Supercomputer

From Wikipedia, the free encyclopedia
Jump to: navigation, search
"High-performance computing" redirects here. For narrower definitions of HPC, see high-throughput computing and many-task computing. For other uses, see Supercomputer (disambiguation).

The Blue Gene/P supercomputer at Argonne National Lab runs over 250,000 processors using normal data center air conditioning, grouped in 72 racks/cabinets connected by a high-speed optical network[1]
A supercomputer is a computer at the frontline of contemporary processing capacity – particularly speed of calculation.
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 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.[2][3] As of June 2013, China's Tianhe-2 supercomputer is the fastest in the world at 33.86 petaFLOPS.
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.[4][5] 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 cloud.[6][7]
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 counter-cryptography.[8]

History

Main article: History of supercomputing

A Cray-1 preserved at the Deutsches Museum
The history of supercomputing goes back to the 1960s when a series of computers at Control Data Corporation (CDC) were designed by Seymour Cray to use innovative designs and parallelism to achieve superior computational peak performance.[9] The CDC 6600, released in 1964, is generally considered the first supercomputer.[10][11]
Cray left CDC in 1972 to form his own company.[12] 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.[13][14] 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 fastest until 1990.[15]
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 Tunnel supercomputer used 166 vector processors to gain the top spot in 1994 with a peak speed of 1.7 gigaflops per processor.[16][17] The Hitachi SR2201 obtained a peak performance of 600 gigaflops in 1996 by using 2048 processors connected via a fast three dimensional crossbar network.[18][19][20] 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.[21]

Hardware and architecture

Main articles: Supercomputer architecture and Parallel computer hardware

A Blue Gene/L cabinet showing the stacked blades, each holding many processors
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.[9] However, in time the demand for increased computational power ushered in the age of massively parallel systems.
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.[2][3]
Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers.[22][23][24] The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components.[25] 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.[15][26]

The CPU share of TOP500
Systems with a massive number of processors generally take one of two paths: in one approach, known as grid computing, the processing power of a large number of computers in distributed, diverse administrative domains, is opportunistically used whenever a computer is available.[4] In another approach, a large number of processors are used in close 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.[27][28] The use of multi-core processors combined with centralization is an emerging direction, e.g. as in the Cyclops64 system.[6][7]
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.[29] 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.[30] However, GPUs are gaining ground and in 2012 the Jaguar supercomputer was transformed into Titan by replacing CPUs with GPUs.[31][32][33]
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 VLSI chips, allowing better price/performance ratios by sacrificing generality. Examples of special-purpose supercomputers include Belle,[34] Deep Blue,[35] and Hydra,[36] for playing chess, Gravity Pipe for astrophysics,[37] MDGRAPE-3 for protein structure computation molecular dynamics[38] and Deep Crack,[39] for breaking the DES cipher.

Energy usage and heat management

See also: Computer cooling and Green 500
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.[40] 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.

An IBM HS20 blade
Heat management is a major issue in complex electronic devices, and affects powerful computer systems in various ways.[41] 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.[42] [43][44]
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.[15] 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.[26]
In the Blue Gene system IBM deliberately used low power processors to deal with heat density.[45] On the other hand, the IBM Power 775, released in 2011, has closely packed elements that require water cooling.[46] 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.[47][48]
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.[49][50] In November 2010, the Blue Gene/Q reached 1684 MFLOPS/Watt.[51][52] In June 2011 the top 2 spots on the Green 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.[53]

Software and system management

Operating systems

Main article: Supercomputer operating systems
Since the end of the 20th century, supercomputer operating systems have undergone major transformations, as sea changes have taken place in supercomputer architecture.[54] 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.[55]
Given that 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.[56][57][58]
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.[59]
Although most modern supercomputers use the Linux operating system, each manufacturer has made its own specific changes to the Linux-derivative they use, 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.[54][60]

Software tools and message passing

Main article: Message passing in computer clusters
See also: Parallel computing and Parallel programming model

Wide-angle view of the ALMA correlator.[61]
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.
Moreover, it is quite difficult to debug and test parallel programs. Special techniques need to be used for testing and debugging such applications.

Distributed supercomputing

Opportunistic approaches

Main article: Grid computing

Example architecture of a grid computing system connecting many personal computers over the internet
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 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.[62]
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[63] The most active project (measured by computational power), MilkyWay@home, reports processing power of over 700 teraflops through over 33,000 active computers.[64]
As of May 2011, GIMPS's distributed Mersenne Prime search currently achieves about 60 teraflops through over 25,000 registered computers.[65] The Internet PrimeNet Server supports GIMPS's grid computing approach, one of the earliest and most successful grid computing projects, since 1997.
In 2012, Simon Cox made a supercomputer consisting of 64 Raspberry Pi units. The system was called Iridis-Pi. The whole system cost under £2,500 (excluding switches) and had a total of 1Tb of memory (16Gb SD cards for each Raspberry Pi).[66]

Quasi-opportunistic approaches

Main article: Quasi-opportunistic supercomputing
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.[67] 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.[67]

Performance measurement

Capability vs capacity

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.[68]
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.[68] 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.[68]

Performance metrics

See also: LINPACK benchmarks

Top supercomputer speeds: logscale speed over 60 years
In general, the speed of supercomputers is measured and benchmarked in "FLOPS" (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.[69] 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.[70] 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.[70]

The TOP500 list

Main article: TOP500

Pie Chart of Supercomputer Countries Share as of Nov 2012
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,[71] and the "Peak speed" is given as the "Rmax" rating. For more historical data see History of supercomputing.
Year Supercomputer Peak speed
(Rmax)		 Location
2008 IBM Roadrunner 1.026 PFLOPS Los Alamos, USA
1.105 PFLOPS
2009 Cray Jaguar 1.759 PFLOPS Oak Ridge, USA
2010 Tianhe-IA 2.566 PFLOPS Tianjin, China
2011 Fujitsu K computer 10.51 PFLOPS Kobe, Japan
2012 Cray Titan 17.59 PFLOPS Oak Ridge, USA
2013 NUDT Tianhe-2 33.86 PFLOPS Guangzhou, China

Applications of supercomputers

The stages of supercomputer application may be summarized in the following table:
Decade Uses and computer involved
1970s Weather forecasting, aerodynamic research (Cray-1).[72]
1980s Probabilistic analysis,[73] radiation shielding modeling[74] (CDC Cyber).
1990s Brute force code breaking (EFF DES cracker),[75]
2000s 3D nuclear test simulations as a substitute for legal conduct Nuclear Non-Proliferation Treaty (ASCI Q).[76]
2010s Molecular Dynamics Simulation (Tianhe-1A)[77]
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.[78]
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.[79]
In 2011, the challenges and difficulties in pushing the envelope in supercomputing were underscored by IBM's abandonment of the Blue Waters petascale project.[80]

Research and development trends


Diagram of a 3 dimensional torus interconnect used by systems such as Blue Gene, Cray XT3, etc.
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.[81] Using the Intel 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.[82] The Indian government has also stated ambitions for an exaflop range supercomputer, which they hope to complete by 2017.[83]
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.[84] Such systems might be built around 2030.[85]

See also

Wikimedia Commons has media related to Supercomputers.

References

  1. Jump up ^ "IBM Blue gene announcement". 03.ibm.com. 2007-06-26. Retrieved 2012-06-09.
  2. ^ Jump up to: a b Hoffman, Allan R.; et al. (1990). Supercomputers: directions in technology and applications. National Academies. pp. 35–47. ISBN 0-309-04088-4.
  3. ^ Jump up to: a b Hill, Mark Donald; Jouppi, Norman Paul; Sohi, Gurindar (1999). Readings in computer architecture. pp. 40–49. ISBN 1-55860-539-8.
  4. ^ Jump up to: a b Prodan, Radu; Fahringer, Thomas (2007). Grid computing: experiment management, tool integration, and scientific workflows. pp. 1–4. ISBN 3-540-69261-4.
  5. Jump up ^ [url=http://boinc.berkeley.edu/trac/wiki/DesktopGrid]
  6. ^ Jump up to: a b Performance Modelling and Optimization of Memory Access on Cellular Computer Architecture Cyclops64 K Barner, GR Gao, Z Hu, Lecture Notes in Computer Science, 2005, Volume 3779, Network and Parallel Computing, Pages 132-143
  7. ^ Jump up to: a b Analysis and performance results of computing betweenness centrality on IBM Cyclops64 by Guangming Tan, Vugranam C. Sreedhar and Guang R. Gao The Journal of Supercomputing Volume 56, Number 1, 1–24 September 2011
  8. Jump up ^ [url=http://odenton.patch.com/articles/nsa-breaks-ground-on-massive-computing-center]
  9. ^ Jump up to: a b Hardware software co-design of a multimedia SOC platform by Sao-Jie Chen, Guang-Huei Lin, Pao-Ann Hsiung, Yu-Hen Hu 2009 ISBN pages 70-72
  10. Jump up ^ John Impagliazzo, John A. N. Lee (2004). History of computing in education. p. 172. ISBN 1-4020-8135-9.
  11. Jump up ^ Richard Sisson, Christian K. Zacher (2006). The American Midwest: an interpretive encyclopedia. p. 1489. ISBN 0-253-34886-2.
  12. Jump up ^ Hannan, Caryn (2008). Wisconsin Biographical Dictionary. pp. 83–84. ISBN 1-878592-63-7.
  13. Jump up ^ Readings in computer architecture by Mark Donald Hill, Norman Paul Jouppi, Gurindar Sohi 1999 ISBN 978-1-55860-539-8 page 41-48
  14. Jump up ^ Milestones in computer science and information technology by Edwin D. Reilly 2003 ISBN 1-57356-521-0 page 65
  15. ^ Jump up to: a b c Parallel computing for real-time signal processing and control by M. O. Tokhi, Mohammad Alamgir Hossain 2003 ISBN 978-1-85233-599-1 pages 201-202
  16. Jump up ^ "TOP500 Annual Report 1994". Netlib.org. 1996-10-01. Retrieved 2012-06-09.
  17. Jump up ^ N. Hirose and M. Fukuda (1997). "Numerical Wind Tunnel (NWT) and CFD Research at National Aerospace Laboratory". Proceedings of HPC-Asia '97. IEEE Computer Society. doi:10.1109/HPC.1997.592130.
  18. Jump up ^ H. Fujii, Y. Yasuda, H. Akashi, Y. Inagami, M. Koga, O. Ishihara, M. Kashiyama, H. Wada, T. Sumimoto, Architecture and performance of the Hitachi SR2201 massively parallel processor system, Proceedings of 11th International Parallel Processing Symposium, April 1997, Pages 233-241.
  19. Jump up ^ Y. Iwasaki, The CP-PACS project, Nuclear Physics B - Proceedings Supplements, Volume 60, Issues 1-2, January 1998, Pages 246-254.
  20. Jump up ^ A.J. van der Steen, Overview of recent supercomputers, Publication of the NCF, Stichting Nationale Computer Faciliteiten, the Netherlands, January 1997.
  21. Jump up ^ Scalable input/output: achieving system balance by Daniel A. Reed 2003 ISBN 978-0-262-68142-1 page 182
  22. Jump up ^ Xue-June Yang, Xiang-Ke Liao, et al in Journal of Computer Science and Technology. "The TianHe-1A Supercomputer: Its Hardware and Software". 26, Number 3. pp. 344–351.
  23. Jump up ^ The Supermen: Story of Seymour Cray and the Technical Wizards Behind the Supercomputer by Charles J. Murray 1997 ISBN 0-471-04885-2 pages 133-135
  24. Jump up ^ Parallel Computational Fluid Dyynamics; Recent Advances and Future Directions edited by Rupak Biswas 2010 ISBN 1-60595-022-X page 401
  25. Jump up ^ Supercomputing Research Advances by Yongge Huáng 2008 ISBN 1-60456-186-6 pages 313-314
  26. ^ Jump up to: a b Computational science -- ICCS 2005: 5th international conference edited by Vaidy S. Sunderam 2005 ISBN 3-540-26043-9 pages 60-67
  27. Jump up ^ Knight, Will: "IBM creates world's most powerful computer", NewScientist.com news service, June 2007
  28. Jump up ^ N. R. Agida et al. (2005). "Blue Gene/L Torus Interconnection Network | IBM Journal of Research and Development" (PDF). Torus Interconnection Network. 45, No 2/3 March–May 2005. p. 265.
  29. Jump up ^ Prickett, Timothy (May 31, 2010). "Top 500 supers – The Dawning of the GPUs". =Theregister.co.uk.
  30. Jump up ^ Hans Hacker et al in Facing the Multicore-Challenge: Aspects of New Paradigms and Technologies in Parallel Computing by Rainer Keller, David Kramer and Jan-Philipp Weiss (2010). Considering GPGPU for HPC Centers: Is It Worth the Effort?. pp. 118–121. ISBN 3-642-16232-0.
  31. Jump up ^ Damon Poeter (, October 11, 2011). "Cray's Titan Supercomputer for ORNL Could Be World's Fastest". Pcmag.com.
  32. Jump up ^ Feldman, Michael (October 11, 2011). "GPUs Will Morph ORNL's Jaguar Into 20-Petaflop Titan". Hpcwire.com.
  33. Jump up ^ Timothy Prickett Morgan (October 11, 2011). "Oak Ridge changes Jaguar's spots from CPUs to GPUs". Theregister.co.uk.
  34. Jump up ^ Condon, J.H. and K.Thompson, "Belle Chess Hardware", In Advances in Computer Chess 3 (ed.M.R.B.Clarke), Pergamon Press, 1982.
  35. Jump up ^ Hsu, Feng-hsiung (2002). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press. ISBN 0-691-09065-3
  36. Jump up ^ C. Donninger, U. Lorenz. The Chess Monster Hydra. Proc. of 14th International Conference on Field-Programmable Logic and Applications (FPL), 2004, Antwerp – Belgium, LNCS 3203, pp. 927 – 932
  37. Jump up ^ J Makino and M. Taiji, Scientific Simulations with Special Purpose Computers: The GRAPE Systems, Wiley. 1998.
  38. Jump up ^ RIKEN press release, Completion of a one-petaflops computer system for simulation of molecular dynamics
  39. Jump up ^ Electronic Frontier Foundation (1998). Cracking DES - Secrets of Encryption Research, Wiretap Politics & Chip Design. Oreilly & Associates Inc. ISBN 1-56592-520-3.
  40. Jump up ^ "NVIDIA Tesla GPUs Power World's Fastest Supercomputer" (Press release). Nvidia. 29 October 2010.
  41. Jump up ^ Balandin, Alexander A. (October 2009). "Better Computing Through CPU Cooling". Spectrum.ieee.org.
  42. Jump up ^ "The Green 500". Green500.org.
  43. Jump up ^ "Green 500 list ranks supercomputers". iTnews Australia.
  44. Jump up ^ Wu-chun Feng (2003). "Making a Case for Efficient Supercomputing | ACM Queue Magazine, Volume 1 Issue 7, 10-01-2003 doi 10.1145/957717.957772" (PDF).
  45. Jump up ^ "IBM uncloaks 20 petaflops BlueGene/Q super". The Register. 2010-11-22. Retrieved 2010-11-25.
  46. Jump up ^ Prickett, Timothy (2011-07-15). "''The Register'': IBM 'Blue Waters' super node washes ashore in August". Theregister.co.uk. Retrieved 2012-06-09.
  47. Jump up ^ "HPC Wire July 2, 2010". Hpcwire.com. 2010-07-02. Retrieved 2012-06-09.
  48. Jump up ^ by Martin LaMonica (2010-05-10). "CNet May 10, 2010". News.cnet.com. Retrieved 2012-06-09.
  49. Jump up ^ "Government unveils world's fastest computer". CNN. Archived from the original on 2008-06-10. "performing 376 million calculations for every watt of electricity used."
  50. Jump up ^ "IBM Roadrunner Takes the Gold in the Petaflop Race".
  51. Jump up ^ "Top500 Supercomputing List Reveals Computing Trends". "IBM... BlueGene/Q system .. setting a record in power efficiency with a value of 1,680 Mflops/watt, more than twice that of the next best system."
  52. Jump up ^ "IBM Research A Clear Winner in Green 500".
  53. Jump up ^ "Green 500 list". Green500.org. Retrieved 2012-06-09.
  54. ^ Jump up to: a b Encyclopedia of Parallel Computing by David Padua 2011 ISBN 0-387-09765-1 pages 426-429
  55. Jump up ^ Knowing machines: essays on technical change by Donald MacKenzie 1998 ISBN 0-262-63188-1 page 149-151
  56. Jump up ^ Euro-Par 2004 Parallel Processing: 10th International Euro-Par Conference 2004, by Marco Danelutto, Marco Vanneschi and Domenico Laforenza ISBN 3-540-22924-8 pages 835
  57. Jump up ^ Euro-Par 2006 Parallel Processing: 12th International Euro-Par Conference, 2006, by Wolfgang E. Nagel, Wolfgang V. Walter and Wolfgang Lehner ISBN 3-540-37783-2 page
  58. Jump up ^ An Evaluation of the Oak Ridge National Laboratory Cray XT3 by Sadaf R. Alam etal International Journal of High Performance Computing Applications February 2008 vol. 22 no. 1 52-80
  59. Jump up ^ Open Job Management Architecture for the Blue Gene/L Supercomputer by Yariv Aridor et al in Job scheduling strategies for parallel processing by Dror G. Feitelson 2005 ISBN ISBN 978-3-540-31024-2 pages 95-101
  60. Jump up ^ "Top500 OS chart". Top500.org. Retrieved 2010-10-31.
  61. Jump up ^ "Wide-angle view of the ALMA correlator". ESO Press Release. Retrieved 13 February 2013.
  62. Jump up ^ Folding@home: OS Statistics. Stanford University. Retrieved 2012-03-11.
  63. Jump up ^ BOINCstats: BOINC Combined. BOINC. Retrieved 2011-05-28Note this link will give current statistics, not those on the date last accessed
  64. Jump up ^ BOINCstats: MilkyWay@home. BOINC. Retrieved 2011-05-28Note this link will give current statistics, not those on the date last accessed
  65. Jump up ^ "Internet PrimeNet Server Distributed Computing Technology for the Great Internet Mersenne Prime Search". GIMPS. Retrieved June 6, 2011
  66. Jump up ^ "Southampton engineers a Raspberry Pi Supercomputer :: University of Southampton". Southampton.ac.uk. Retrieved 2013-04-20.
  67. ^ Jump up to: a b Kravtsov, Valentin; Carmeli, David; Dubitzky, Werner; Orda, Ariel; Schuster, Assaf; Yoshpa, Benny. "Quasi-opportunistic supercomputing in grids, hot topic paper (2007)". IEEE International Symposium on High Performance Distributed Computing. IEEE. Retrieved 4 August 2011.
  68. ^ Jump up to: a b c The Potential Impact of High-End Capability Computing on Four Illustrative Fields of Science and Engineering by Committee on the Potential Impact of High-End Computing on Illustrative Fields of Science and Engineering and National Research Council (October 28, 2008) ISBN 0-309-12485-9 page 9
  69. Jump up ^ Xingfu Wu (1999). Performance Evaluation, Prediction and Visualization of Parallel Systems. pp. 114–117. ISBN 0-7923-8462-8.
  70. ^ Jump up to: a b Dongarra, Jack J.; Luszczek, Piotr; Petitet, Antoine (2003), "The LINPACK Benchmark: past, present and future", Concurrency and Computation: Practice and Experience (John Wiley & Sons, Ltd.): 803–820
  71. Jump up ^ Intel brochure - 11/91. "Directory page for Top500 lists. Result for each list since June 1993". Top500.org. Retrieved 2010-10-31.
  72. Jump up ^ "The Cray-1 Computer System" (PDF). Cray Research, Inc. Retrieved May 25, 2011.
  73. Jump up ^ Joshi, Rajani R. (9 June 1998). "A new heuristic algorithm for probabilistic optimization" (Subscription required). Department of Mathematics and School of Biomedical Engineering, Indian Institute of Technology Powai, Bombay, India. Retrieved 2008-07-01.
  74. Jump up ^ "Abstract for SAMSY - Shielding Analysis Modular System". OECD Nuclear Energy Agency, Issy-les-Moulineaux, France. Retrieved May 25, 2011.
  75. Jump up ^ "EFF DES Cracker Source Code". Cosic.esat.kuleuven.be. Retrieved 2011-07-08.
  76. Jump up ^ "Disarmament Diplomacy: - DOE Supercomputing & Test Simulation Programme". Acronym.org.uk. 2000-08-22. Retrieved 2011-07-08.
  77. Jump up ^ "China’s Investment in GPU Supercomputing Begins to Pay Off Big Time!". Blogs.nvidia.com. Retrieved 2011-07-08.
  78. Jump up ^ Kaku, Michio. Physics of the Future (New York: Doubleday, 2011), 65.
  79. Jump up ^ "Faster Supercomputers Aiding Weather Forecasts". News.nationalgeographic.com. 2010-10-28. Retrieved 2011-07-08.
  80. Jump up ^ Washington Post August 8, 2011[dead link]
  81. Jump up ^ Kan Michael (2012-10-31). "China is building a 100-petaflop supercomputer, InfoWorld, October 31, 2012". infoworld.com. Retrieved 2012-10-31.
  82. Jump up ^ Agam Shah (2011-06-20). "SGI, Intel plan to speed supercomputers 500 times by 2018, ComputerWorld, June 20, 2011". Computerworld.com. Retrieved 2012-06-09.
  83. Jump up ^ Dillow Clay (2012-09-18). "India Aims To Take The "World's Fastest Supercomputer" Crown By 2017, POPSCI, September 9, 2012". popsci.com. Retrieved 2012-10-31.
  84. Jump up ^ DeBenedictis, Erik P. (2005). "Reversible logic for supercomputing". Proceedings of the 2nd conference on Computing frontiers. pp. 391–402. ISBN 1-59593-019-1.
  85. Jump up ^ "IDF: Intel says Moore's Law holds until 2029". Heise Online. 2008-04-04.