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What is supercomputer? – Definition from WhatIs.com

A supercomputer is a computer that performs at or near the currently highest operational rate for computers. Traditionally, supercomputers have been used for scientific and engineering applications that must handle very large databases or do a great amount of computation (or both).Although advances likemulti-core processors and GPGPUs (general-purpose graphics processing units)have enabled powerful machinesfor personal use (see: desktop supercomputer, GPU supercomputer),by definition, a supercomputer is exceptional in terms of performance.

At any given time, there are a few well-publicized supercomputers that operate at extremely high speeds relative to all other computers. The term is also sometimes applied to far slower (but still impressively fast) computers. The largest, most powerful supercomputers are really multiple computers that perform parallel processing. In general, there are two parallel processing approaches: symmetric multiprocessing (SMP) and massively parallel processing (MPP).

As of June 2016, the fastest supercomputer in the world was the Sunway TaihuLight, in the city of Wixu in China. A few statistics on TaihuLight:

The first commercially successful supercomputer, the CDC (Control Data Corporation) 6600 was designed by Seymour Cray. Released in 1964, the CDC 6600 had a single CPU and cost $8 million the equivalent of $60 million today. The CDC could handle three million floating point operations per second (flops).

Cray went on to found a supercomputer company under his name in 1972. Although the company has changed hands a number of times it is still in operation. In September 2008, Cray and Microsoft launched CX1, a $25,000 personal supercomputer aimed at markets such as aerospace, automotive, academic, financial services and life sciences.

IBM has been a keen competitor. The company’s Roadrunner, once the top-ranked supercomputer, was twice as fast as IBM’s Blue Gene and six times as fast as any of other supercomputers at that time. IBM’s Watson is famous for having adopted cognitive computing to beat champion Ken Jennings on Jeopardy!, a popular quiz show.

Year

Supercomputer

Peak speed (Rmax)

Location

2016

Sunway TaihuLight

93.01PFLOPS

Wuxi, China

2013

NUDTTianhe-2

33.86PFLOPS

Guangzhou, China

2012

CrayTitan

17.59PFLOPS

Oak Ridge, U.S.

2012

IBMSequoia

17.17PFLOPS

Livermore, U.S.

2011

FujitsuK computer

10.51PFLOPS

Kobe, Japan

2010

Tianhe-IA

2.566PFLOPS

Tianjin, China

2009

CrayJaguar

1.759PFLOPS

Oak Ridge, U.S.

2008

IBMRoadrunner

1.026PFLOPS

Los Alamos, U.S.

1.105PFLOPS

In the United States, some supercomputer centers are interconnected on an Internet backbone known as vBNS or NSFNet. This network is the foundation for an evolving network infrastructure known as the National Technology Grid. Internet2 is a university-led project that is part of this initiative.

At the lower end of supercomputing, clustering takes more of a build-it-yourself approach to supercomputing. The Beowulf Project offers guidance on how to put together a number of off-the-shelf personal computer processors, using Linux operating systems, and interconnecting the processors with Fast Ethernet. Applications must be written to manage the parallel processing.

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What is supercomputer? – Definition from WhatIs.com

Supercomputer – Simple English Wikipedia, the free …

A supercomputer is a computer with great speed and memory. This kind of computer can do jobs faster than any other computer of its generation. They are usually thousands of times faster than ordinary personal computers made at that time. Supercomputers can do arithmetic jobs very fast, so they are used for weather forecasting, code-breaking, genetic analysis and other jobs that need many calculations. When new computers of all classes become more powerful, new ordinary computers are made with powers that only supercomputers had in the past, while new supercomputers continue to outclass them.

Electrical engineers make supercomputers that link many thousands of microprocessors.

Supercomputer types include: shared memory, distributed memory and array. Supercomputers with shared memory are developed by using a parallel computing and pipelining concept. Supercomputers with distributed memory consist of many (about 100~10000) nodes. CRAY series of CRAYRESERCH and VP 2400/40, NEC SX-3 of HUCIS are shared memory types. nCube 3, iPSC/860, AP 1000, NCR 3700, Paragon XP/S, CM-5 are distributed memory types.

An array type computer named ILIAC started working in 1972. Later, the CF-11, CM-2, and the Mas Par MP-2 (which is also an array type) were developed. Supercomputers that use a physically separated memory as one shared memory include the T3D, KSR1, and Tera Computer.

Organizations

Centers

View original post here:

Supercomputer – Simple English Wikipedia, the free …

What is supercomputer? – Definition from WhatIs.com

A supercomputer is a computer that performs at or near the currently highest operational rate for computers. Traditionally, supercomputers have been used for scientific and engineering applications that must handle very large databases or do a great amount of computation (or both).Although advances likemulti-core processors and GPGPUs (general-purpose graphics processing units)have enabled powerful machinesfor personal use (see: desktop supercomputer, GPU supercomputer),by definition, a supercomputer is exceptional in terms of performance.

At any given time, there are a few well-publicized supercomputers that operate at extremely high speeds relative to all other computers. The term is also sometimes applied to far slower (but still impressively fast) computers. The largest, most powerful supercomputers are really multiple computers that perform parallel processing. In general, there are two parallel processing approaches: symmetric multiprocessing (SMP) and massively parallel processing (MPP).

As of June 2016, the fastest supercomputer in the world was the Sunway TaihuLight, in the city of Wixu in China. A few statistics on TaihuLight:

The first commercially successful supercomputer, the CDC (Control Data Corporation) 6600 was designed by Seymour Cray. Released in 1964, the CDC 6600 had a single CPU and cost $8 million the equivalent of $60 million today. The CDC could handle three million floating point operations per second (flops).

Cray went on to found a supercomputer company under his name in 1972. Although the company has changed hands a number of times it is still in operation. In September 2008, Cray and Microsoft launched CX1, a $25,000 personal supercomputer aimed at markets such as aerospace, automotive, academic, financial services and life sciences.

IBM has been a keen competitor. The company’s Roadrunner, once the top-ranked supercomputer, was twice as fast as IBM’s Blue Gene and six times as fast as any of other supercomputers at that time. IBM’s Watson is famous for having adopted cognitive computing to beat champion Ken Jennings on Jeopardy!, a popular quiz show.

Year

Supercomputer

Peak speed (Rmax)

Location

2016

Sunway TaihuLight

93.01PFLOPS

Wuxi, China

2013

NUDTTianhe-2

33.86PFLOPS

Guangzhou, China

2012

CrayTitan

17.59PFLOPS

Oak Ridge, U.S.

2012

IBMSequoia

17.17PFLOPS

Livermore, U.S.

2011

FujitsuK computer

10.51PFLOPS

Kobe, Japan

2010

Tianhe-IA

2.566PFLOPS

Tianjin, China

2009

CrayJaguar

1.759PFLOPS

Oak Ridge, U.S.

2008

IBMRoadrunner

1.026PFLOPS

Los Alamos, U.S.

1.105PFLOPS

In the United States, some supercomputer centers are interconnected on an Internet backbone known as vBNS or NSFNet. This network is the foundation for an evolving network infrastructure known as the National Technology Grid. Internet2 is a university-led project that is part of this initiative.

At the lower end of supercomputing, clustering takes more of a build-it-yourself approach to supercomputing. The Beowulf Project offers guidance on how to put together a number of off-the-shelf personal computer processors, using Linux operating systems, and interconnecting the processors with Fast Ethernet. Applications must be written to manage the parallel processing.

See the original post:

What is supercomputer? – Definition from WhatIs.com

Supercomputer – Simple English Wikipedia, the free …

A supercomputer is a computer with great speed and memory. This kind of computer can do jobs faster than any other computer of its generation. They are usually thousands of times faster than ordinary personal computers made at that time. Supercomputers can do arithmetic jobs very fast, so they are used for weather forecasting, code-breaking, genetic analysis and other jobs that need many calculations. When new computers of all classes become more powerful, new ordinary computers are made with powers that only supercomputers had in the past, while new supercomputers continue to outclass them.

Electrical engineers make supercomputers that link many thousands of microprocessors.

Supercomputer types include: shared memory, distributed memory and array. Supercomputers with shared memory are developed by using a parallel computing and pipelining concept. Supercomputers with distributed memory consist of many (about 100~10000) nodes. CRAY series of CRAYRESERCH and VP 2400/40, NEC SX-3 of HUCIS are shared memory types. nCube 3, iPSC/860, AP 1000, NCR 3700, Paragon XP/S, CM-5 are distributed memory types.

An array type computer named ILIAC started working in 1972. Later, the CF-11, CM-2, and the Mas Par MP-2 (which is also an array type) were developed. Supercomputers that use a physically separated memory as one shared memory include the T3D, KSR1, and Tera Computer.

Organizations

Centers

Read the original here:

Supercomputer – Simple English Wikipedia, the free …

What is supercomputer? – Definition from WhatIs.com

A supercomputer is a computer that performs at or near the currently highest operational rate for computers. Traditionally, supercomputers have been used for scientific and engineering applications that must handle very large databases or do a great amount of computation (or both).Although advances likemulti-core processors and GPGPUs (general-purpose graphics processing units)have enabled powerful machinesfor personal use (see: desktop supercomputer, GPU supercomputer),by definition, a supercomputer is exceptional in terms of performance.

At any given time, there are a few well-publicized supercomputers that operate at extremely high speeds relative to all other computers. The term is also sometimes applied to far slower (but still impressively fast) computers. The largest, most powerful supercomputers are really multiple computers that perform parallel processing. In general, there are two parallel processing approaches: symmetric multiprocessing (SMP) and massively parallel processing (MPP).

As of June 2016, the fastest supercomputer in the world was the Sunway TaihuLight, in the city of Wixu in China. A few statistics on TaihuLight:

The first commercially successful supercomputer, the CDC (Control Data Corporation) 6600 was designed by Seymour Cray. Released in 1964, the CDC 6600 had a single CPU and cost $8 million the equivalent of $60 million today. The CDC could handle three million floating point operations per second (flops).

Cray went on to found a supercomputer company under his name in 1972. Although the company has changed hands a number of times it is still in operation. In September 2008, Cray and Microsoft launched CX1, a $25,000 personal supercomputer aimed at markets such as aerospace, automotive, academic, financial services and life sciences.

IBM has been a keen competitor. The company’s Roadrunner, once the top-ranked supercomputer, was twice as fast as IBM’s Blue Gene and six times as fast as any of other supercomputers at that time. IBM’s Watson is famous for having adopted cognitive computing to beat champion Ken Jennings on Jeopardy!, a popular quiz show.

Year

Supercomputer

Peak speed (Rmax)

Location

2016

Sunway TaihuLight

93.01PFLOPS

Wuxi, China

2013

NUDTTianhe-2

33.86PFLOPS

Guangzhou, China

2012

CrayTitan

17.59PFLOPS

Oak Ridge, U.S.

2012

IBMSequoia

17.17PFLOPS

Livermore, U.S.

2011

FujitsuK computer

10.51PFLOPS

Kobe, Japan

2010

Tianhe-IA

2.566PFLOPS

Tianjin, China

2009

CrayJaguar

1.759PFLOPS

Oak Ridge, U.S.

2008

IBMRoadrunner

1.026PFLOPS

Los Alamos, U.S.

1.105PFLOPS

In the United States, some supercomputer centers are interconnected on an Internet backbone known as vBNS or NSFNet. This network is the foundation for an evolving network infrastructure known as the National Technology Grid. Internet2 is a university-led project that is part of this initiative.

At the lower end of supercomputing, clustering takes more of a build-it-yourself approach to supercomputing. The Beowulf Project offers guidance on how to put together a number of off-the-shelf personal computer processors, using Linux operating systems, and interconnecting the processors with Fast Ethernet. Applications must be written to manage the parallel processing.

Read more:

What is supercomputer? – Definition from WhatIs.com

Tianhe-I – Wikipedia

Tianhe-1 and Tianhe-1AActiveTianhe-1 Operational 29 October 2009, Tianhe-1A Operational 28 October 2010SponsorsNational University of Defense TechnologyOperatorsNational Supercomputing CenterLocationNational Supercomputing Center, Tianjin, People’s Republic of ChinaOperating systemLinux[1]Storage96 TB (98304 GB) for Tianhe-1,262TB for Tianhe-1ASpeedTianhe-1: 563 teraFLOPS (Rmax) 1,206.2 teraFLOPS (Rpeak),Tianhe-1A: 2,566.0 teraFLOPS (Rmax) 4,701.0 teraFLOPS (Rpeak)RankingTOP500: 2nd, June 2011 (Tianhe-1A)PurposePetroleum exploration, aircraft simulationSourcestop500.org

Tianhe-I, Tianhe-1, or TH-1 (Chinese: , [tjnxixau]; Sky River Number One)[2] is a supercomputer capable of an Rmax (maximum range) of 2.5 petaFLOPS. Located at the National Supercomputing Center of Tianjin, China, it was the fastest computer in the world from October 2010 to June 2011 and is one of the few Petascale supercomputers in the world.[3][4]

In October 2010, an upgraded version of the machine (Tianhe-1A) overtook ORNL’s Jaguar to become the world’s fastest supercomputer, with a peak computing rate of 2.57 petaFLOPS.[5][6] In June 2011 the Tianhe-1A was overtaken by the K computer as the world’s fastest supercomputer, which was also subsequently superseded.[7]

Both the original Tianhe-1 and Tianhe-1A use a Linux-based operating system.[8][9]

On 12 August 2015, the 186,368-core Tianhe-1, felt the impact of the powerful Tianjin explosions and went offline for some time. Xinhua reports that “the office building of Chinese supercomputer Tianhe-1, one of the world’s fastest supercomputers, suffered damage.” Sources at Tianhe-1 told Xinhua the computer is not damaged, but they have shut down some of its operations as a precaution.[10] Operation resumed on 17 August 2015.[11]

Tianhe-1 was developed by the Chinese National University of Defense Technology (NUDT) in Changsha, Hunan. It was first revealed to the public on 29 October 2009, and was immediately ranked as the world’s fifth fastest supercomputer in the TOP500 list released at the 2009 Supercomputing Conference (SC09) held in Portland, Oregon, on 16 November 2009. Tianhe achieved a speed of 563 teraflops in its first Top 500 test and had a peak performance of 1.2 petaflops. Thus at startup, the system had an efficiency of 46%.[12][13] Originally, Tianhe-1 was powered by 4,096 Intel Xeon E5540 processors and 1,024 Intel Xeon E5450 processors, with 5,120 AMD graphics processing units (GPUs), which were made up of 2,560 dual-GPU ATI Radeon HD 4870 X2 graphics cards.[14][15]

In October 2010, Tianhe-1A, an upgraded supercomputer, was unveiled at HPC 2010 China.[16] It is now equipped with 14,336 Xeon X5670 processors and 7,168 Nvidia Tesla M2050 general purpose GPUs. 2,048 FeiTeng 1000 SPARC-based processors are also installed in the system, but their computing power was not counted into the machine’s official Linpack statistics as of October2010.[17] Tianhe-1A has a theoretical peak performance of 4.701 petaflops.[18] NVIDIA suggests that it would have taken “50,000 CPUs and twice as much floor space to deliver the same performance using CPUs alone.” The current heterogeneous system consumes 4.04 megawatts compared to over 12 megawatts had it been built only with CPUs.[19]

The Tianhe-1A system is composed of 112 computer cabinets, 12 storage cabinets, 6 communications cabinets, and 8 I/O cabinets. Each computer cabinet is composed of four frames, with each frame containing eight blades, plus a 16-port switching board. Each blade is composed of two computer nodes, with each computer node containing two Xeon X5670 6-core processors and one Nvidia M2050 GPU processor.[20] The system has 3584 total blades containing 7168 GPUs, and 14,336 CPUs, managed by the SLURM job scheduler.[21] The total disk storage of the systems is 2 Petabytes implemented as a Lustre clustered file system,[2] and the total memory size of the system is 262 Terabytes.[17]

Another significant reason for the increased performance of the upgraded Tianhe-1A system is the Chinese-designed NUDT custom designed proprietary high-speed interconnect called Arch that runs at 160 Gbit/s, twice the bandwidth of InfiniBand.[17]

The system also used the Chinese made FeiTeng-1000 central processing unit.[22] The FeiTeng-1000 processor is used both on service nodes and to enhance the system interconnect.[22][23]

The supercomputer is installed at the National Supercomputing Center, Tianjin, and is used to carry out computations for petroleum exploration and aircraft design.[13] It is an “open access” computer, meaning it provides services for other countries.[24] The supercomputer will be available to international clients.[25]

The computer cost $88 million to build. Approximately $20 million is spent annually for electricity and operating expenses. Approximately 200 workers are employed in its operation.

Tianhe-IA was ranked as the world’s fastest supercomputer in the TOP500 list[26][27] until July 2011 when the K computer overtook it.

In June 2011, scientists at the Institute of Process Engineering (IPE) at the Chinese Academy of Sciences (CAS) announced a record-breaking scientific simulation on the Tianhe-1A supercomputer that furthers their research in solar energy. CAS-IPE scientists ran a complex molecular dynamics simulation on all 7,168 NVIDIA Tesla GPUs to achieve a performance of 1.87 petaflops (about the same performance as 130,000 laptops).[28]

The Tianhe-1A supercomputer was shut down after the National Supercomputing Center of Tianjin was damaged by an explosion nearby. The computer was not damaged and still remains operational.[29]

Originally posted here:

Tianhe-I – Wikipedia

Home | National Center for Supercomputing Applications at …

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Supercomputer – Simple English Wikipedia, the free …

A supercomputer is a computer with great speed and memory. This kind of computer can do jobs faster than any other computer of its generation. They are usually thousands of times faster than ordinary personal computers made at that time. Supercomputers can do arithmetic jobs very fast, so they are used for weather forecasting, code-breaking, genetic analysis and other jobs that need many calculations. When new computers of all classes become more powerful, new ordinary computers are made with powers that only supercomputers had in the past, while new supercomputers continue to outclass them.

Electrical engineers make supercomputers that link many thousands of microprocessors.

Supercomputer types include: shared memory, distributed memory and array. Supercomputers with shared memory are developed by using a parallel computing and pipelining concept. Supercomputers with distributed memory consist of many (about 100~10000) nodes. CRAY series of CRAYRESERCH and VP 2400/40, NEC SX-3 of HUCIS are shared memory types. nCube 3, iPSC/860, AP 1000, NCR 3700, Paragon XP/S, CM-5 are distributed memory types.

An array type computer named ILIAC started working in 1972. Later, the CF-11, CM-2, and the Mas Par MP-2 (which is also an array type) were developed. Supercomputers that use a physically separated memory as one shared memory include the T3D, KSR1, and Tera Computer.

Organizations

Centers

Read more:

Supercomputer – Simple English Wikipedia, the free …

What is supercomputer? – Definition from WhatIs.com

A supercomputer is a computer that performs at or near the currently highest operational rate for computers. Traditionally, supercomputers have been used for scientific and engineering applications that must handle very large databases or do a great amount of computation (or both).Although advances likemulti-core processors and GPGPUs (general-purpose graphics processing units)have enabled powerful machinesfor personal use (see: desktop supercomputer, GPU supercomputer),by definition, a supercomputer is exceptional in terms of performance.

At any given time, there are a few well-publicized supercomputers that operate at extremely high speeds relative to all other computers. The term is also sometimes applied to far slower (but still impressively fast) computers. The largest, most powerful supercomputers are really multiple computers that perform parallel processing. In general, there are two parallel processing approaches: symmetric multiprocessing (SMP) and massively parallel processing (MPP).

As of June 2016, the fastest supercomputer in the world was the Sunway TaihuLight, in the city of Wixu in China. A few statistics on TaihuLight:

The first commercially successful supercomputer, the CDC (Control Data Corporation) 6600 was designed by Seymour Cray. Released in 1964, the CDC 6600 had a single CPU and cost $8 million the equivalent of $60 million today. The CDC could handle three million floating point operations per second (flops).

Cray went on to found a supercomputer company under his name in 1972. Although the company has changed hands a number of times it is still in operation. In September 2008, Cray and Microsoft launched CX1, a $25,000 personal supercomputer aimed at markets such as aerospace, automotive, academic, financial services and life sciences.

IBM has been a keen competitor. The company’s Roadrunner, once the top-ranked supercomputer, was twice as fast as IBM’s Blue Gene and six times as fast as any of other supercomputers at that time. IBM’s Watson is famous for having adopted cognitive computing to beat champion Ken Jennings on Jeopardy!, a popular quiz show.

Year

Supercomputer

Peak speed (Rmax)

Location

2016

Sunway TaihuLight

93.01PFLOPS

Wuxi, China

2013

NUDTTianhe-2

33.86PFLOPS

Guangzhou, China

2012

CrayTitan

17.59PFLOPS

Oak Ridge, U.S.

2012

IBMSequoia

17.17PFLOPS

Livermore, U.S.

2011

FujitsuK computer

10.51PFLOPS

Kobe, Japan

2010

Tianhe-IA

2.566PFLOPS

Tianjin, China

2009

CrayJaguar

1.759PFLOPS

Oak Ridge, U.S.

2008

IBMRoadrunner

1.026PFLOPS

Los Alamos, U.S.

1.105PFLOPS

In the United States, some supercomputer centers are interconnected on an Internet backbone known as vBNS or NSFNet. This network is the foundation for an evolving network infrastructure known as the National Technology Grid. Internet2 is a university-led project that is part of this initiative.

At the lower end of supercomputing, clustering takes more of a build-it-yourself approach to supercomputing. The Beowulf Project offers guidance on how to put together a number of off-the-shelf personal computer processors, using Linux operating systems, and interconnecting the processors with Fast Ethernet. Applications must be written to manage the parallel processing.

The rest is here:

What is supercomputer? – Definition from WhatIs.com

Supercomputer – Simple English Wikipedia, the free …

A supercomputer is a computer with great speed and memory. This kind of computer can do jobs faster than any other computer of its generation. They are usually thousands of times faster than ordinary personal computers made at that time. Supercomputers can do arithmetic jobs very fast, so they are used for weather forecasting, code-breaking, genetic analysis and other jobs that need many calculations. When new computers of all classes become more powerful, new ordinary computers are made with powers that only supercomputers had in the past, while new supercomputers continue to outclass them.

Electrical engineers make supercomputers that link many thousands of microprocessors.

Supercomputer types include: shared memory, distributed memory and array. Supercomputers with shared memory are developed by using a parallel computing and pipelining concept. Supercomputers with distributed memory consist of many (about 100~10000) nodes. CRAY series of CRAYRESERCH and VP 2400/40, NEC SX-3 of HUCIS are shared memory types. nCube 3, iPSC/860, AP 1000, NCR 3700, Paragon XP/S, CM-5 are distributed memory types.

An array type computer named ILIAC started working in 1972. Later, the CF-11, CM-2, and the Mas Par MP-2 (which is also an array type) were developed. Supercomputers that use a physically separated memory as one shared memory include the T3D, KSR1, and Tera Computer.

Organizations

Centers

Go here to read the rest:

Supercomputer – Simple English Wikipedia, the free …

Tianhe-I – Wikipedia

Tianhe-1 and Tianhe-1AActiveTianhe-1 Operational 29 October 2009, Tianhe-1A Operational 28 October 2010SponsorsNational University of Defense TechnologyOperatorsNational Supercomputing CenterLocationNational Supercomputing Center, Tianjin, People’s Republic of ChinaOperating systemLinux[1]Storage96 TB (98304 GB) for Tianhe-1,262TB for Tianhe-1ASpeedTianhe-1: 563 teraFLOPS (Rmax) 1,206.2 teraFLOPS (Rpeak),Tianhe-1A: 2,566.0 teraFLOPS (Rmax) 4,701.0 teraFLOPS (Rpeak)RankingTOP500: 2nd, June 2011 (Tianhe-1A)PurposePetroleum exploration, aircraft simulationSourcestop500.org

Tianhe-I, Tianhe-1, or TH-1 (Chinese: , [tjnxixau]; Sky River Number One)[2] is a supercomputer capable of an Rmax (maximum range) of 2.5 petaFLOPS. Located at the National Supercomputing Center of Tianjin, China, it was the fastest computer in the world from October 2010 to June 2011 and is one of the few Petascale supercomputers in the world.[3][4]

In October 2010, an upgraded version of the machine (Tianhe-1A) overtook ORNL’s Jaguar to become the world’s fastest supercomputer, with a peak computing rate of 2.57 petaFLOPS.[5][6] In June 2011 the Tianhe-1A was overtaken by the K computer as the world’s fastest supercomputer, which was also subsequently superseded.[7]

Both the original Tianhe-1 and Tianhe-1A use a Linux-based operating system.[8][9]

On 12 August 2015, the 186,368-core Tianhe-1, felt the impact of the powerful Tianjin explosions and went offline for some time. Xinhua reports that “the office building of Chinese supercomputer Tianhe-1, one of the world’s fastest supercomputers, suffered damage.” Sources at Tianhe-1 told Xinhua the computer is not damaged, but they have shut down some of its operations as a precaution.[10] Operation resumed on 17 August 2015.[11]

Tianhe-1 was developed by the Chinese National University of Defense Technology (NUDT) in Changsha, Hunan. It was first revealed to the public on 29 October 2009, and was immediately ranked as the world’s fifth fastest supercomputer in the TOP500 list released at the 2009 Supercomputing Conference (SC09) held in Portland, Oregon, on 16 November 2009. Tianhe achieved a speed of 563 teraflops in its first Top 500 test and had a peak performance of 1.2 petaflops. Thus at startup, the system had an efficiency of 46%.[12][13] Originally, Tianhe-1 was powered by 4,096 Intel Xeon E5540 processors and 1,024 Intel Xeon E5450 processors, with 5,120 AMD graphics processing units (GPUs), which were made up of 2,560 dual-GPU ATI Radeon HD 4870 X2 graphics cards.[14][15]

In October 2010, Tianhe-1A, an upgraded supercomputer, was unveiled at HPC 2010 China.[16] It is now equipped with 14,336 Xeon X5670 processors and 7,168 Nvidia Tesla M2050 general purpose GPUs. 2,048 FeiTeng 1000 SPARC-based processors are also installed in the system, but their computing power was not counted into the machine’s official Linpack statistics as of October2010.[17] Tianhe-1A has a theoretical peak performance of 4.701 petaflops.[18] NVIDIA suggests that it would have taken “50,000 CPUs and twice as much floor space to deliver the same performance using CPUs alone.” The current heterogeneous system consumes 4.04 megawatts compared to over 12 megawatts had it been built only with CPUs.[19]

The Tianhe-1A system is composed of 112 computer cabinets, 12 storage cabinets, 6 communications cabinets, and 8 I/O cabinets. Each computer cabinet is composed of four frames, with each frame containing eight blades, plus a 16-port switching board. Each blade is composed of two computer nodes, with each computer node containing two Xeon X5670 6-core processors and one Nvidia M2050 GPU processor.[20] The system has 3584 total blades containing 7168 GPUs, and 14,336 CPUs, managed by the SLURM job scheduler.[21] The total disk storage of the systems is 2 Petabytes implemented as a Lustre clustered file system,[2] and the total memory size of the system is 262 Terabytes.[17]

Another significant reason for the increased performance of the upgraded Tianhe-1A system is the Chinese-designed NUDT custom designed proprietary high-speed interconnect called Arch that runs at 160 Gbit/s, twice the bandwidth of InfiniBand.[17]

The system also used the Chinese made FeiTeng-1000 central processing unit.[22] The FeiTeng-1000 processor is used both on service nodes and to enhance the system interconnect.[22][23]

The supercomputer is installed at the National Supercomputing Center, Tianjin, and is used to carry out computations for petroleum exploration and aircraft design.[13] It is an “open access” computer, meaning it provides services for other countries.[24] The supercomputer will be available to international clients.[25]

The computer cost $88 million to build. Approximately $20 million is spent annually for electricity and operating expenses. Approximately 200 workers are employed in its operation.

Tianhe-IA was ranked as the world’s fastest supercomputer in the TOP500 list[26][27] until July 2011 when the K computer overtook it.

In June 2011, scientists at the Institute of Process Engineering (IPE) at the Chinese Academy of Sciences (CAS) announced a record-breaking scientific simulation on the Tianhe-1A supercomputer that furthers their research in solar energy. CAS-IPE scientists ran a complex molecular dynamics simulation on all 7,168 NVIDIA Tesla GPUs to achieve a performance of 1.87 petaflops (about the same performance as 130,000 laptops).[28]

The Tianhe-1A supercomputer was shut down after the National Supercomputing Center of Tianjin was damaged by an explosion nearby. The computer was not damaged and still remains operational.[29]

Follow this link:

Tianhe-I – Wikipedia

Home | National Center for Supercomputing Applications at …

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Data collection and processing infrastructure enables exploration of 400 million astronomical objects.Read more

For NCSA, 2017 was so much more than just 365 days. Read more

Blue Waters has accelerated research and impact across an enormous range of science and engineering disciplines throughout its more than 4-year history covered by the report series. This year is no different.Read more

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Supercomputer – Wikipedia

A supercomputer is a computer with a high level of performance compared to a general-purpose computer. Performance of a supercomputer is measured in floating-point operations per second (FLOPS) instead of million instructions per second (MIPS). As of 2017, there are supercomputers which can perform up to nearly a hundred quadrillions of FLOPS,[3] measured in P(eta)FLOPS.[4] As of November 2017, all of the world’s fastest 500 supercomputers run Linux-based operating systems.[5] Additional, state of the art research is being conducted in China, United States, European Union, Taiwan and Japan to build even faster, more powerful and more technologically superior exascale supercomputers.[6]

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 mechanics, weather forecasting, climate research, oil and gas exploration, molecular modeling (computing the structures and properties of chemical compounds, biological macromolecules, 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.[7]

Supercomputers were introduced in the 1960s, and for several decades the fastest were made by Seymour Cray at Control Data Corporation (CDC), Cray Research and subsequent companies bearing his name or monogram. The first such machines were highly tuned conventional designs that ran faster than their more general-purpose contemporaries. Through the 1960s, they began to add increasing amounts of parallelism with one to four processors being typical. From the 1970s, the vector computing concept with specialized math units operating on large arrays of data came to dominate. A notable example is the highly successful Cray-1 of 1976. Vector computers remained the dominant design into the 1990s. From then until today, massively parallel supercomputers with tens of thousands of off-the-shelf processors became the norm.[8][9]

The US has long been a leader in the supercomputer field, first through Cray’s almost uninterrupted dominance of the field, and later through a variety of technology companies. Japan made major strides in the field in the 1980s and 90s, but since then China has become increasingly important. As of June 2016, the fastest supercomputer on the TOP500 supercomputer list is the Sunway TaihuLight, in China, with a LINPACK benchmark score of 93PFLOPS, exceeding the previous record holder, Tianhe-2, by around 59PFLOPS. Sunway TaihuLight’s emergence is also notable for its use of indigenous chips, and is the first Chinese computer to enter the TOP500 list without using hardware from the United States. As of June 2016, China, for the first time, had more computers (167) on the TOP500 list than the United States (165). However, US built computers held ten of the top 20 positions;[10][11] as of November 2017, the U.S. has four of the top 10 and China two.

The history of supercomputing goes back to the 1960s, with the Atlas at the University of Manchester, the IBM 7030 Stretch and a series of computers at Control Data Corporation (CDC), designed by Seymour Cray. These used innovative designs and parallelism to achieve superior computational peak performance.[12]

The Atlas was a joint venture between Ferranti and the Manchester University and was designed to operate at processing speeds approaching onemicrosecond per instruction, about onemillion instructions per second.[13] 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 7094s.[14]

For the CDC 6600 (which Cray designed) released in 1964, a switch from using germanium to silicon transistors was implemented, as they could run very fast, solving the overheating problem by introducing refrigeration,[15] and helped to make it the fastest in the world. Given that the 6600 outperformed all the other contemporary computers by about 10 times, it was dubbed a supercomputer and defined the supercomputing market, when one hundred computers were sold at $8 million each.[16][17][18][19]

Cray left CDC in 1972 to form his own company, Cray Research.[17] Four years after leaving CDC, Cray delivered the 80MHz Cray 1 in 1976, and it became one of the most successful supercomputers in history.[20][21] The Cray-2 released in 1985 was an 8 processor liquid cooled computer and Fluorinert was pumped through it as it operated. It performed at 1.9 gigaFLOPS and was the world’s second fastest after M-13 supercomputer in Moscow .[22]

In 1982, Osaka University’s LINKS-1 Computer Graphics System used a massively parallel processing architecture, with 514 microprocessors, including 257 Zilog Z8001 control processors and 257 iAPX 86/20 floating-point processors. It was mainly used for rendering realistic 3D computer graphics.[23]

While the supercomputers of the 1980s used only a few processors, in the 1990s, machines with thousands of processors began to appear in Japan and the United States, setting new computational performance records. Fujitsu’s Numerical Wind Tunnel supercomputer used 166 vector processors to gain the top spot in 1994 with a peak speed of 1.7gigaFLOPS (GFLOPS) per processor.[24][25] The Hitachi SR2201 obtained a peak performance of 600GFLOPS in 1996 by using 2048 processors connected via a fast three-dimensional crossbar network.[26][27][28] 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 Passing Interface.[29]

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 designs and local parallelism to achieve superior computational performance.[12] Cray had noted that increasing processor speeds did little if the rest of the system did not also improve; the CPU would end up waiting longer for data to arrive from the offboard storage units. The CDC 6600, the first mass-produced supercomputer, solved this problem by providing ten simple computers whose only purpose was to read and write data to and from main memory, allowing the CPU to concentrate solely on processing the data. This made both the main CPU and the ten “PPU” units much simpler. As such, they were physically smaller and reduced the amount of wiring between the various parts. This reduced the electrical signaling delays and allowed the system to run at a higher clock speed. The 6600 outperformed all other machines by an average of 10 times when it was introduced.

The CDC 6600’s spot as the fastest computer was eventually replaced by its successor, the CDC 7600. This design was very similar to the 6600 in general organization but added instruction pipelining to further improve performance. Generally speaking, every computer instruction required several steps to process; first, the instruction is read from memory, then any required data it refers to is read, the instruction is processed, and the results are written back out to memory. Each of these steps is normally accomplished by separate circuitry. In most early computers, including the 6600, each of these steps runs in turn, and while any one unit is currently active, the hardware handling the other parts of the process is idle. In the 7600, as soon as one instruction cleared a particular unit, that unit began processing the next instruction. Although each instruction takes the same time to complete, there are parts of several instructions being processed at the same time, offering much-improved overall performance. This, combined with further packaging improvements and improvements in the electronics, made the 7600 about four to ten times as fast as the 6600.

The 7600 was intended to be replaced by the CDC 8600, which was essentially four 7600’s in a small box. However, this design ran into intractable problems and was eventually canceled in 1974 in favor of another CDC design, the CDC STAR-100. The STAR was essentially a simplified and slower version of the 7600, but it was combined with new circuits that could rapidly process sequences of math instructions. The basic idea was similar to the pipeline in the 7600 but geared entirely toward math, and in theory, much faster. In practice, the STAR proved to have poor real-world performance, and ultimately only two or three were built.

Cray, meanwhile, had left CDC and formed his own company. Considering the problems with the STAR, he designed an improved version of the same basic concept but replaced the STAR’s memory-based vectors with ones that ran in large registers. Combining this with his famous packaging improvements produced the Cray-1. This completely outperformed every computer in the world, save one, and would ultimately sell about 80 units, making it one of the most successful supercomputer systems in history. Through the 1970s, 80s and 90s a series of machines from Cray further improved on these basic concepts.

The basic concept of using a pipeline dedicated to processing large data units became known as vector processing, and came to dominate the supercomputer field. A number of Japanese firms also entered the field, producing similar concepts in much smaller machines. Three main lines were produced by these companies, the Fujitsu VP, Hitachi HITAC and NEC SX series, all announced in the early 1980s and updated continually into the 1990s. CDC attempted to re-enter this market with the ETA10 but this was not very successful. Convex Computer took another route, introducing a series of much smaller vector machines aimed at smaller businesses.

The only computer to seriously challenge the Cray-1’s performance in the 1970s was the ILLIAC IV. This machine was the first realized example of a true massively parallel computer, in which many processors worked together to solve different parts of a single larger problem. In contrast with the vector systems, which were designed to run a single stream of data as quickly as possible, in this concept, the computer instead feeds separate parts of the data to entirely different processors and then recombines the results. The ILLIAC’s design was finalized in 1966 with 256 processors and offer speed up to 1 GFLOPS, compared to the 1970s Cray-1’s peak of 250 MFLOPS. However, development problems led to only 64 processors being built, and the system could never operate faster than about 200 MFLOPS, while being much larger and more complex than the Cray. Another problem was that writing software for the system was difficult, and getting peak performance from it was a matter of serious effort.

But the partial success of the ILLIAC IV was widely seen as pointing the way to the future of supercomputing. Cray argued against this, famously quipping that “If you were plowing a field, which would you rather use? Two strong oxen or 1024 chickens?”[30] But by the early 1980s, several teams were working on parallel designs with thousands of processors, notably the Connection Machine (CM) that developed from research at MIT. The CM-1 used as many as 65,536 simplified custom microprocessors connected together in a network to share data. Several updated versions followed; the CM-5 supercomputer is a massively parallel processing computer capable of many billions of arithmetic operations per second.[31]

Software development remained a problem, but the CM series sparked off considerable research into this issue. Similar designs using custom hardware were made by many companies, including the Evans & Sutherland ES-1, MasPar, nCUBE, Intel iPSC and the Goodyear MPP. But by the mid-1990s, general-purpose CPU performance had improved so much in that a supercomputer could be built using them as the individual processing units, instead of using custom chips. By the turn of the 21st century, designs featuring tens of thousands of commodity CPUs were the norm, with later machines adding graphic units to the mix.[8][9]

Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers.[32][33][34] The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components.[35] 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 conditioning temperatures.[36][37]

Systems with a massive number of processors generally take one of two paths. In the grid computing approach, the processing power of many computers, organised as distributed, diverse administrative domains, is opportunistically used whenever a computer is available.[38] In another approach, a large number of processors are used in proximity to each other, e.g. in a computer 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.[39][40] The use of multi-core processors combined with centralization is an emerging direction, e.g. as in the Cyclops64 system.[41][42]

As the price, performance and energy efficiency of general purpose graphic processors (GPGPUs) have improved,[43] a number of petaFLOPS supercomputers such as Tianhe-I and Nebulae have started to rely on them.[44] 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 may be 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.[45][46] However, GPUs are gaining ground and in 2012 the Jaguar supercomputer was transformed into Titan by retrofitting CPUs with GPUs.[47][48][49]

High performance computers have an expected life cycle of about three years before requiring an upgrade.[50]

A number of “special-purpose” systems have been designed, dedicated to a single problem. This allows the use of specially programmed FPGA chips or even custom ASICs, allowing better price/performance ratios by sacrificing generality. Examples of special-purpose supercomputers include Belle,[51] Deep Blue,[52] and Hydra,[53] for playing chess, Gravity Pipe for astrophysics,[54] MDGRAPE-3 for protein structure computation molecular dynamics[55] and Deep Crack,[56] 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 4.04megawatts (MW) of electricity.[57] 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.[58] 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.[59][60][61]

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.[36] 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 the Liebert company.[37]

In the Blue Gene system, IBM deliberately used low power processors to deal with heat density.[62] The IBM Power 775, released in 2011, has closely packed elements that require water cooling.[63] The IBM Aquasar system uses hot water cooling to achieve energy efficiency, the water being used to heat buildings as well.[64][65]

The energy efficiency of computer systems is generally measured in terms of “FLOPS per watt”. In 2008, IBM’s Roadrunner operated at 3.76MFLOPS/W.[66][67] In November 2010, the Blue Gene/Q reached 1,684MFLOPS/W.[68][69] In June 2011 the top 2 spots on the Green 500 list were occupied by Blue Gene machines in New York (one achieving 2097MFLOPS/W) with the DEGIMA cluster in Nagasaki placing third with 1375MFLOPS/W.[70]

Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat,[71] the ability of the cooling systems to remove waste heat is a limiting factor.[72][73] As of 2015[update], many existing supercomputers have more infrastructure capacity than the actual peak demand of the machine designers generally conservatively design the power and cooling infrastructure to handle more than the theoretical peak electrical power consumed by the supercomputer. Designs for future supercomputers are power-limited the thermal design power of the supercomputer as a whole, the amount that the power and cooling infrastructure can handle, is somewhat more than the expected normal power consumption, but less than the theoretical peak power consumption of the electronic hardware.[74]

Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes in supercomputer architecture.[75] 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.[76]

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.[77][78][79]

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 deal with inevitable hardware failures when tens of thousands of processors are present.[80]

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 design.[75][81]

The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed. Software tools for distributed processing include standard APIs such as MPI and PVM, VTL, and open 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 or OpenCL.

Moreover, it is quite difficult to debug and test parallel programs. Special techniques need to be used for testing and debugging such applications.

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 parallel problems 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). F@h reported 101 PFLOPS of x86 processing power As of October2016[update]. Of this, over 100 PFLOPS are contributed by clients running on various GPUs, and the rest from various CPU systems.[83]

The Berkeley Open Infrastructure for Network Computing (BOINC) platform hosts a number of distributed computing projects. As of February2017[update], BOINC recorded a processing power of over 166 PetaFLOPS through over 762 thousand active Computers (Hosts) on the network.[84]

As of October2016[update], Great Internet Mersenne Prime Search’s (GIMPS) distributed Mersenne Prime search achieved about 0.313 PFLOPS through over 1.3 million computers.[85] The Internet PrimeNet Server supports GIMPS’s grid computing approach, one of the earliest and most successful[citation needed] grid computing projects, since 1997.

Quasi-opportunistic supercomputing is a form of distributed computing whereby the super virtual computer of many networked geographically disperse computers performs computing tasks that demand huge processing power.[86] 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.[86]

Cloud Computing with its recent and rapid expansions and development have grabbed the attention of HPC users and developers in recent years. Cloud Computing attempts to provide HPC-as-a-Service exactly like other forms of services currently available in the Cloud such as Software-as-a-Service, Platform-as-a-Service, and Infrastructure-as-a-Service. HPC users may benefit from the Cloud in different angles such as scalability, resources being on-demand, fast, and inexpensive. On the other hand, moving HPC applications have a set of challenges too. Good examples of such challenges are virtualization overhead in the Cloud, multi-tenancy of resources, and network latency issues. Much research[87][88][89][90] is currently being done to overcome these challenges and make HPC in the cloud a more realistic possibility.

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 simulation application.[91]

Capacity computing, in contrast, is typically thought of as using efficient cost-effective computing power to solve a few somewhat large problems or many small problems.[91] 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 complex problem.[91]

In general, the speed of supercomputers is measured and benchmarked in “FLOPS” (FLoating point Operations Per Second), and not in terms of “MIPS” (Million Instructions Per Second), as is the case with general-purpose computers.[92] 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 (EFLOPS) range. An EFLOPS is one quintillion (1018) FLOPS (one million TFLOPS).

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.[93] 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.[94] The LINPACK benchmark typically performs LU decomposition of a large matrix.[95] 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.[93]

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,[96] and the “Peak speed” is given as the “Rmax” rating.

Source: TOP500

The stages of supercomputer application may be summarized in the following table:

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.[103]

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.[104]

In 2011, the challenges and difficulties in pushing the envelope in supercomputing were underscored by IBM’s abandonment of the Blue Waters petascale project.[105]

The Advanced Simulation and Computing Program currently uses supercomputers to maintain and simulate the United States nuclear stockpile.[106]

Given the current speed of progress, industry experts estimate that supercomputers will reach 1EFLOPS (1018, 1,000 PFLOPS or one quintillion FLOPS) by 2018. The Chinese government in particular is pushing to achieve this goal after they achieved the most powerful supercomputer in the world with Tianhe-2 since 2013. Using the Intel MIC multi-core processor architecture, which is Intel’s response to GPU systems, SGI also plans to achieve a 500-fold increase in performance by 2018 in order to achieve one EFLOPS. Samples of MIC chips with 32 cores, which combine vector processing units with standard CPU, have become available.[107] The Indian government has also stated ambitions for an EFLOPS-range supercomputer, which they hope to complete by 2017.[108] In November 2014, it was reported that India is working on the fastest supercomputer ever, which is set to work at 132EFLOPS.[109]

Erik P. DeBenedictis of Sandia National Laboratories theorizes that a zettaFLOPS (1021, one sextillion FLOPS) computer is required to accomplish full weather modeling, which could cover a two-week time span accurately.[110][111][112] Such systems might be built around 2030.[113]

Many Monte Carlo simulations use the same algorithm to process a randomly generated data set; particularly, integro-differential equations describing physical transport processes, the random paths, collisions, and energy and momentum depositions of neutrons, photons, ions, electrons, etc. The next step for microprocessors may be into the third dimension; and specializing to Monte Carlo, the many layers could be identical, simplifying the design and manufacture process.[114]

High performance supercomputers usually require high energy, as well. However, Iceland may be a benchmark for the future with the world’s first zero-emission supercomputer. Located at the Thor Data Center in Reykjavik, Iceland, this supercomputer relies on completely renewable sources for its power rather than fossil fuels. The colder climate also reduces the need for active cooling, making it one of the greenest facilities in the world.[115]

Many science-fiction writers have depicted supercomputers in their works, both before and after the historical construction of such computers. Much of such fiction deals with the relations of humans with the computers they build and with the possibility of conflict eventually developing between them. Some scenarios of this nature appear on the AI-takeover page.

Examples of supercomputers in fiction include HAL-9000, Multivac, The Machine Stops, GLaDOS, The Evitable Conflict and Vulcan’s Hammer.

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Supercomputer – Wikipedia

TOP500 – Official Site

Michael Feldman | February 9, 2018 01:27 CET Tech startup Sylabs Inc. has emerged from stealth mode and released Singularity Pro, the enterprise version of the popular container platform for high performance computing. Michael Feldman | February 6, 2018 01:39 CET Ampere Computing, a chip startup headed by former Intel president Renee James, emerged from stealth mode this week, announcing it will be delivering a 64-bit ARM processor for the hyperscale market. Michael Feldman | February 1, 2018 16:11 CET The popularity of NVIDIAs Tesla V100 GPUs got another boost this week with IBMs announcement that it has added them to its cloud offerings. Michael Feldman | January 30, 2018 19:21 CET Samsung Electronics has released its first-generation Z-SSD product, a high-performance solid state drive that is meant to compete with Intels Optane SSDs. Michael Feldman | January 29, 2018 12:06 CET New Zealand-based storage start-up Nyriad has teamed up with Netlist and HPC Systems to develop a Linux storage platform accelerated by GPUs and NVDIMMs. Michael Feldman | January 23, 2018 21:22 CET Forschungszentrum Jlich has contractedAtos to build and deploy a 12-petaflop supercomputer, which, when operational, will be the most powerful system in Germany. Michael Feldman | January 22, 2018 22:21 CET Chip manufacturer Taiwan Semiconductor Manufacturing Company (TSMC) reported double-digit growth for its HPC segment in 2017 and expects that trend to continue through 2018. Michael Feldman | January 19, 2018 15:32 CET Eni S.p.A., an Italian oil and gas multinational, has deployed an 18.6-petaflop supercomputer, making it the most powerful commercially-owned system in world. Michael Feldman | January 18, 2018 13:57 CET The Ministry of Earth Sciences (MoES) in India has deployed the countrys two fastest supercomputers, which will be used to conduct earth science research and improve weather and climate forecasts. Michael Feldman | January 17, 2018 15:28 CET Microsoft and Alibaba have independently developed AI models that scored better than humans in a Stanford University reading comprehension test. More News

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TOP500 – Official Site

What is supercomputer? – Definition from WhatIs.com

A supercomputer is a computer that performs at or near the currently highest operational rate for computers. Traditionally, supercomputers have been used for scientific and engineering applications that must handle very large databases or do a great amount of computation (or both).Although advances likemulti-core processors and GPGPUs (general-purpose graphics processing units)have enabled powerful machinesfor personal use (see: desktop supercomputer, GPU supercomputer),by definition, a supercomputer is exceptional in terms of performance.

At any given time, there are a few well-publicized supercomputers that operate at extremely high speeds relative to all other computers. The term is also sometimes applied to far slower (but still impressively fast) computers. The largest, most powerful supercomputers are really multiple computers that perform parallel processing. In general, there are two parallel processing approaches: symmetric multiprocessing (SMP) and massively parallel processing (MPP).

As of June 2016, the fastest supercomputer in the world was the Sunway TaihuLight, in the city of Wixu in China. A few statistics on TaihuLight:

The first commercially successful supercomputer, the CDC (Control Data Corporation) 6600 was designed by Seymour Cray. Released in 1964, the CDC 6600 had a single CPU and cost $8 million the equivalent of $60 million today. The CDC could handle three million floating point operations per second (flops).

Cray went on to found a supercomputer company under his name in 1972. Although the company has changed hands a number of times it is still in operation. In September 2008, Cray and Microsoft launched CX1, a $25,000 personal supercomputer aimed at markets such as aerospace, automotive, academic, financial services and life sciences.

IBM has been a keen competitor. The company’s Roadrunner, once the top-ranked supercomputer, was twice as fast as IBM’s Blue Gene and six times as fast as any of other supercomputers at that time. IBM’s Watson is famous for having adopted cognitive computing to beat champion Ken Jennings on Jeopardy!, a popular quiz show.

Year

Supercomputer

Peak speed (Rmax)

Location

2016

Sunway TaihuLight

93.01PFLOPS

Wuxi, China

2013

NUDTTianhe-2

33.86PFLOPS

Guangzhou, China

2012

CrayTitan

17.59PFLOPS

Oak Ridge, U.S.

2012

IBMSequoia

17.17PFLOPS

Livermore, U.S.

2011

FujitsuK computer

10.51PFLOPS

Kobe, Japan

2010

Tianhe-IA

2.566PFLOPS

Tianjin, China

2009

CrayJaguar

1.759PFLOPS

Oak Ridge, U.S.

2008

IBMRoadrunner

1.026PFLOPS

Los Alamos, U.S.

1.105PFLOPS

In the United States, some supercomputer centers are interconnected on an Internet backbone known as vBNS or NSFNet. This network is the foundation for an evolving network infrastructure known as the National Technology Grid. Internet2 is a university-led project that is part of this initiative.

At the lower end of supercomputing, clustering takes more of a build-it-yourself approach to supercomputing. The Beowulf Project offers guidance on how to put together a number of off-the-shelf personal computer processors, using Linux operating systems, and interconnecting the processors with Fast Ethernet. Applications must be written to manage the parallel processing.

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What is supercomputer? – Definition from WhatIs.com

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Michael Feldman | February 6, 2018 01:39 CET Ampere Computing, a chip startup headed by former Intel president Renee James, emerged from stealth mode this week, announcing it will be delivering a 64-bit ARM processor for the hyperscale market. Michael Feldman | February 1, 2018 16:11 CET The popularity of NVIDIAs Tesla V100 GPUs got another boost this week with IBMs announcement that it has added them to its cloud offerings. Michael Feldman | January 30, 2018 19:21 CET Samsung Electronics has released its first-generation Z-SSD product, a high-performance solid state drive that is meant to compete with Intels Optane SSDs. Michael Feldman | January 29, 2018 12:06 CET New Zealand-based storage start-up Nyriad has teamed up with Netlist and HPC Systems to develop a Linux storage platform accelerated by GPUs and NVDIMMs. Michael Feldman | January 23, 2018 21:22 CET Forschungszentrum Jlich has contractedAtos to build and deploy a 12-petaflop supercomputer, which, when operational, will be the most powerful system in Germany. Michael Feldman | January 22, 2018 22:21 CET Chip manufacturer Taiwan Semiconductor Manufacturing Company (TSMC) reported double-digit growth for its HPC segment in 2017 and expects that trend to continue through 2018. Michael Feldman | January 19, 2018 15:32 CET Eni S.p.A., an Italian oil and gas multinational, has deployed an 18.6-petaflop supercomputer, making it the most powerful commercially-owned system in world. Michael Feldman | January 18, 2018 13:57 CET The Ministry of Earth Sciences (MoES) in India has deployed the countrys two fastest supercomputers, which will be used to conduct earth science research and improve weather and climate forecasts. Michael Feldman | January 17, 2018 15:28 CET Microsoft and Alibaba have independently developed AI models that scored better than humans in a Stanford University reading comprehension test. Michael Feldman | January 16, 2018 15:24 CET The US Department of Energy (DOE) is investing 1.87 million in seven projects intended to advance manufacturing using high performance computing. More News

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Supercomputer – Simple English Wikipedia, the free …

A supercomputer is a computer with great speed and memory. This kind of computer can do jobs faster than any other computer of its generation. They are usually thousands of times faster than ordinary personal computers made at that time. Supercomputers can do arithmetic jobs very fast, so they are used for weather forecasting, code-breaking, genetic analysis and other jobs that need many calculations. When new computers of all classes become more powerful, new ordinary computers are made with powers that only supercomputers had in the past, while new supercomputers continue to outclass them.

Electrical engineers make supercomputers that link many thousands of microprocessors.

Supercomputer types include: shared memory, distributed memory and array. Supercomputers with shared memory are developed by using a parallel computing and pipelining concept. Supercomputers with distributed memory consist of many (about 100~10000) nodes. CRAY series of CRAYRESERCH and VP 2400/40, NEC SX-3 of HUCIS are shared memory types. nCube 3, iPSC/860, AP 1000, NCR 3700, Paragon XP/S, CM-5 are distributed memory types.

An array type computer named ILIAC started working in 1972. Later, the CF-11, CM-2, and the Mas Par MP-2 (which is also an array type) were developed. Supercomputers that use a physically separated memory as one shared memory include the T3D, KSR1, and Tera Computer.

Organizations

Centers

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Supercomputer – Simple English Wikipedia, the free …

History of supercomputing – Wikipedia

The history of supercomputing goes back to the early 1920s in the United States with the IBM tabulators at Columbia University and a series of computers at Control Data Corporation (CDC), designed by Seymour Cray to use innovative designs and parallelism to achieve superior computational peak performance.[1] The CDC 6600, released in 1964, is generally considered the first supercomputer.[2][3]

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.

By the end of the 20th century, massively parallel supercomputers with thousands of “off-the-shelf” processors similar to those found in personal computers were constructed and broke through the teraflop computational barrier.

Progress in the first decade of the 21st century was dramatic and supercomputers with over 60,000 processors appeared, reaching petaflop performance levels.

The term “Super Computing” was first used in the New York World in 1929 to refer to large custom-built tabulators that IBM had made for Columbia University.

In 1957 a group of engineers left Sperry Corporation to form Control Data Corporation (CDC) in Minneapolis, MN. Seymour Cray left Sperry a year later to join his colleagues at CDC.[1] In 1960 Cray completed the CDC 1604, one of the first solid state computers, and the fastest computer in the world[dubious discuss] at a time when vacuum tubes were found in most large computers.[4]

Around 1960 Cray decided to design a computer that would be the fastest in the world by a large margin. After four years of experimentation along with Jim Thornton, and Dean Roush and about 30 other engineers Cray completed the CDC 6600 in 1964. Cray switched from germanium to silicon transistors, built by Fairchild Semiconductor, that used the planar process. These did not have the drawbacks of the mesa silicon transistors. He ran them very fast, and the speed of light restriction forced a very compact design with severe overheating problems, which were solved by introducing refrigeration, designed by Dean Roush.[5] 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 million each.[4][6]

The 6600 gained speed by “farming out” work to peripheral computing elements, freeing the CPU (Central Processing Unit) to process actual data. The Minnesota FORTRAN compiler for the machine was developed by Liddiard and Mundstock at the University of Minnesota and with it the 6600 could sustain 500kiloflops on standard mathematical operations.[7] In 1968 Cray completed the CDC 7600, again the fastest computer in the world.[4] At 36MHz, the 7600 had about three and a half times the clock speed of the 6600, but ran significantly faster due to other technical innovations. They sold only about 50 of the 7600s, not quite a failure. Cray left CDC in 1972 to form his own company.[4] Two years after his departure CDC delivered the STAR-100 which at 100megaflops was three times the speed of the 7600. Along with the Texas Instruments ASC, the STAR-100 was one of the first machines to use vector processing – the idea having been inspired around 1964 by the APL programming language.[8][9]

In 1956, a team at Manchester University in the United Kingdom, began development of MUSE a name derived from microsecond engine with the aim of eventually building a computer that could operate at processing speeds approaching onemicrosecond per instruction, about onemillion instructions per second.[10] Mu (or ) is a prefix in the SI and other systems of units denoting a factor of 106 (one millionth).

At the end of 1958 Ferranti agreed to begin to collaborate with Manchester University on the project, and the computer was shortly afterwards renamed Atlas, with the joint venture under the control of Tom Kilburn. The first Atlas was officially commissioned on 7December 1962, nearly three years before the Cray CDC 6600 supercomputer was introduced, as one of the world’s first supercomputers – and was considered to be the most powerful computer in England and for a very short time was considered to be one of the most powerful computers in the world, and equivalent to four IBM 7094s.[11] It was said that whenever England’s Atlas went offline half of the United Kingdom’s computer capacity was lost.[11] The Atlas Computer pioneered the use of virtual memory and paging as a way to extend the Atlas Computer’s working memory by combining its 16 thousand words of primary core memory with an additional 96 thousand words of secondary drum memory.[12] Atlas also pioneered the Atlas Supervisor, “considered by many to be the first recognizable modern operating system”.[11]

Four years after leaving CDC, Cray delivered the 80MHz Cray 1 in 1976, and it became the most successful supercomputer in history.[9][13] The Cray 1 used integrated circuits with two gates per chip and was a vector processor which introduced a number of innovations such as chaining in which scalar and vector registers generate interim results which can be used immediately, without additional memory references which reduce computational speed.[5][14] The Cray X-MP (designed by Steve Chen) was released in 1982 as a 105MHz shared-memory parallel vector processor with better chaining support and multiple memory pipelines. All three floating point pipelines on the X-MP could operate simultaneously.[14]

The Cray-2 released in 1985 was a 4processor liquid cooled computer totally immersed in a tank of Fluorinert, which bubbled as it operated.[15] It could perform to 1.9gigaflops and was the world’s second fastest supercomputer after M-13 (2.4gigaflops)[16] until 1990 when ETA-10G from CDC overtook both. The Cray 2 was a totally new design and did not use chaining and had a high memory latency, but used much pipelining and was ideal for problems that required large amounts of memory.[14] The software costs in developing a supercomputer should not be underestimated, as evidenced by the fact that in the 1980s the cost for software development at Cray came to equal what was spent on hardware.[17] That trend was partly responsible for a move away from the in-house, Cray Operating System to UNICOS based on Unix.[17]

The Cray Y-MP, also designed by Steve Chen, was released in 1988 as an improvement of the X-MP and could have eight vector processors at 167MHz with a peak performance of 333megaflops per processor.[14] In the late 1980s, Cray’s experiment on the use of gallium arsenide semiconductors in the Cray-3 did not succeed. Cray began to work on a massively parallel computer in the early 1990s, but died in a car accident in 1996 before it could be completed. Cray Research did, however, produce such computers.[13][15]

The Cray-2 which set the frontiers of supercomputing in the mid to late 1980s had only 8 processors. In the 1990s, supercomputers with thousands of processors began to appear. Another development at the end of the 1980s was the arrival of Japanese supercomputers, some of which were modeled after the Cray-1.

The SX-3/44R was announced by NEC Corporation in 1989 and a year later earned the fastest in the world title with a 4 processor model.[18] However, Fujitsu’s Numerical Wind Tunnel supercomputer used 166 vector processors to gain the top spot in 1994. It had a peak speed of 1.7gigaflops per processor.[19][20] The Hitachi SR2201 on the other hand obtained a peak performance of 600gigaflops in 1996 by using 2048processors connected via a fast three-dimensional crossbar network.[21][22][23]

In the same timeframe 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 Passing Interface.[24] By 1995 Cray was also shipping massively parallel systems, e.g. the Cray T3E with over 2,000 processors, using a three-dimensional torus interconnect.[25][26]

The Paragon architecture soon led to the Intel ASCI Red supercomputer in the United States, which held the top supercomputing spot to the end of the 20th century as part of the Advanced Simulation and Computing Initiative. This was also a mesh-based MIMD massively-parallel system with over 9,000 compute nodes and well over 12 terabytes of disk storage, but used off-the-shelf Pentium Pro processors that could be found in everyday personal computers. ASCI Red was the first system ever to break through the 1teraflop barrier on the MP-Linpack benchmark in 1996; eventually reaching 2teraflops.[27]

Significant progress was made in the first decade of the 21st century. The efficiency of supercomputers continued to increase, but not dramatically so. The Cray C90 used 500 kilowatts of power in 1991, while by 2003 the ASCI Q used 3,000kW while being 2,000 times faster, increasing the performance per watt 300 fold.[28]

In 2004, the Earth Simulator supercomputer built by NEC at the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) reached 35.9teraflops, using 640nodes, each with eight proprietary vector processors.[29]

The IBM Blue Gene supercomputer architecture found widespread use in the early part of the 21st century, and 27 of the computers on the TOP500 list used that architecture. The Blue Gene approach is somewhat different in that it trades processor speed for low power consumption so that a larger number of processors can be used at air cooled temperatures. It can use over 60,000 processors, with 2048 processors “per rack”, and connects them via a three-dimensional torus interconnect.[30][31]

Progress in China has been rapid, in that China placed 51st on the TOP500 list in June 2003, then 14th in November 2003, and 10th in June 2004 and then 5th during 2005, before gaining the top spot in 2010 with the 2.5petaflop Tianhe-I supercomputer.[32][33]

In July 2011, the 8.1petaflop Japanese K computer became the fastest in the world using over 60,000 SPARC64 VIIIfx processors housed in over 600 cabinets. The fact that K computer is over 60 times faster than the Earth Simulator, and that the Earth Simulator ranks as the 68th system in the world seven years after holding the top spot demonstrates both the rapid increase in top performance and the widespread growth of supercomputing technology worldwide.[34][35][36]

This is a list of the computers which appeared at the top of the Top500 list since 1993.[37] The “Peak speed” is given as the “Rmax” rating.

Combined performance of 500 largest supercomputers

Fastest supercomputer

Supercomputer on 500th place

The CoCom and its later replacement, the Wassenaar Arrangement, legally regulated – required licensing and approval and record-keeping; or banned entirely – the export of high-performance computers (HPCs) to certain countries. Such controls have become harder to justify, leading to loosening of these regulations. Some have argued these regulations were never justified.[38][39][40][41][42][43]

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History of supercomputing – Wikipedia

What is supercomputer? – Definition from WhatIs.com

A supercomputer is a computer that performs at or near the currently highest operational rate for computers. Traditionally, supercomputers have been used for scientific and engineering applications that must handle very large databases or do a great amount of computation (or both).Although advances likemulti-core processors and GPGPUs (general-purpose graphics processing units)have enabled powerful machinesfor personal use (see: desktop supercomputer, GPU supercomputer),by definition, a supercomputer is exceptional in terms of performance.

At any given time, there are a few well-publicized supercomputers that operate at extremely high speeds relative to all other computers. The term is also sometimes applied to far slower (but still impressively fast) computers. The largest, most powerful supercomputers are really multiple computers that perform parallel processing. In general, there are two parallel processing approaches: symmetric multiprocessing (SMP) and massively parallel processing (MPP).

As of June 2016, the fastest supercomputer in the world was the Sunway TaihuLight, in the city of Wixu in China. A few statistics on TaihuLight:

The first commercially successful supercomputer, the CDC (Control Data Corporation) 6600 was designed by Seymour Cray. Released in 1964, the CDC 6600 had a single CPU and cost $8 million the equivalent of $60 million today. The CDC could handle three million floating point operations per second (flops).

Cray went on to found a supercomputer company under his name in 1972. Although the company has changed hands a number of times it is still in operation. In September 2008, Cray and Microsoft launched CX1, a $25,000 personal supercomputer aimed at markets such as aerospace, automotive, academic, financial services and life sciences.

IBM has been a keen competitor. The company’s Roadrunner, once the top-ranked supercomputer, was twice as fast as IBM’s Blue Gene and six times as fast as any of other supercomputers at that time. IBM’s Watson is famous for having adopted cognitive computing to beat champion Ken Jennings on Jeopardy!, a popular quiz show.

Year

Supercomputer

Peak speed (Rmax)

Location

2016

Sunway TaihuLight

93.01PFLOPS

Wuxi, China

2013

NUDTTianhe-2

33.86PFLOPS

Guangzhou, China

2012

CrayTitan

17.59PFLOPS

Oak Ridge, U.S.

2012

IBMSequoia

17.17PFLOPS

Livermore, U.S.

2011

FujitsuK computer

10.51PFLOPS

Kobe, Japan

2010

Tianhe-IA

2.566PFLOPS

Tianjin, China

2009

CrayJaguar

1.759PFLOPS

Oak Ridge, U.S.

2008

IBMRoadrunner

1.026PFLOPS

Los Alamos, U.S.

1.105PFLOPS

In the United States, some supercomputer centers are interconnected on an Internet backbone known as vBNS or NSFNet. This network is the foundation for an evolving network infrastructure known as the National Technology Grid. Internet2 is a university-led project that is part of this initiative.

At the lower end of supercomputing, clustering takes more of a build-it-yourself approach to supercomputing. The Beowulf Project offers guidance on how to put together a number of off-the-shelf personal computer processors, using Linux operating systems, and interconnecting the processors with Fast Ethernet. Applications must be written to manage the parallel processing.

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What is supercomputer? – Definition from WhatIs.com


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