslautern
2011, [email protected]
© 2010,
Hubble
http://hartenstein.de
25
Learning how to go Exascale
TU Kaiserslautern
CACHES 2011
1st International Workshop on Characterizing
Applications for Heterogeneous Exascale Systems
June 4th, 2011, held in conjunction with ICS'2011
25th International Conference on Supercomputing
2011,
http://hartenstein.de
© 2010,
May
[email protected]
- June 4, 2011, Loews Ventana Canyon Resort, Tucson,
Arizona
Outline (3)
TU Kaiserslautern
•Energy consumption of Computers
•Toward Exascale Computing
•The von Neumann syndrome
•We need to Reinvent Computing
•Conclusions
2011, [email protected]
© 2010,
26
http://hartenstein.de
TU Kaiserslautern
Potential of RC
Reconfigurable Computing offers
an overwhelming reduction
of electricity consumption
as well as massive speed-up factors …
2011, [email protected]
© 2010,
27
http://hartenstein.de
TU Kaiserslautern
PISA project
>15000
Speed-up
factors are
not new
100,000
10,000
?
Speedup-Factor
1,000,000
Image processing,
Pattern matching,
Multimedia
DSP and
real-time
wireless
face detection
6000
Reed-Solomon
Decoding
video-rate
stereo vision
pattern
recognition 730
900
1000
by avoiding the
von Neumann
paradigm
52
40
10
20
2011,
©©2010,
2011 [email protected]
[email protected]
1000
400
SPIHT wavelet-based
image compression
100
1
MAC
BLAST
288
457
FFT
88
protein
identification
28500
?
DES breaking
2400
DNA
seq.
8723
3000
crypto CT imaging
1000
Viterbi Decoding
Smith-Waterman
pattern matching
100
molecular
dynamics
simulation
Bioinformatics
Astrophysics
GRAPE
28
http://hartenstein.de
TU Kaiserslautern
Power save
factors
obtained
Speedup-Factor
106
Image processing,
Pattern matching,
Multimedia DSP and
6000
Energy saving
factors: ~10%
of speedup
SPIHT wavelet-based
image compression
52
40
20
100
http://hartenstein.de
2011,
©©2010,
2011 [email protected]
[email protected]
DES breaking
Reed-Solomon
Decoding
video-rate
stereo vision MAC
pattern 730
1000
900
recognition
400
103
28500
wireless
real-time
face detection
BLAST
288
457
FFT
88
protein
identification
DNA
2400 seq.
8723
3000
crypto CT imaging
1000
Viterbi Decoding
Smith-Waterman
pattern matching
100
molecular
dynamics
simulation
Bioinformatics
Astrophysics
GRAPE
29
http://hartenstein.de
RC*: Demonstrating the intensive Impact
TU Kaiserslautern
Tarek
El-Ghazawi
[Tarek El-Ghazawi et al.: IEEE COMPUTER, Febr. 2008]
SGI Altix 4700 with RC 100 RASC compared to Beowulf cluster
Application
.
DNA and Protein
sequencing
DES breaking
Power
Savings
Cost
Size
8723
779
22
253
28514
3439
96
1116
Speed-up
factor
massively
saving energy
*) RC = Reconfigurable Computing
2011, [email protected]
© 2010,
30
much less
equipment
needed
http://hartenstein.de
Drastically less Equipment needed
TU Kaiserslautern
a single rack without
air conditioning
For instance: a hangar full of racks replaced by
or ½ rack
© 2010, [email protected]
31
http://hartenstein.de
The Reconfigurability Paradox
TU Kaiserslautern
• Lower clock speed
• Massive wiring overhead
• Massive reconfigurability overhead
• Routing congestion
Orders of magnitude better performance by a
massively worse area-inefficient technology ?
2011, [email protected]
© 2010,
32
http://hartenstein.de
The von Neumann
Syndrome
because of
TU Kaiserslautern
2011 [email protected]
[email protected]
©©2010,
33
33
http://hartenstein.de
TU Kaiserslautern
von Neumann
Syndrome
Lambert M. Surhone,
Mariam T. Tennoe,
Susan F. Hennessow (ed.):
Von Neumann Syndrome;
ßetascript publishing 2011
2011, [email protected]
© 2010,
34
http://hartenstein.de
35
von Neumann Model Critics
TU Kaiserslautern
Nathan’s Law: Software is a gas.
It expands to fill all its containers ...
Nathan Myhrvold, Microsoft Ex-CTO
incompetent programmers
year
system
2001
Windows XP
2005 MAC OS X 10.4
2007 SAP Net Weaver
SLOC (millions)
40
86
238
“The von Neumann Syndrome”:
[C.V. “RAM” Ramamoorthy 2007; UC Berkeley]
Critique of von Neumann is not new:
Software Desaster Reports:
N. N. 1995: THE STANDISH GROUP REPORT
2011 [email protected]
E. ©
Dijkstra
1968; J. Backus 1978; Arvind , 1983;
Robert N. Charette 2005: Why Software Fails; IEEE Spectrum
2011, [email protected]
http://hartenstein.de
© 2010,
Anthony Berglas 2008: Why it is Important that Software Projects Fail
Peter G. Neumann 1985-2003; L. Savain 2006.
All hardware but ALU is overhead:
TU Kaiserslautern
x20 inefficiency
[R. Hameed et al.: Understanding Sources of Inefficiency in GeneralPurpose Chips; 37th ISCA, June 19-23, 2010, St. Malo, France]
“GP Processors
are inefficient”
(data
cashe)
x20 inefficiency:
just one of several
overhead layers
2011, [email protected]
© 2010,
36
http://hartenstein.de
„The Memory Wall“
TU Kaiserslautern
Performance
1000
coined by Sally McKee
The overwhealming problem is data
moving complexity, not processor
performance. Dr. Djordje Maric* (ETH Zurich),
100
>1000
Patterson’s Law:
Processor-Memory
Performance Gap:
(grows 50% / year)
CPU
10
1
1980
2011, [email protected]
© 2010,
DRAM
1990
2000
37
2008
http://hartenstein.de
Through-Silicon-Via (TSV)
TU Kaiserslautern
reducing the memory wall?
SIP multiple dice
PoP Package on Package
PiP Package in Package
TSV Through silicon via
reduce power consumption by 75%
[Wally Rh., Micro News 2/28/2011 ]
2011, [email protected]
© 2010,
38
http://hartenstein.de
Massive Overhead Phenomena
TU Kaiserslautern
von Neumann
overhead
machine
instruction fetch
instruction stream
state address computation instruction stream
data address computation instruction stream
data meet PU + other overh. instruction stream
i / o to / from off-chip RAM instruction stream
Inter PU communication
instruction stream
message passing overhead instruction stream
transactional memory overh. instruction stream
proportionate
to the number
of processors
overproportionate
to the number
of processors
multithreading overhead etc. instruction stream
© 2010, [email protected]
39
http://hartenstein.de
TU Kaiserslautern
von Neumann overhead vs.
Reconfigurable Computing
overhead
instruction fetch
state address computation
von Neumann
machine
instruction stream
instruction stream
data address computation
instruction stream
data meet PU + other overh. instruction stream
i / o to / from off-chip RAM instruction stream
Inter PU communication
instruction stream
datastream machine
none*
none*
none*
none*
none*
message passing overhead instruction stream
transactional memory overh. instruction stream
none*
none*
none*
multithreading overhead etc. instruction stream
none*
40
© 2010, [email protected]
40
http://hartenstein.de
Outline (4)
TU Kaiserslautern
•Energy consumption of Computers
•Toward Exascale Computing
•The von Neumann Syndrome
•We need to Reinvent Computing
•Conclusions
2011, [email protected]
© 2010,
41
http://hartenstein.de
Putting Old Ideas Into Practice
Software Engineering http://www.acm.org/sigsoft/SEN/parnas.html
SEN vol. 24 no. 3, May 1999
TU Kaiserslautern
The biggest payoff will come from
putting old ideas into practice
(POIIP) and teaching people
how to apply them properly. [David Parnas]
2011, [email protected]
© 2010,
42
http://hartenstein.de
Mike Flynn‘s Taxonomy
TU Kaiserslautern
M. J. Flynn: “Very high-speed computing systems”;
Proc. IEEE, Vol. 54, No. 12, pp. 1901–1909, Dec., 1966.
2011, [email protected]
© 2010,
43
http://hartenstein.de
© 2011
[email protected]
44
Diana‘s extended Taxonomy
TU Kaiserslautern
4 x SISD:
rSI: I can be reconfigured
at run time: e. g. RISP
rSD: can exchange data
memory or datapath
rSIrSD: both possible
4 x SIMD:
rSI: I can be reconfigured
at run time: e. g. RISP
rMD: SIMD processors can
exchange their data memories
or reconfigure their datapaths
rSIrMD: can reconfigure
both, D and Iat run time
4 x MIMD:
rMI: MPSoCs w.
reconfigurable I
rMD: MPSoCs w.
reconfigurable D
rMIrMD: supports both
I: instruction stream
D: data stream
D. Göhringer, M. Hübner, T. Perschke, J. Becker: “A Taxonomy of Reconfigurable Single/Multi-Processor Systems-on-Chip”;
2011, [email protected]
http://hartenstein.de
© 2010,
International
Journal of Reconfigurable Computing, Hindawi, Special Issue: Selected Papers from
ReCoSoC 2008, 2009.
Software to Configware Migration
TU Kaiserslautern
S = R + (if C then A else B endif);
section of a very large pipe
network:
R B A
decision box:
C =1
0
C
1
(de)multiplexer:
B
A
0
1
POIIP:
+
C
decision box turns
into (de)multiplexer **
W. A. Clark: 1967 SJCC, AFIPS Conf. Proc.
C. G. Bell et al: IEEE Trans-C21/5, May 1972
© 2010, [email protected]
45
http://hartenstein.de
POIIP: Loop to Pipe Mapping
TU Kaiserslautern
loop:
Memory
CPU
FMDemod
Pipeline:
Split
(reconfigurable)
DataPath Unit:
loop
body
complex
loop body
nested
loops
rDPU
loop
body
rDPU
rDPU
LPF1
LPF2
LPF3
HPF1
HPF2
HPF3
rDPU
Gather
rDPU
complex rDPU
or pipe network
inside rDPU
© 2010, [email protected]
46
Adder
Source:
MIT
StreamIT
Speaker
complex
pipe network
http://hartenstein.de
POIIP: Loop to Pipe Mapping
TU Kaiserslautern
loop:
Memory
CPU
FMDemod
Pipeline:
Split
(reconfigurable)
DataPath Unit:
loop
body
complex
loop body
nested
loops
rDPU
loop
body
rDPU
rDPU
LPF1
LPF2
LPF3
HPF1
HPF2
HPF3
rDPU
Gather
rDPU
complex rDPU
or pipe network
inside rDPU
© 2010, [email protected]
47
Adder
Source:
MIT
StreamIT
Speaker
complex
pipe network
http://hartenstein.de
on „platform
FPGAs“
Imperative Language Twins
MoPL: [FPL‘94, Prague]
TU Kaiserslautern
language category
Computer
Languages
von
Neumann
Languages
Languages f. Anti Machine
both deterministic
Software
proceduralLanguages
sequencing: traceable,
checkpointable
Flowware
Languages
read next instruction,
goto (instr. addr.),
jump (to instr. addr.),
instr. loop, loop nesting
no parallel loops, escapes,
instruction stream branching
program counter
massive memory
cycle overhead
read next data item,
goto (data addr.),
jump (to data addr.),
data loop, loop nesting,
parallel loops, escapes,
data stream branching
data counter(s)
Instruction fetch
parallel memory
bank access
memory cycle overhead
overhead avoided
interleaving only
no restrictions
language features
control flow +
data manipulation
data streams only
(no data manipulation)
operation
sequence
driven by:
state register
address
computation
© 2010, [email protected]
48
overhead avoided
Antimachine:
[COMPEURO
’89]
http://hartenstein.de
A Heliocentric CS Model needed
auto-sequencing Memory
TU Kaiserslautern
asM
FE
Flowware
Engineering
CPU SE
Software
Engineering
PE
Program
Engineering
structures
pipe network model
The Generalization of
Software Engineering —
2011, [email protected]
© 2010,
49
*) do not confuse
with „dataflow“!
Configware
CE Engineering
rDPU reconfigurable-Data-Path- Unit
rDPA reconfigurable-Data-Path- Array
http://hartenstein.de
TU Kaiserslautern
A Clean Terminology, please
program source
compilation result
Software
instruction streams
Flowware
data streams
Configware
© 2010, [email protected]
datapath structures configured
50
http://hartenstein.de
Outline (5)
TU Kaiserslautern
•Energy consumption of Computers
•Toward Exascale Computing
•The von Neumann Syndrome
•We need to Reinvent Computing
•Conclusions
2011, [email protected]
© 2010,
51
http://hartenstein.de
TU Kaiserslautern
absurdely
incomprehensible
abstractions
are the problem in „standard“ languages
We need model-based abstractions
at algorithmic level
[For architecture design & debug]
Concurrency models can operate at
component architecture level rather
than programming languages. [E. A. Lee]
[E. A. Lee: Are new languages necessary for multicore? 2007]
[E. A. Lee. The problem with threads. Computer, 2006.]
2011, [email protected]
© 2010,
52
http://hartenstein.de
Higher Abstraction Levels
TU Kaiserslautern
Nick Tredennick:
Efforts to extend standards-based, serial
programming languages with features to
describe parallel constructs are likely to fail.
What’s more likely to succeed are languages that
raise the level of abstraction in algorithm description
Mauricio Ayala-Rincón:
Term Rewriting Systems (TRS) may raise
the abstraction level up to math formulae
TRS: powerful for better language design and design space exploration
© 2010, [email protected]
53
http://hartenstein.de
Conclusions
TU Kaiserslautern
Since we‘ve to re-write software anyway
we should do it twin-pardigm.
We need a tool flow & education efforts supporting
a twin-paradigm approach and locality awareness
Twin Paradigm skills & basic hardware knowledge
are essential qualifications for programmers.
We urgently need a fundamental CS Education
and Research Revolution for dual-rail-thinking
© 2010, [email protected]
54
http://hartenstein.de
TU Kaiserslautern
We need „une' Levée en Masses“
We need „une' Levée en
Masses“
© 2010, [email protected]
55
55
http://hartenstein.de
Thank You very much !
too many panels
Don‘t worry !
and keynotes?
TU Kaiserslautern
2011, [email protected]
© 2010,
56
http://hartenstein.de
TU Kaiserslautern
2011, [email protected]
© 2010,
57
http://hartenstein.de
time to space mapping
TU Kaiserslautern
time domain:
procedure domain
space domain:
structure domain
time algorithm
space algorithm
pipeline
program loop
n time steps, 1 CPU
1 time step, n DPUs
Shuffle Sort
Bubble Sort
conditional
swap
n x k time steps,
1 „conditional
x
swap“ unit
y
k time steps,
n conditional swap“
units
conditional
swap
conditional
swap
conditional
swap
space/time algorithm s
time algorithm
© 2010, [email protected]
conditional
swap
58
http://hartenstein.de
Architecture instead of synchro:
Example
TU Kaiserslautern
conditional
swap
conditional
swap
conditional
swap
conditional
swap
conditional
swap
conditional
swap
conditional
swap
Better Architecture
instead of complex
synchronisation: half he
number of Blocks + up
und down of data (shuffle
function) – no von
Neumann-syndrome !
conditional
swap
conditional
swap
conditional
swap
conditional
swap
conditional
swap
direct time to
space mapping
accessing conflicts
modification:
with shufflefunction
„Shuffle Sort“
© 2010, [email protected]
59
http://hartenstein.de
TU Kaiserslautern
Understanding Complex
Hetero Systems
[Ed Lee]
We must change how programmers think
Internode Communications reduces Computational Efficiency
Understanding streams through complex fabrics needed
Efficient Distribution of Tasks being memory limited
Focusing on memory mapping issues and transfer modes to detect
overhead and bottlenecks
Layers of Abstraction and Automatic Parallelization hide
critical sources of, and limits to efficient parallel execution
essential: awareness of locality,
© 2010, [email protected]
60
http://hartenstein.de
Vertical Disintegration
courtesy Manfred Glesner
TU Kaiserslautern
1960
2011, [email protected]
© 2010,
61
200X
http://hartenstein.de
TU Kaiserslautern
Market Complexity
Source: Gartner
2011, [email protected]
© 2010,
62
http://hartenstein.de
TU Kaiserslautern
Taxonomy of Twin Paradigm
Programming Flows (HPRC)
[courtesy
Richard Newton]
„The nroff of EDA“ [R. N.]
E. El-Araby et al.: Comparative Analysis of High Level Programming for Reconfigurable Computers:
Methodology And Empirical Study; Proc. SPL2007, Mar del Plata, Argentina, Febr. 2007
2011, [email protected]
© 2010,
63
http://hartenstein.de
HLL programming models
TU Kaiserslautern
2011, [email protected]
© 2010,
64
http://hartenstein.de
TU Kaiserslautern
Some
hardware
description
languaqges
DeFacto
Galadriel & Nenya
MATCH
© 2010, [email protected]
65
http://hartenstein.de
TU Kaiserslautern
Some
programming
languages
© 2010, [email protected]
66
http://hartenstein.de
Some languages for parallelism
TU Kaiserslautern
© 2010, [email protected]
67
http://hartenstein.de
More Languages
© 2010, [email protected]
68
Some datastream languages
Some functional languages
TU Kaiserslautern
http://hartenstein.de
69
TU Kaiserslautern
© 2010, [email protected]
R. Rajkumar, I. Lee, L. Sha, J.
Stankovic: Cyber-Physical
Systems: The Next Computing
Revolution; DAC 2010
Why Computers
are important
http://hartenstein.de
Science alone ?
TU Kaiserslautern
see the claims by
Andrew Jones, …
2011, [email protected]
© 2010,
70
http://hartenstein.de
TU Kaiserslautern
Mobile Communication
Worldwide radio base station sites* (millions)
Average power consumption per site (kW)
Total power consumption of all sites (TW)
Total global RAN energy consumption (TWh)
total # of subscriptions expected (billions)
Broadband subscriptions expected (billions)
Video streams (%)
Share of mobile data in total mobile traffic (%)
*) all standards
37.5
2014
7.6
1.3
10
84
6
2
66
98
2020
11.2
1.1
12.5
99
9
90
99.6
A. Fehske, J. Malmodin, G. Biczók, G. Fettweis: The Global
Footprint of Mobile Communications – The Ecological and
Economic Perspective; IEEE Communications Magazine, Aug 2011
The data transmission speed growth
by a factor of ten every five years
(cellular, local + personal area networks),
2011, [email protected]
© 2010,
2007
3.3
1.7
5.6
49
71
Technologies to reduce energy
consumption are a key enabler
http://hartenstein.de
<1000 repeaters: <25 kW
TU Kaiserslautern
Undersea Cable
Google: 9,620km submarine cable Japan-US; 1st use Febr 21, 2011
Five fiber pairs deliver up to 4.8 Terabits per second (Tbps)
>100 kilometers
between repeaters
repeater laser power
consumption <25 W
wavelength-division
multiplexing dramatically
increases fiber capacity.
2011, [email protected]
© 2010,
multiple (e.g. 5) pairs of fibers: each
pair has one fiber in each direction
power consumption of fabrication
and cable layer ships much higher
72
http://hartenstein.de
Saudi Arabia
TU Kaiserslautern
2011 [email protected]
[email protected]
2011,
©©2010,
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TU Kaiserslautern
How many
more
Saudi
Arabias
needed?
Rio de Janeiro
2011, [email protected]
© 2010,
http://hartenstein.de
<1000 repeaters: <25 kW
TU Kaiserslautern
Undersea Cable
Google: 9,620km submarine cable Japan-US; 1st use Febr 21, 2011
Five fiber pairs deliver up to 4.8 Terabits per second (Tbps)
>100 kilometers
between repeaters
repeater laser power
consumption <25 W
wavelength-division
multiplexing dramatically
increases fiber capacity.
2011, [email protected]
© 2010,
multiple (e.g. 5) pairs of fibers: each
pair has one fiber in each direction
power consumption of fabrication
and cable layer ships much higher
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