REMEMBER THE MOVIE 2001 AND HAL
Dee Finney's blog
start date July 20, 2011
today's date April 27, 2014
TOPIC: THE WORLD'S SUPER COMPUTER
NOTE: WHEN I FIRST HEARD ABOUT THIS SUPER COMPUTER - I THOUGHT -
CERTAINLY CHINA WOULD HAVE IT. THE UNITED STATES NO LONGER IS TOP DOG
INTERESTING VIDEO - HOWEVER - WHOEVER OPERATED THIS COMPUTER CAN'T SPELL.
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a computer at
the frontline of contemporary processing capacity – particularly speed of
calculation which can happen at speeds of nanoseconds.
Supercomputers were introduced in the 1960s, made initially and, for decades,
primarily by Seymour
Cray at Control
Data Corporation (CDC), Cray
Research and subsequent companies
bearing his name or monogram. While the supercomputers of the 1970s used only a
in the 1990s machines with thousands of processors began to appear and, by the
end of the 20th century, massively
parallelsupercomputers with tens of thousands of "off-the-shelf" processors
were the norm. As of November 2013,
China's Tianhe-2 supercomputer
is the fastest
in the world at 33.86 petaFLOPS,
or 33.86 quadrillion floating point operations per second.
Systems with massive numbers of processors generally take one of two paths: In
one approach (e.g., in distributed
computing), a large number of discrete computers (e.g.,laptops)
distributed across a network (e.g., the Internet)
devote some or all of their time to solving a common problem; each individual
computer (client) receives and completes many small tasks, reporting the results
to a central server which integrates the task results from all the clients into
the overall solution. In another
approach, a large number of dedicated processors are placed in close proximity
to each other (e.g. in a computer
cluster); this saves considerable time moving data around and makes it
possible for the processors to work together (rather than on separate tasks),
for example in mesh and hypercube architectures.
The use of multi-core
processors combined with centralization is
an emerging trend; one can think of this as a small cluster (the multicore
processor in a smartphone, tablet,
laptop, etc.) that both depends upon and contributes to the
Supercomputers play an important role in the field of computational
science, and are used for a wide range of computationally intensive tasks in
various fields, including quantum
and gas exploration, molecular
modeling(computing the structures and properties of chemical compounds,
polymers, and crystals), and physical simulations (such as simulations of the
early moments of the universe, airplane and spacecraft aerodynamics, the
detonation of nuclear
weapons, and nuclear
fusion). Throughout their history, they have been essential in the field of cryptanalysis.
The history of supercomputing goes back to the 1960s, with the Atlas at
of Manchester and a series of
computers at Control
Data Corporation (CDC), designed
Cray. These used innovative designs and parallelism to achieve superior
computational peak performance.
The Atlas was
a joint venture between Ferranti and
the Manchester University and was designed to operate at processing speeds
approaching one microsecond per instruction, about one million instructions per
second. The first Atlas was
officially commissioned on 7 December 1962 as one of the world's first
supercomputers – considered to be the most powerful computer in the world at
that time by a considerable margin, and equivalent to four IBM
6600, released in 1964, was designed by Cray to be the fastest in the world
by a large margin. Cray switched from germanium to silicon transistors, which he
ran very fast, solving the overheating problem by introducing refrigeration. Given
that the 6600 outran all computers of the time by about 10 times, it was dubbed
a supercomputer and
defined the supercomputing market when one hundred computers were sold at $8
Cray left CDC in 1972 to form his own company. Four
years after leaving CDC, Cray delivered the 80 MHz Cray
1 in 1976, and it became one of
the most successful supercomputers in history. The Cray-2 released
in 1985 was an 8 processor liquid
cooledcomputer and Fluorinert was
pumped through it as it operated. It performed at 1.9 gigaflops and
was the world's fastest until 1990.
While the supercomputers of the 1980s used only a few processors, in the 1990s,
machines with thousands of processors began to appear both in the United States
and in Japan, setting new computational performance records. Fujitsu's Numerical
Wind Tunnelsupercomputer used 166 vector processors to gain the top spot in
1994 with a peak speed of 1.7 gigaflops per
SR2201 obtained a peak
performance of 600 gigaflops in 1996 by using 2048 processors connected via a
fast three-dimensionalcrossbar network.The Intel
Paragon could have 1000 to 4000 Intel
i860 processors in various
configurations, and was ranked the fastest in the world in 1993. The Paragon was
a MIMD machine
which connected processors via a high speed two dimensional
mesh, allowing processes to execute on separate nodes; communicating via the Message
Approaches to supercomputer
architecture have taken dramatic
turns since the earliest systems were introduced in the 1960s. Early
supercomputer architectures pioneered by Seymour
Cray relied on compact innovative
designs and local parallelism to
achieve superior computational peak performance.However, in time the demand for
increased computational power ushered in the age of massively
While the supercomputers of the 1970s used only a few processors,
in the 1990s, machines with thousands of processors began to appear and by the
end of the 20th century, massively parallel supercomputers with tens of
thousands of "off-the-shelf" processors were the norm. Supercomputers of the
21st century can use over 100,000 processors (some being graphic
units) connected by fast connections.
Throughout the decades, the management of heat
density has remained a key issue
for most centralized supercomputers. The
large amount of heat generated by a system may also have other effects, e.g.
reducing the lifetime of other system components. There
have been diverse approaches to heat management, from pumping Fluorinert through
the system, to a hybrid liquid-air cooling system or air cooling with normal air
Systems with a massive number of processors generally take one of two paths. In
computingapproach, the processing power of a large number of computers,
organised as distributed, diverse administrative domains, is opportunistically
used whenever a computer is available. In
another approach, a large number of processors are used in close proximity to
each other, e.g. in acomputer
cluster. In such a centralized massively
parallel system the speed and
flexibility of the interconnect becomes very important and modern supercomputers
have used various approaches ranging from enhanced Infiniband systems
to three-dimensional torus
interconnects. The use of multi-core
processors combined with
centralization is an emerging direction, e.g. as in the
As the price/performance of general
purpose graphic processors (GPGPUs)
has improved, a number of petaflop supercomputers
such as Tianhe-I and Nebulae have
started to rely on them. However,
other systems such as the K
computer continue to use
conventional processors such as SPARC-based
designs and the overall applicability of GPGPUs in
general purpose high performance computing applications has been the subject of
debate, in that while a GPGPU maybe tuned to score well on specific benchmarks
its overall applicability to everyday algorithms may be limited unless
significant effort is spent to tune the application towards it. However,
GPUs are gaining ground and in 2012 the Jaguar
supercomputer was transformed
into Titan by
replacing CPUs with GPUs.
A number of "special-purpose" systems have been designed, dedicated to a single
problem. This allows the use of specially programmedFPGA chips
or even custom VLSI chips,
allowing better price/performance ratios by sacrificing generality. Examples of
special-purpose supercomputers include Belle, Deep
Blue, and Hydra, for
playing chess, Gravity
Pipe for astrophysics, MDGRAPE-3 for
protein structure computation molecular dynamics and Deep
Crack, for breaking the DES cipher.
A typical supercomputer consumes large amounts of electrical power, almost all
of which is converted into heat, requiring cooling. For example, Tianhe-1A consumes
4.04 Megawatts of electricity. The
cost to power and cool the system can be significant, e.g. 4MW at $0.10/kWh is
$400 an hour or about $3.5 million per year.
Heat management is a major issue in complex electronic devices, and affects
powerful computer systems in various ways. The thermal
design power and CPU
power dissipation issues in
supercomputing surpass those of traditional computer
cooling technologies. The
supercomputing awards for green
computing reflect this issue.
The packing of thousands of processors together inevitably generates significant
amounts of heat
density that need to be dealt
with. The Cray
2 was liquid
cooled, and used a Fluorinert "cooling
waterfall" which was forced through the modules under pressure. However,
the submerged liquid cooling approach was not practical for the multi-cabinet
systems based on off-the-shelf processors, and in System
X a special cooling system that
combined air conditioning with liquid cooling was developed in conjunction with
In the Blue
Gene system IBM deliberately used
low power processors to deal with heat density. On
the other hand, the IBM Power
775, released in 2011, has closely packed elements that require water
cooling. The IBM Aquasar system,
on the other hand uses hot water
cooling to achieve energy
efficiency, the water being used to heat buildings as well.
The energy efficiency of computer systems is generally measured in terms of
"FLOPS per Watt". In 2008 IBM's
Roadrunner operated at 376 MFLOPS/Watt. In
November 2010, the Blue
Gene/Q reached 1684 MFLOPS/Watt. In
June 2011 the top 2 spots on theGreen
500 list were occupied by Blue
Gene machines in New York (one
achieving 2097 MFLOPS/W) with the DEGIMA
cluster in Nagasaki placing third
with 1375 MFLOPS/W.
Since the end of the 20th century, supercomputer
operating systems have undergone
major transformations, based on the changes insupercomputer
architecture. While early
operating systems were custom tailored to each supercomputer to gain speed, the
trend has been to move away from in-house operating systems to the adaptation of
generic software such as Linux.
Since modern massively
parallel supercomputers typically
separate computations from other services by using multiple types of nodes,
they usually run different operating systems on different nodes, e.g. using a
small and efficient lightweight
kernel such as CNK or CNL on
compute nodes, but a larger system such as a Linux-derivative
on server and I/O nodes.
While in a traditional multi-user computer system job
scheduling is in effect a tasking problem
for processing and peripheral resources, in a massively parallel system, the job
management system needs to manage the allocation of both computational and
communication resources, as well as gracefully dealing with inevitable hardware
failures when tens of thousands of processors are present.
Although most modern supercomputers use the Linux operating
system, each manufacturer has its own specific Linux-derivative, and no industry
standard exists, partly due to the fact that the differences in hardware
architectures require changes to optimize the operating system to each hardware
The parallel architectures of supercomputers often dictate the use of special
programming techniques to exploit their speed. Software tools for distributed
processing include standard APIssuch
as MPI and PVM, VTL,
source-based software solutions such as Beowulf.
In the most common scenario, environments such as PVM and MPI for
loosely connected clusters and OpenMP for
tightly coordinated shared memory machines are used. Significant effort is
required to optimize an algorithm for the interconnect characteristics of the
machine it will be run on; the aim is to prevent any of the CPUs from wasting
time waiting on data from other nodes.GPGPUs have
hundreds of processor cores and are programmed using programming models such as CUDA.
Moreover, it is quite difficult to debug and test parallel programs. Special
techniques need to be used for
testing and debugging such applications.
Main article: Grid
Opportunistic Supercomputing is a form of networked grid
computing whereby a “super
virtual computer” of many loosely
coupled volunteer computing
machines performs very large computing tasks. Grid computing has been applied to
a number of large-scale embarrassingly
parallelproblems that require supercomputing performance scales. However,
basic grid and cloud
computing approaches that rely on volunteer
computing can not handle
traditional supercomputing tasks such as fluid dynamic simulations.
The fastest grid computing system is the distributed
computing project Folding@home.
F@h reported 8.1 petaflops of x86 processing power as of March 2012. Of this,
5.8 petaflops are contributed by clients running on various GPUs, 1.7 petaflops
come from PlayStation
3 systems, and the rest from
various CPU systems.
The BOINC platform
hosts a number of distributed computing projects. As of May 2011, BOINC recorded
a processing power of over 5.5 petaflops through over 480,000 active computers
on the network The
most active project (measured by computational power), MilkyWay@home,
reports processing power of over 700 teraflops through
over 33,000 active computers.
As of May 2011, GIMPS's distributed Mersenne
Prime search currently achieves
about 60 teraflops through over 25,000 registered computers. The Internet
PrimeNet Server supports GIMPS's
grid computing approach, one of the earliest and most successful grid computing
projects, since 1997.
Quasi-opportunistic supercomputing is a form of distributed
computing whereby the “super
virtual computer” of a large number of networked geographically disperse
computers performs huge processing power demanding computing tasks. Quasi-opportunistic
supercomputing aims to provide a higher quality of service than opportunistic
grid computing by achieving more
control over the assignment of tasks to distributed resources and the use of
intelligence about the availability and reliability of individual systems within
the supercomputing network. However, quasi-opportunistic distributed execution
of demanding parallel computing software in grids should be achieved through
implementation of grid-wise allocation agreements, co-allocation subsystems,
communication topology-aware allocation mechanisms, fault tolerant message
passing libraries and data pre-conditioning.
Supercomputers generally aim for the maximum in capability
computing rather than capacity
computing. Capability computing is typically thought of as using the maximum
computing power to solve a single large problem in the shortest amount of time.
Often a capability system is able to solve a problem of a size or complexity
that no other computer can, e.g. a very complex weather
Capacity computing in contrast is typically thought of as using efficient
cost-effective computing power to solve a small number of somewhat large
problems or a large number of small problems, e.g. many user access requests to
a database or a web site.Architectures that lend themselves to supporting many
users for routine everyday tasks may have a lot of capacity but are not
typically considered supercomputers, given that they do not solve a single very
In general, the speed of supercomputers is measured and benchmarked in
(FLoating Point Operations Per Second), and not in terms of MIPS,
i.e. as "instructions per second", as is the case with general purpose
computers. These measurements are
commonly used with an SI
prefix such as tera-,
combined into the shorthand "TFLOPS" (1012 FLOPS,
pronounced teraflops), or peta-,
combined into the shorthand "PFLOPS" (1015 FLOPS,
pronounced petaflops.) "Petascale"
supercomputers can process one quadrillion (1015)
(1000 trillion) FLOPS. Exascale is
computing performance in the exaflops range. An exaflop is one quintillion (1018)
FLOPS (one million teraflops).
No single number can reflect the overall performance of a computer system, yet
the goal of the Linpack benchmark is to approximate how fast the computer solves
numerical problems and it is widely used in the industry. The
FLOPS measurement is either quoted based on the theoretical floating point
performance of a processor (derived from manufacturer's processor specifications
and shown as "Rpeak" in the TOP500 lists) which is generally unachievable when
running real workloads, or the achievable throughput, derived from the LINPACK
benchmarks and shown as "Rmax" in
the TOP500 list. The LINPACK benchmark typically performs LU
decomposition of a large matrix.
The LINPACK performance gives some indication of performance for some real-world
problems, but does not necessarily match the processing requirements of many
other supercomputer workloads, which for example may require more memory
bandwidth, or may require better integer computing performance, or may need a
high performance I/O system to achieve high levels of performance.
Since 1993, the fastest supercomputers have been ranked on the TOP500 list
according to their
LINPACK benchmark results. The
list does not claim to be unbiased or definitive, but it is a widely cited
current definition of the "fastest" supercomputer available at any given time.
This is a recent list of the computers which appeared at the top of the TOP500
list, and the "Peak speed" is given
as the "Rmax" rating. For more historical data see History
The stages of supercomputer application may be summarized in the following
The IBM Blue
Gene/P computer has been used to simulate a number of artificial neurons
equivalent to approximately one percent of a human cerebral cortex, containing
1.6 billion neurons with approximately 9 trillion connections. The same research
group also succeeded in using a supercomputer to simulate a number of artificial
neurons equivalent to the entirety of a rat's brain.
Modern-day weather forecasting also relies on supercomputers. The National
Oceanic and Atmospheric Administration uses
supercomputers to crunch hundreds of millions of observations to help make
weather forecasts more accurate.
In 2011, the challenges and difficulties in pushing the envelope in
supercomputing were underscored by IBM's
abandonment of the Blue
Waters petascale project.
Given the current speed of progress, industry experts estimate that
supercomputers will reach 1 exaflops (1018,
one quintillion FLOPS) by 2018. China has stated plans to have a 1 exaflop
supercomputer online by 2018. Using
MIC multi-core processor
architecture, which is Intel's response to GPU systems, SGI plans to achieve a
500-fold increase in performance by 2018, in order to achieve one exaflop.
Samples of MIC chips with 32 cores, which combine vector processing units with
standard CPU, have become available. The
Indian government has also stated ambitions for an exaflop-range supercomputer,
which they hope to complete by 2017.
Erik P. DeBenedictis of Sandia
National Laboratories theorizes
that a zettaflop (1021, one sextillion FLOPS)
computer is required to accomplish full weather
modeling, which could cover a two-week time span accurately. Such
systems might be built around 2030.
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Intel says Moore's Law holds until 2029". Heise
The TOP500 project
ranks and details the 500 most powerful (non-distributed) computer systems
in the world. The project was started in 1993 and publishes an updated list of
the supercomputers twice
a year. The first of these updates always coincides with the International
Supercomputing Conference in
June, and the second one is presented in November at theACM/IEEE
Supercomputing Conference. The project aims to provide a reliable basis for
tracking and detecting trends in high-performance computing and bases rankings
on HPL, a portable implementation of
the high-performance LINPACK benchmark written
in Fortran for distributed-memory computers.
The TOP500 list is compiled by Hans
Meuer of the University
of Mannheim,Germany, Jack
Dongarra of the University
of Tennessee, Knoxville,
and Erich Strohmaier and Horst Simon of NERSC/Lawrence
Berkeley National Laboratory.
In the early 1990s, a
new definition of supercomputer was needed to produce meaningful statistics.
After experimenting with metrics based on processor count in 1992, the idea
was born at the University
of Mannheim to use a detailed
listing of installed systems as the basis. Early 1993 Jack
Dongarrawas persuaded to join the project with his LINPACK
benchmark. A first test version was produced in May 1993, partially
based on data available on the Internet, including the following sources:
- "List of the World's Most Powerful
Computing Sites" maintained by Gunter Ahrendt
David Kahaner, the director of the Asian
Technology Information Program (ATIP), in
1992 had published a report titled "Kahaner
Report on Supercomputer in Japan" which
had an immense amount of data.
The information from
those sources was used for the first two lists. Since June 1993, the TOP500
is produced bi-annually based on site and vendor submissions only.
Since 1993, performance
of the #1 ranked position has steadily grown in agreement with Moore's
law, doubling roughly every 14 months. As of June 2013, the fastest
system, the Tianhe-2 with
Rpeak of 54.9024 PFlop/s,
is over 419,102 times faster than the fastest system in November 1993, the Connection
Machine CM-5/1024 (1024
cores) with Rpeak of 131.0 GFlop/s.
As of November 2013,
TOP500 supercomputers are overwhelmingly based on x86-64 CPUs (Intel EMT64 and AMD AMD64 instruction
set architecture), with the RISC-based Power
Architecture used by IBM
POWER microprocessors, and SPARC making
up the remainder. Prior to the ascendance of 32-bit x86 and
later 64-bit x86-64 in
the early 2000s,
a variety of RISC processor
families made up the majority of TOP500 supercomputers, including RISC architectures
such as SPARC, MIPS, PA-RISC and Alpha.
Top 10 positions of the 42nd TOP500
on November 18, 2013
Xeon E5–2692 + Xeon
Phi 31S1P, TH
National Supercomputing Center in Guangzhou
Opteron 6274 + Tesla
K20X, Cray Gemini Interconnect
Oak Ridge National Laboratory
Linux (CLE, SLESbased)
PowerPC A2, Custom
Lawrence Livermore National Laboratory
Linux (RHEL andCNK)
SPARC64 VIIIfx, Tofu
PowerPC A2, Custom
Argonne National Laboratory
Linux (RHEL andCNK)
Xeon E5–2670 + Tesla
Swiss National Supercomputing Centre
Xeon E5–2680 + Xeon
Texas Advanced Computing Center
PowerPC A2, Custom
Linux (RHEL andCNK)
PowerPC A2, Custom
Lawrence Livermore National Laboratory
Linux (RHEL andCNK)
Xeon E5–2680, Infiniband
- Rank – Position within the
TOP500 ranking. In the TOP500 List table, the computers are ordered
first by their Rmax value. In the case of equal performances (Rmax
value) for different computers, the order is by Rpeak. For sites that
have the same computer, the order is by memory size and then
- Rmax – The highest score
measured using the LINPACK
benchmark suite. This is
the number that is used to rank the computers. Measured in quadrillions of floating
point operations per second,
- Rpeak – This is the theoretical
peak performance of the system. Measured in Pflops.
- Name – Some supercomputers are
unique, at least on its location, and are therefore christened by its
- Computer – The computing
platform as it is marketed.
- Processor cores – The number of
cores actively used
running LINPACK. After this figure is the processor
architectureof the cores named. If the interconnect between
computing nodes is of interest, it's also included here.
- Vendor – The manufacturer of
the platform and hardware.
- Site – The name of the facility
operating the supercomputer.
- Country – The country in which
the computer is situated.
- Year – The year of
installation/last major update.
- Operating system – The
operating system that the computer uses.
Numbers below represent
the number of computers in the TOP500 that are in each of the listed
- NUDT Tianhe-2A ( China,
June 2013 - present)
- Cray Titan ( United
States, November 2012 - June 2013)
- IBM Sequoia Blue
Gene/Q ( United
States, June 2012 – November 2012)
- Fujitsu K
computer ( Japan,
June 2011 – June 2012)
- NUDT Tianhe-1A ( China,
November 2010 – June 2011)
- Cray Jaguar ( United
States, November 2009 – November 2010)
- IBM Roadrunner ( United
States, June 2008 – November 2009)
- IBM Blue
Gene/L ( United
States, November 2004 – June 2008)
- NEC Earth
Simulator ( Japan,
June 2002 – November 2004)
- IBM ASCI
White ( United
States, November 2000 – June 2002)
- Intel ASCI
Red ( United
States, June 1997 – November 2000)
- Hitachi CP-PACS ( Japan,
November 1996 – June 1997)
- Hitachi SR2201 ( Japan,
June 1996 – November 1996)
- Fujitsu Numerical
Wind Tunnel ( Japan,
November 1994 – June 1996)
- Intel Paragon
XP/S140 ( United
States, June 1994 – November 1994)
- Fujitsu Numerical
Wind Tunnel ( Japan,
November 1993 – June 1994)
- TMC CM-5 ( United
States, June 1993 – November 1993)
By number of systems as
of November 2013:
A few machines that have
not been benchmarked are not eligible for the list: such as NCSA's Blue
Waters. Additionally purpose-built machines that are not capable or do
not run the benchmark are not included: such as RIKEN
China Still Has The World's Fastest Supercomputer
Earlier this week, the Top500 organization
announced its semi-annual list of the Top
500 supercomputers in
the world. And for the second year in a row, China’s Tianhe-2 is the
world’s fastest by a long shot, maintaining its performance of 33.86
petaflop/s (quadrillions of calculations per second) on the
standardized benchmark attached to every supercomputer on the list.
Tiahne-2 supercomputer. (Credit: National University of Defense
In fact, the top 5 fastest supercomputers on the November 2013 list
are the same as the top 5 fastest supercomputers on the June 2013
list. The second fastest supercomputer in the world is Cray’s Titan
supercomputer at the Oak Ridge National Laboratory.
Rounding out the top five are IBM’s Sequoia supercomputer, RIKEN’s K
Computer in Japan, and IBM’s Mira supercomputer at the Argonne
The stability of the the top five is unique in the past few years,
which has seen several different computers being named the fastest,
while others moved down and up the ranks.
The Tianhe-2 was built by the National
University of Defense Technology in China. It has a total of
3,120,000 Intel processing cores, but also features a number of
Chinese built components and runs on a version of Linux
called Kylin, which was natively developed by the NUDT.
The newest entry into the top ten supercomputer list is number 6 on
the list, Piz Daint, a Cray system that has been installed at Swiss
National Supercomputing Centre. Piz Daint is now the fastest
supercomputer in Europe. It’s also the most energy efficient system
in the Top 10. That’s something to note, because one of the biggest
constraints on supercomputing is the sheer amount of power needed to
operate the systems.
“In the top 10, computers might hit a high mark,” Kai Dupke, a
senior product manager at SUSE Linux told me in a conversation about
the supercomputer race. “But in the usual operation, they operate
more slowly simply because it’s too expensive to run on full speed.”
Although China is still home to the world’s fastest supercomputer,
the United States continues to be the leader in high performance
computing. In June, the United States had 253 of the top 500 fastest
computers. In the November list, it has 265. China, on the other
hand, has 63 computers on the list, down from the 65 it had in June.
Follow me on Twitter or Facebook.
Read my Forbes blog here.
That new supercomputer is not your friend
China reclaims the fastest computer in the world prize. Get ready for even
We learned this week that China has the fastest
supercomputer in the world, by a long shot. The Tianhe-2 is almost
twice as speedy as the previous record holder,
a U.S.-made Cray Titan.
Such news, by itself, isn’t particularly amazing.
It’s not even the first time a Chinese supercomputer has held the top
ranking. The Tianhe-1 grabbed the pole position in November 2010 and held it
until June 2011. Previously, Japan and the United States had traded
places since 1993. Supercomputing speed
follows roughly the same trajectory as Moore’s Law — it doubles about every
14 months. There will always be new contenders for the throne.
But this month, there’s a new context for news
about the debut of ever more powerful supercomputers. Consider the first
comment left on Reddit to a thread announcing the
exploits of Tianhe-2:
This would be a pretty awesome tool for churning through millions of
phone records and digital copies of people’s online data.
Haha. Funny. But not really. The not-so-subtle
implication of the constant expansion of supercomputing capacity is that, to
borrow a quote from Intel’s Raj Hazra that appeared in the
New York Times, “the insatiable need for
computing is driving this.” Big Data wants Big Computers.
“WE JOKINGLY REFERRED TO HIM AS EMPEROR ALEXANDER, BECAUSE
WHATEVER KEITH WANTS, KEITH GETS.”
Inside the government, the general is regarded with a
mixture of respect and fear, not unlike J. Edgar Hoover,
another security figure whose tenure spanned multiple
presidencies. “We jokingly referred to him as Emperor
Alexander—with good cause, because whatever Keith wants,
Keith gets,” says one former senior CIA official who agreed
to speak on condition of anonymity. “We would sit back
literally in awe of what he was able to get from Congress,
from the White House, and at the expense of everybody else.”
Now 61, Alexander has said he plans to retire in 2014; when
he does step down he will leave behind an enduring legacy—a
position of far-reaching authority and potentially
Strangelovian powers at a time when the distinction between
cyberwarfare and conventional warfare is beginning to blur.
A recent Pentagon report made that point in dramatic terms.
It recommended possible deterrents to a cyberattack on the
US. Among the options: launching nuclear weapons.
THIS IS WHERE I POST WHAT I'M DOING AND THINKING
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