Minicomputers and Large Scale Computations

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From then until today, massively parallel supercomputers with tens of thousands of off-the-shelf processors became the norm. The US has long been the 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 s and 90s, but since then China has become increasingly active in the field. It still used high-speed drum memory , rather than the newly emerging disk drive technology. The IBM used transistors , magnetic core memory, pipelined instructions, prefetched data through a memory controller and included pioneering random access disk drives.

The IBM was completed in and despite not meeting the challenge of a hundredfold increase in performance, it was purchased by the Los Alamos National Laboratory. Customers in England and France also bought the computer and it became the basis for the IBM Harvest , a supercomputer built for cryptanalysis.

The third pioneering supercomputer project in the early s was the Atlas at the University of Manchester , built by a team led by Tom Kilburn. He designed the Atlas to have memory space for up to a million words of 48 bits, but because magnetic storage with such a capacity was unaffordable, the actual core memory of Atlas was only 16, words, with a drum providing memory for a further 96, words. The Atlas operating system swapped data in the form of pages between the magnetic core and the drum. The Atlas operating system also introduced time-sharing to supercomputing, so that more than one programe could be executed on the supercomputer at any one time.

The CDC , designed by Seymour Cray , was finished in and marked the transition from germanium to silicon transistors. Silicon transistors could run faster and the overheating problem was solved by introducing refrigeration to the supercomputer design. It had eight central processing units CPUs , liquid cooling and the electronics coolant liquid fluorinert was pumped through the supercomputer architecture.

It performed at 1. 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.

However, development problems led to only 64 processors being built, and the system could never operate faster than about 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.

Departmental computers

Cray argued against this, famously quipping that "If you were plowing a field, which would you rather use? Two strong oxen or chickens? The CM-1 used as many as 65, 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. It was mainly used for rendering realistic 3D computer graphics.

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. Software development remained a problem, but the CM series sparked off considerable research into this issue. But by the mids, 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.


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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. 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.

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

A number of "special-purpose" systems have been designed, dedicated to a single problem. Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers. For example, Tianhe-1A consumes 4. Heat management is a major issue in complex electronic devices and affects powerful computer systems in various ways. The supercomputing awards for green computing reflect this issue. The packing of thousands of processors together inevitably generates significant amounts of heat density that need to be dealt with.

Intel Compute Stick Mini-PC Unboxing (Deutsch - German)

The Cray 2 was liquid cooled , and used a Fluorinert "cooling waterfall" which was forced through the modules under pressure. In , IBM's Roadrunner operated at 3. Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat , [73] the ability of the cooling systems to remove waste heat is a limiting factor. Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes in supercomputer architecture.

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. 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.

Although most modern supercomputers use a Linux -based 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. The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed. 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.

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 cannot handle traditional supercomputing tasks such as fluid dynamic simulations. The fastest grid computing system is the distributed computing project Folding home F h.

The Minicomputers of the 70s

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. 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.

Cloud Computing with its recent and rapid expansions and development have grabbed the attention of HPC users and developers in recent years. 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 is currently being done to overcome these challenges and make HPC in the cloud a more realistic possibility.

The Penguin On Demand POD cloud is a bare-metal compute model to execute code, but each user is given virtualized login node.

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Penguin Computing has also criticized that HPC clouds may allocated computing nodes to customers that are far apart, causing latency that impairs performance for some HPC applications. 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.

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Often a capability system is able to solve a problem of a size or complexity that no other computer can, e. 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.

No single number can reflect the overall performance of a computer system, yet the goal of the Linpack benchmark is to approximate how fast the computer solves numerical problems and it is widely used in the industry. The 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 TOP list, [] and the "Peak speed" is given as the "Rmax" rating.

Source: TOP In , Lenovo became the worlds largest provider for the top supercomputers. The same research group also succeeded in using a supercomputer to simulate a number of artificial neurons equivalent to the entirety of a rat's brain. Modern-day weather forecasting also relies on supercomputers. The National Oceanic and Atmospheric Administration uses supercomputers to crunch hundreds of millions of observations to help make weather forecasts more accurate. In , the challenges and difficulties in pushing the envelope in supercomputing were underscored by IBM 's abandonment of the Blue Waters petascale project.

The Advanced Simulation and Computing Program currently uses supercomputers to maintain and simulate the United States nuclear stockpile. 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. The cost of operating high performance supercomputers has risen, mainly due to increasing power consumption.

In the mid s a top 10 supercomputer required in the range of kilowatt, in the top 10 supercomputers required between 1 and 2 megawatt. Supercomputing facilities were constructed to efficiently remove the increasing amount of heat produced by modern multi-core central processing units. Based on the energy consumption of the Green list of supercomputers between and , a supercomputer with 1 exaflops in would have required nearly megawatt.

Operating systems were developed for existing hardware to conserve energy whenever possible. The increasing cost of operating supercomputers has been a driving factor in a trend towards bundling of resources through a distributed supercomputer infrastructure. National supercomputing centres first emerged in the US, followed by Germany and Japan.

Minicomputer

The European Union launched the Partnership for Advanced Computing in Europe PRACE with the aim of creating a persistent pan-European supercomputer infrastructure with services to support scientists across the European Union in porting, scaling and optimizing supercomputing applications. 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 of computers.

China and the US dominate the top spots. The EU cannot afford to lag behind.


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  • The Council plans to set up a joint undertaking after the Commission had previously introduced the regulation in January. The joint undertaking is estimated to be operational in early , with the overarching target of developing supercomputers capable of reaching exascale calculating capabilities by COM Ltd. A large central computer needed a team to manage it and to share its resources among departments and users.

    Almost every university created a computing center with its director.

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    Most of these centers served both academic and administrative users. The center would have a system programming staff to support the central machine and applications programmers for administrative computing. The centers never fully solved the problem that different groups of users have different computing needs. Most academic users run large numbers of small jobs, but some people want to run big computations or process very large sets of data. Administrative computing has an entirely different set of needs.

    In aggregate the capacity of the central computer was never enough to satisfy everybody. With varying success, computing centers attempted to balance priorities by technical, administrative, and financial mechanisms, but they could not make everybody happy. For researchers with research grants, this unhappiness was aggravated by a peculiarity of how many universities charged for computer time. Research universities wanted to recover the cost of research computing from funding agencies, such as the National Science Foundation. These universities charged all users for machine time.

    Researchers used their grants to cover the costs, while other departments paid from their own budgets. To recoup as much money as possible from grants the universities set their computing charges at the highest rate that the government would allow, including full overhead recovery. Frustrated by these high charges and the inflexibility of central computing, the richer departments used their research grants to buy minicomputers and set up their own computing centers. Staff costs were largely eliminated by having graduate students look after the systems.

    There were well-run centers in computer science, electrical engineering, physics, statistics, and several other departments. Later, as personal computers became widely available, schools and colleges, such as fine arts and the business school, set up personal computing centers. Many computer directors felt threatened by these developments. Some of these feelings were justified.



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