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Supercomputer
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"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 IBM Blue Gene/P supercomputer "Intrepid" at Argonne National Laboratory runs 164,000 processor cores using normal data center air conditioning, grouped in 40 racks/cabinets connected by a high-speed 3-D torus network.[1][2]
A supercomputer is a computer with a high level of computing 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] The majority of supercomputers today run Linux-based operating systems.
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.[5]
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.[6][7]
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 93 PFLOPS, exceeding the previous record holder, Tianhe-2, by around 59 PFLOPS. 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;[8][9] in November 2016 the U.S. has five of the top 10 and China two, in fact the top two.
Contents
[hide]
1 History
2 Hardware and architecture
2.1 Early machines; simple but fast
2.2 Vector designs
2.3 Massively parallel designs
2.4 Energy usage and heat management
3 Software and system management
3.1 Operating systems
3.2 Software tools and message passing
4 Distributed supercomputing
4.1 Opportunistic approaches
4.2 Quasi-opportunistic approaches
5 HPC in the Cloud
6 Performance measurement
6.1 Capability versus capacity
6.2 Performance metrics
6.3 The TOP500 list
7 Largest Supercomputer Vendors according to the total Rmax (GFLOPS) operated
8 Applications
9 Research and development trends
10 Energy use
11 In fiction
12 See also
13 Notes and references
14 External links
History[edit]
Main article: History of supercomputing
A Cray-1 preserved at the Deutsches Museum
The history of supercomputing goes back to the 1960s, with the Atlas at the University of Manchester 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.[10]
The Atlas was a joint venture between Ferranti and the Manchester University and was designed to operate at processing speeds approaching one microsecond per instruction, about one million instructions per second.[11] 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.[12]
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,[13] 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.[14][15][16][17]
Cray left CDC in 1972 to form his own company, Cray Research.[15] 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.[18][19] 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 .[20]
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.[21]
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 1.7 gigaFLOPS (GFLOPS) per processor.[22][23] The Hitachi SR2201 obtained a peak performance of 600 GFLOPS in 1996 by using 2048 processors connected via a fast three-dimensional crossbar network.[24][25][26] 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.[27]
Hardware and architecture[edit]
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 machines; simple but fast[edit]
Early supercomputer architectures pioneered by Seymour Cray relied on compact designs and local parallelism to achieve superior computational performance.[10] 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.
Vector designs[edit]
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.
Massively parallel designs[edit]
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?"[28] 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.[29]
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.[6][7]
Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers.[30][31][32] The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components.[33] 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.[34][35]
The CPU share of TOP500
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.[36] 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.[37][38] The use of multi-core processors combined with centralization is an emerging direction, e.g. as in the Cyclops64 system.[39][40]
As the price, performance and energy efficiency of general purpose graphic processors (GPGPUs) have improved,[41] a number of petaFLOPS supercomputers such as Tianhe-I and Nebulae have started to rely on them.[42] 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.[43][44] However, GPUs are gaining ground and in 2012 the Jaguar supercomputer was transformed into Titan by retrofitting CPUs with GPUs.[45][46][47]
High performance computers have an expected life cycle of about three years before requiring an upgrade.[48]
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,[49] Deep Blue,[50] and Hydra,[51] for playing chess, Gravity Pipe for astrophysics,[52] MDGRAPE-3 for protein structure computation molecular dynamics[53] and Deep Crack,[54] for breaking the DES cipher.
Energy usage and heat management[edit]
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 (MW) of electricity.[55] The cost to power and cool the system can be significant, e.g. 4 MW 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.[56] 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.[57][58][59]
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.[34] 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.[35]
In the Blue Gene system, IBM deliberately used low power processors to deal with heat density.[60] The IBM Power 775, released in 2011, has closely packed elements that require water cooling.[61] The IBM Aquasar system uses hot water cooling to achieve energy efficiency, the water being used to heat buildings as well.[62][63]
The energy efficiency of computer systems is generally measured in terms of "FLOPS per watt". In 2008, IBM's Roadrunner operated at 3.76 MFLOPS/W.[64][65] In November 2010, the Blue Gene/Q reached 1,684 MFLOPS/W.[66][67] 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.[68]
Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat,[69] the ability of the cooling systems to remove waste heat is a limiting factor.[70][71] 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.[72]

Software and system management[edit]
Operating systems[edit]
Main article: Supercomputer operating systems
Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes in supercomputer architecture.[73] 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.[74]
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.[75][76][77]
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.[78]
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.[73][79]
Software tools and message passing[edit]
Main article: Message passing in computer clusters
See also: Parallel computing and Parallel programming model
Wide-angle view of the ALMA correlator.[80]
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.
Distributed supercomputing[edit]
Opportunistic approaches[edit]
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 101 PFLOPS of x86 processing power As of October 2016[update]. Of this, over 100 PFLOPS are contributed by clients running on various GPUs, and the rest from various CPU systems.[81]
The BOINC platform hosts a number of distributed computing projects. As of February 2017[update], BOINC recorded a processing power of over 166 PetaFLOPS through over 762 thousand active Computers (Hosts) on the network.[82]
As of October 2016[update], GIMPS's distributed Mersenne Prime search achieved about 0.313 PFLOPS through over 1.3 million computers.[83] 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 approaches[edit]
Main article: Quasi-opportunistic supercomputing
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.[84] 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.[84]
HPC in the Cloud[edit]
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[85][86][87][88] is currently being done to overcome these challenges and make HPC in the cloud a more realistic possibility.
Performance measurement[edit]
Capability versus capacity[edit]
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.[89]
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.[89] 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.[89]
Performance metrics[edit]
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" (Million Instructions Per Second), as is the case with general-purpose computers.[90] 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.[91] 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.[91]
The TOP500 list[edit]
Main article: TOP500
Further information: History of supercomputing
Distribution of TOP500 supercomputers among different countries, as of November 2015
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,[92] and the "Peak speed" is given as the "Rmax" rating.
Top 20 Supercomputers in the World, as of June 2013
Year
Supercomputer
Peak speed
(Rmax)
Location
2016 Sunway TaihuLight 93.01 PFLOPS Wuxi, China
2013 NUDT Tianhe-2 33.86 PFLOPS Guangzhou, China
2012 Cray Titan 17.59 PFLOPS Oak Ridge, U.S.
2012 IBM Sequoia 17.17 PFLOPS Livermore, U.S.
2011 Fujitsu K computer 10.51 PFLOPS Kobe, Japan
2010 Tianhe-IA 2.566 PFLOPS Tianjin, China
2009 Cray Jaguar 1.759 PFLOPS Oak Ridge, U.S.
2008 IBM Roadrunner 1.026 PFLOPS Los Alamos, U.S.
1.105 PFLOPS
Largest Supercomputer Vendors according to the total Rmax (GFLOPS) operated[edit]
See also: Grid computing § Fastest virtual supercomputers
Source : TOP500
Country/Vendor
System count
System share (%)
Rmax (GFLOPS)
Rpeak (GFLOPS)
Processor cores
IBM 27 5.4 56,428,002 67,161,639 4,611,236
Cray Inc. 57 11.4 160,476,360 229,400,160 5,981,864
HPE 143 28.6 124,430,645 181,738,373 4,996,780
NUDT 4 0.8 39,271,790 64,020,685 3,534,336
SGI 23 4.6 14,741,773 17,963,102 813,376
Fujitsu 11 2.2 37,624,378 51,859,986 1,753,368
Bull 18 3.6 24,362,683 31,212,663 978,924
Dell 15 3.0 24,528,727 42,623,632 1,247,118
Atipa Technologies 3 0.6 3,044,976 4,163,712 214,584
NEC/HP 1 0.2 2,785,000 5,735,685 76,032
T-Platforms 3 0.6 4,428,620 6,355,903 170,824
RSC Group 1 0.2 658,112 829,338 19,936
Dawning 2 0.4 1,451,600 3,217,772 151,360
Hitachi/Fujitsu 1 0.2 1,018,000 1,502,236 222,072
Supermicro 1 0.2 602,983 677,376 20,160
NRCPCET 1 0.2 795,900 1,070,160 137,200
ClusterVision 2 0.4 784,735 881,254 42,368
Intel 1 0.2 758,873 933,481 51,392
Amazon 2 0.4 724,269 947,610 43,520
Oracle 2 0.4 708,300 804,835 68,672
MEGWARE 3 0.6 610,521 710,592 54,800
NEC 3 0.6 578,987 709,520 21,296
Adtech 1 0.2 532,600 1,098,000 38,400
Hitachi 2 0.4 496,900 622,598 20,544
IPE, Nvidia, Tyan 1 0.2 496,500 1,012,650 29,440
Itautec 2 0.4 411,800 920,830 27,776
Netweb Technologies 1 0.2 388,442 520,358 30,056
Xenon Systems 1 0.2 335,300 472,498 6,875
AMD, ASUS, FIAS, GSI 1 0.2 316,700 593,600 10,976
Clustervision/Supermicro 1 0.2 299,300 588,749 44,928
Niagara Computers, Supermicro 1 0.2 289,500 348,660 5,310
Inspur 1 0.2 196,234 262,560 8,412
HP/WIPRO 1 0.2 188,700 394,760 12,532
PEZY Computing/Exascaler Inc. 1 0.2 178,107 395,264 262,784
Acer Group 1 0.2 177,100 231,859 26,244
Applications[edit]
The stages of supercomputer application may be summarized in the following table:
Decade
Uses and computer involved
1970s Weather forecasting, aerodynamic research (Cray-1).[93]
1980s Probabilistic analysis,[94] radiation shielding modeling[95] (CDC Cyber).
1990s Brute force code breaking (EFF DES cracker).[96]
2000s 3D nuclear test simulations as a substitute for legal conduct Nuclear Non-Proliferation Treaty (ASCI Q).[97]
2010s Molecular Dynamics Simulation (Tianhe-1A)[98]
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.[99]
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.[100]
In 2011, the challenges and difficulties in pushing the envelope in supercomputing were underscored by IBM's abandonment of the Blue Waters petascale project.[101]
The Advanced Simulation and Computing Program currently uses supercomputers to maintain and simulate the United States nuclear stockpile.[102]
Research and development trends[edit]
Diagram of a three-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 EFLOPS (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.[103] The Indian government has also stated ambitions for an EFLOPS-range supercomputer, which they hope to complete by 2017.[104] In November 2014, it was reported that India is working on the fastest supercomputer ever, which is set to work at 132 EFLOPS.[105]
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.[106][not in citation given] Such systems might be built around 2030.[107]
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.[108]
Energy use[edit]
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.[109]
In fiction[edit]
Main article: AI takeover
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 Multivac, The Machine Stops, GLaDOS, The Evitable Conflict and Vulcan's Hammer.
See also[edit]
Wikimedia Commons has media related to Supercomputers.
ACM/IEEE Supercomputing Conference
Jungle computing
Nvidia Tesla Personal Supercomputer
Parallel computing
Supercomputing in China
Supercomputing in Europe
Supercomputing in India
Supercomputing in Japan
Supercomputing in Pakistan
Testing high-performance computing applications
Ultra Network Technologies
Notes and references[edit]
Jump up ^ "IBM Blue gene announcement". 03.ibm.com. 26 June 2007. Retrieved 9 June 2012.
Jump up ^ "Argonne National Laboratory, Intrepid". Retrieved 24 May 2017.
Jump up ^ https://www.top500.org/news/china-tops-supercomputer-rankings-with-new-93-petaflop-machine/
Jump up ^ "The List: November 2015". Top 500. Retrieved 24 January 2016.
Jump up ^ Lemke, Tim (8 May 2013). "NSA Breaks Ground on Massive Computing Center". Retrieved 11 December 2013.
^ 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.
^ 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.
Jump up ^ Clark, Don, China computer claims top speed, Wall Street Journal, 21 June 2016, p. B4
Jump up ^ Markoff, John, China crowds top computer list, New York Times, 21 June 2016, page B1
^ 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, pages 70–72
Jump up ^ The Atlas, University of Manchester, retrieved 21 September 2010
Jump up ^ Lavington, Simon (1998), A History of Manchester Computers (2 ed.), Swindon: The British Computer Society, pp. 41–52, ISBN 978-1-902505-01-5
Jump up ^ The Supermen, Charles Murray, Wiley & Sons, 1997.
Jump up ^ A history of modern computing by Paul E. Ceruzzi 2003 ISBN 978-0-262-53203-7 page 161 [1]
^ Jump up to: a b Hannan, Caryn (2008). Wisconsin Biographical Dictionary. pp. 83–84. ISBN 1-878592-63-7.
Jump up ^ John Impagliazzo; John A. N. Lee (2004). History of computing in education. p. 172. ISBN 1-4020-8135-9.
Jump up ^ Richard Sisson; Christian K. Zacher (2006). The American Midwest: an interpretive encyclopedia. p. 1489. ISBN 0-253-34886-2.
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
Jump up ^ Milestones in computer science and information technology by Edwin D. Reilly 2003 ISBN 1-57356-521-0 page 65
Jump up ^ http://www.icfcst.kiev.ua/MUSEUM/Kartsev.html
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Jump up ^ A.J. van der Steen, Overview of recent supercomputers, Publication of the NCF, Stichting Nationale Computer Faciliteiten, the Netherlands, January 1997.
Jump up ^ Scalable input/output: achieving system balance by Daniel A. Reed 2003 ISBN 978-0-262-68142-1 page 182
Jump up ^ Seymour Cray quotes
Jump up ^ Steve Nelson (3 October 2014). "ComputerGK.com : Supercomputers".
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". pp. 344–351. CS1 maint: Multiple names: authors list (link)
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Jump up ^ Parallel Computational Fluid Dyynamics; Recent Advances and Future Directions edited by Rupak Biswas 2010 ISBN 1-60595-022-X page 401
Jump up ^ Supercomputing Research Advances by Yongge Huáng 2008, ISBN 1-60456-186-6, pages 313–314
^ Jump up to: a b 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
^ 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
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Jump up ^ Knight, Will: "IBM creates world's most powerful computer", NewScientist.com news service, June 2007
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Jump up ^ Prickett, Timothy (31 May 2010). "Top 500 supers – The Dawning of the GPUs". Theregister.co.uk.
Jump up ^ "A Survey of CPU-GPU Heterogeneous Computing Techniques", ACM Computing Surveys, 2015
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.
Jump up ^ Damon Poeter (11 October 2011). "Cray's Titan Supercomputer for ORNL Could Be World's Fastest". Pcmag.com.
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Jump up ^ Timothy Prickett Morgan (11 October 2011). "Oak Ridge changes Jaguar's spots from CPUs to GPUs". Theregister.co.uk.
Jump up ^ "The NETL SuperComputer". page 2.
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Jump up ^ J Makino and M. Taiji, Scientific Simulations with Special Purpose Computers: The GRAPE Systems, Wiley. 1998.
Jump up ^ RIKEN press release, Completion of a one-petaFLOPS computer system for simulation of molecular dynamics
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Jump up ^ "NVIDIA Tesla GPUs Power World's Fastest Supercomputer" (Press release). Nvidia. 29 October 2010.
Jump up ^ Balandin, Alexander A. (October 2009). "Better Computing Through CPU Cooling". Spectrum.ieee.org.
Jump up ^ "The Green 500". Green500.org.
Jump up ^ "Green 500 list ranks supercomputers". iTnews Australia.
Jump up ^ Wu-chun Feng (2003). "Making a Case for Efficient Supercomputing | ACM Queue Magazine, Volume 1 Issue 7, 10 January 2003 doi 10.1145/957717.957772" (PDF). Archived from the original (PDF) on 30 March 2012.
Jump up ^ "IBM uncloaks 20 petaflops BlueGene/Q super". The Register. 22 November 2010. Retrieved 25 November 2010.
Jump up ^ Prickett, Timothy (15 July 2011). "The Register: IBM 'Blue Waters' super node washes ashore in August". Theregister.co.uk. Retrieved 9 June 2012.
Jump up ^ "HPC Wire 2 July 2010". Hpcwire.com. 2 July 2010. Archived from the original on 13 August 2012. Retrieved 9 June 2012.
Jump up ^ Martin LaMonica (10 May 2010). "CNet 10 May 2010". News.cnet.com. Retrieved 9 June 2012.
Jump up ^ "Government unveils world's fastest computer". CNN. Archived from the original on 10 June 2008. performing 376 million calculations for every watt of electricity used.
Jump up ^ "IBM Roadrunner Takes the Gold in the Petaflop Race".
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/W, more than twice that of the next best system.
Jump up ^ "IBM Research A Clear Winner in Green 500".
Jump up ^ "Green 500 list". Green500.org. Archived from the original on 3 July 2011. Retrieved 9 June 2012.
Jump up ^ Saed G. Younis. "Asymptotically Zero Energy Computing Using Split-Level Charge Recovery Logic". 1994. page 14.
Jump up ^ "Hot Topic – the Problem of Cooling Supercomputers" Archived 18 January 2015 at the Wayback Machine..
Jump up ^ Anand Lal Shimpi. "Inside the Titan Supercomputer: 299K AMD x86 Cores and 18.6K NVIDIA GPUs". 2012.
Jump up ^ Curtis Storlie; Joe Sexton; Scott Pakin; Michael Lang; Brian Reich; William Rust. "Modeling and Predicting Power Consumption of High Performance Computing Jobs". 2014.
^ Jump up to: a b Encyclopedia of Parallel Computing by David Padua 2011 ISBN 0-387-09765-1 pages 426–429
Jump up ^ Knowing machines: essays on technical change by Donald MacKenzie 1998 ISBN 0-262-63188-1 page 149-151
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, page 835
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
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
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 978-3-540-31024-2 pages 95–101
Jump up ^ "Top500 OS chart". Top500.org. Retrieved 31 October 2010.
Jump up ^ "Wide-angle view of the ALMA correlator". ESO Press Release. Retrieved 13 February 2013.
Jump up ^ "Folding@home: OS Statistics". Stanford University. Retrieved 30 October 2016.
Jump up ^ "BOINCstats: BOINC Combined". BOINC. Retrieved 30 October 2016Note this link will give current statistics, not those on the date last accessed.
Jump up ^ "Internet PrimeNet Server Distributed Computing Technology for the Great Internet Mersenne Prime Search". GIMPS. Retrieved 6 June 2011.
^ 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.
Jump up ^ Jamalian, S.; Rajaei, H. (2015-03-01). "ASETS: A SDN Empowered Task Scheduling System for HPCaaS on the Cloud". 2015 IEEE International Conference on Cloud Engineering: 329–334. doi:10.1109/IC2E.2015.56.
Jump up ^ Jamalian, S.; Rajaei, H. (2015-06-01). "Data-Intensive HPC Tasks Scheduling with SDN to Enable HPC-as-a-Service". 2015 IEEE 8th International Conference on Cloud Computing: 596–603. doi:10.1109/CLOUD.2015.85.
Jump up ^ Gupta, A.; Milojicic, D. (2011-10-01). "Evaluation of HPC Applications on Cloud". 2011 Sixth Open Cirrus Summit: 22–26. doi:10.1109/OCS.2011.10.
Jump up ^ Kim, H.; el-Khamra, Y.; Jha, S.; Parashar, M. (2009-12-01). "An Autonomic Approach to Integrated HPC Grid and Cloud Usage". 2009 Fifth IEEE International Conference on e-Science: 366–373. doi:10.1109/e-Science.2009.58.
^ 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 (28 October 2008) ISBN 0-309-12485-9 page 9
Jump up ^ Xingfu Wu (1999). Performance Evaluation, Prediction and Visualization of Parallel Systems. pp. 114–117. ISBN 0-7923-8462-8.
^ Jump up to: a b Dongarra, Jack J.; Luszczek, Piotr; Petitet, Antoine (2003), "The LINPACK Benchmark: past, present and future" (PDF), Concurrency and Computation: Practice and Experience, John Wiley & Sons, Ltd.: 803–820
Jump up ^ Intel brochure – 11/91. "Directory page for Top500 lists. Result for each list since June 1993". Top500.org. Retrieved 31 October 2010.
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Jump up ^ Joshi, Rajani R. (9 June 1998). "A new heuristic algorithm for probabilistic optimization". Department of Mathematics and School of Biomedical Engineering, Indian Institute of Technology Powai, Bombay, India. Retrieved 1 July 2008. (Subscription required (help)).
Jump up ^ "Abstract for SAMSY – Shielding Analysis Modular System". OECD Nuclear Energy Agency, Issy-les-Moulineaux, France. Retrieved 25 May 2011.
Jump up ^ "EFF DES Cracker Source Code". Cosic.esat.kuleuven.be. Retrieved 8 July 2011.
Jump up ^ "Disarmament Diplomacy: – DOE Supercomputing & Test Simulation Programme". Acronym.org.uk. 22 August 2000. Retrieved 8 July 2011.
Jump up ^ "China’s Investment in GPU Supercomputing Begins to Pay Off Big Time!". Blogs.nvidia.com. Retrieved 8 July 2011.
Jump up ^ Kaku, Michio. Physics of the Future (New York: Doubleday, 2011), 65.
Jump up ^ "Faster Supercomputers Aiding Weather Forecasts". News.nationalgeographic.com. 28 October 2010. Retrieved 8 July 2011.
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Jump up ^ Prashanth G N (13 November 2014). "India working on building fastest supercomputer". Deccan Herald. Retrieved 28 November 2014.
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.
Jump up ^ "IDF: Intel says Moore's Law holds until 2029". Heise Online. 4 April 2008.
Jump up ^ Solem, J. C. (1985). "MECA: A multiprocessor concept specialized to Monte Carlo". Proceedings of the Joint Los Alamos National Laboratory – Commissariat à l'Energie Atomique Meeting Held at Cadarache Castle, Provence, France 22–26 April 1985; Monte-Carlo Methods and Applications in Neutronics, Photonics and Statistical Physics, Alcouffe, R.; Dautray, R.; Forster, A.; Forster, G.; Mercier, B.; eds. (Springer Verlag, Berlin). 240: 184–195.
Jump up ^ "Green Supercomputer Crunches Big Data in Iceland". intelfreepress.com. 21 May 2015. Retrieved 18 May 2015.
External links[edit]
A Tunable, Software-based DRAM Error Detection and Correction Library for HPC
Detection and Correction of Silent Data Corruption for Large-Scale High-Performance Computing
"NetworkComputer – Industry's Fastest Enterprise Job Scheduler".
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Suspicious activity report
From Wikipedia, the free encyclopedia
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The examples and perspective in this article deal primarily with the United States and do not represent a worldwide view of the subject. You may improve this article, discuss the issue on the talk page. (May 2014) (Learn how and when to remove this template message)
This article relies largely or entirely on a single source. Relevant discussion may be found on the talk page. Please help improve this article by introducing citations to additional sources. (December 2011)
In financial regulation, a Suspicious Activity Report (SAR) or Suspicious Transaction Report (STR) is a report made by a financial institution about suspicious or potentially suspicious activity. The criteria to decide when a report must be made varies from country to country but generally is any financial transaction that does not make sense to the financial institution, is unusual for that particular client or appears to be done only for the purpose of hiding or obfuscating a transaction. The report is filed with that country's financial crime enforcement unit, which is typically a specialist agency designed to collect and analyse transactions and report these to relevant law enforcement units. Front line staff in the financial institution have the responsibility to identify transactions that may be suspicious and these are reported to a designated person that is responsible for the reporting the transaction. The financial institution is not allowed to inform the client or the parties to the transaction that a SAR has been lodged.
For example, in the United States, suspicious transaction reports[1] must be reported to the Financial Crimes Enforcement Network (FinCEN), an agency of the United States Department of the Treasury. In Australia the SAR must be reported to Australian Transaction Reports and Analysis Centre (AUSTRAC), an Australian government agency. Most countries have laws that require financial institutions to report transactions and will have a designated agency to receive these. The agency to which a report is required to be filed for a given country is typically part of the law enforcement or financial regulatory department of that country.
Contents
1 Reporting
2 Reporters
3 Other related forms
4 Confidentiality
5 SAR filing options
6 Penalties for non-compliance
7 History
8 See also
9 References
10 External links
Reporting[edit]
SARs include detailed information about transactions that are or appear to be suspicious. The goal of SAR filings is to help the government identify individuals, groups and organizations involved in fraud, terrorist financing, money laundering, and other crimes.
The purpose of a suspicious activity report is to report known or suspected violations of law or suspicious activity observed by financial institutions subject to the regulations (for example, the Bank Secrecy Act (BSA)). In many instances, SARs have been instrumental in enabling law enforcement to initiate or supplement major money laundering or terrorist financing investigations and other criminal cases.[2] Information provided in SAR forms also presents FinCEN with a method of identifying emerging trends and patterns associated with financial crimes. The information about those trends and patterns is vital to law enforcement agencies and provides valuable feedback to financial institutions.[2]
In the United States, FinCEN requires that an SAR be filed by a financial institution when the financial institution suspects insider abuse by an employee; violations of law aggregating over $5,000 where a subject can be identified;[clarification needed] violations of law aggregating over $25,000 regardless of a potential subject; transactions aggregating $5,000 or more that involve potential money laundering or violations of the Bank Secrecy Act; computer intrusion; or when a financial institution knows that a customer is operating as an unlicensed money services business.
Each SAR must be filed within 30 days of the date of the initial determination for the necessity of filing the report. An extension of 30 days can be obtained if the identity of the person conducting the suspicious activity is not known. At no time, however, should the filing of an SAR be delayed longer than 60 days. The Bank Secrecy Act specifies that each firm must maintain records of its SARs for a period of five years from the date of filing.
Reporters[edit]
The report can start with any employee of a financial service. The employees are generally trained to be alert for suspicious activity, such as situations where people are trying to wire money out of the country without identification, or activity by someone with no job who starts depositing large amounts of cash into an account. Employees are trained to communicate their suspicion up their chain of command where further decisions are made about whether to file a report or not.[citation needed]
Many different types of finance-related industries are required to file SARs. These include:[3]
depository institutions (for example, banks and credit unions)
securities and futures dealers (for example, stock brokers and mutual fund brokers)
money services businesses (for example, check cashing services, currency exchange bureaus, and money order providers)
casinos and card clubs
dealers in precious metals and gems (for example, jewelery dealers)
insurance companies
mortgage companies and brokers
Other related forms[edit]
There are other forms that FinCEN requires businesses and individuals to file. These include:[3]
individuals who transport more than $10,000 in currency into or out of the United States
shippers and receivers involved in the transfer of $10,000 in currency into or out of the United States
businesses that receive more than $10,000 in currency in a single transaction or in related transactions
people who have control over more than $10,000 in financial accounts outside of the U.S. during a calendar year
Confidentiality[edit]
See also: Gag order
Unauthorized disclosure of a SAR filing is a federal criminal offense.[4]
Financial institutions undertake an investigation process prior to filing a SAR to ensure that the information reported is appropriate, complete, and accurate. This process will often include review by financial investigators, management and/or attorneys prior to filing.
To encourage complete candor and cooperation, there are disclosure and evidentiary privileges that protect SAR filers. First, an individual or organization is precluded from discovering the existence or contents of a SAR that includes the individual or organization's name. SARs filers are immune from the discovery process.[5] Second, SAR filers enjoy immunity for all statements made in their SARs, regardless of whether those statements were allegedly made in bad faith.[6][7]
SAR filing options[edit]
Effective July 1, 2012 all SAR Reports must be filed through FinCEN's BSA E-filing System.[8]
A SAR has five sections each containing information about the filing institution or the activity in question:
Part I - Subject Information
Any name, address, social security or tax ID's, birth date, drivers license numbers, passport numbers, occupation and phone numbers of all parties involved with the activity.
Part II - Suspicious Activity Information
Date Range and codes for the type of Suspicious Activity
Part III - Information about Financial Institution where Activity Occurred
Part IV - Filing Institution Contact Information
Usually[clarification needed] contains the contact information for the financial institution's compliance officer or equivalent and list of any law enforcement agency that has been contacted while investigating the activity..
Part V - Suspicious Activity Information - Narrative
A written description of the activity.
Penalties for non-compliance[edit]
Financial institutions and their employees face civil and criminal penalties for failing to properly file suspicious activity reports, including any combination of large[quantify] fines, regulatory restrictions, loss of banking charter, or imprisonment.
History[edit]
The requirement to file suspicious activity reports (as well as the accompanying implied gag order) was added by Section 1517(b) of the Annunzio-Wylie Anti-Money Laundering Act (part of the Housing and Community Development Act of 1992, Pub.L. 102–550, 106 Stat. 3762, 4060).
See also[edit]
Casino regulations under the Bank Secrecy Act
Money laundering
Tax evasion
Terrorist financing
Structuring
Suspicious Activity Report (justice and homeland security)
References[edit]
Jump up ^ See generally Title 31 of the Code of Federal Regulations sec. 1010.320.
^ Jump up to: a b "Guidance on Preparing A Complete & Sufficient Suspicious Activity Report: Narrative" (PDF). Guidance on Preparing a Complete & Sufficient Suspicious Activity Report. Financial Crimes Enforcement Network. Retrieved May 24, 2017.
^ Jump up to: a b "Bank Secrecy Act Forms and Filing Requirements". Financial Crimes Enforcement Network. Archived from the original on 2013-06-26. Retrieved 2013-06-28.
Jump up ^ "Unauthorized Disclosure of suspicious activity reports" (PDF). Financial Crimes Enforcement Network. 2004-08-18. Retrieved 2011-12-22.
Jump up ^ Union Bank of California v. Superior Court, 130 Cal. App. 4th 378, 29 Cal. Rptr. 3d 894 (2005)
Jump up ^ Lee v. Bankers Trust Co., 166 F.3d 540 (2d Cir. 1999)
Jump up ^ Stoutt v. Banco Popular de Puerto Rico, 320 F.3d 26 (1st Cir. 2003).
Jump up ^ "FinCEN Reports Going Paperless". Financial Crimes Enforcement Network. Retrieved 2012-06-13.
External links[edit]
FinCEN: Financial Crimes Enforcement Network — official site
Retrieved from "https://en.wikipedia.org/w/index.php?title=Suspicious_activity_report&oldid=842511937"
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Alana de la Garza
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Alana de la Garza
Garza at the 16th Annual Impact Awards Gala, February 2013
Born
(1976-06-18) June 18, 1976 (age 41)
Columbus, Ohio, United States
Occupation
Actress, Model
Years active
1999–present
Spouse(s)
Michael Roberts (m. 2008)
Children
2
Alana de la Garza (born June 18, 1976) is an American actress. She is best known for her roles as Connie Rubirosa on the NBC television series Law & Order, Law & Order: LA, and Law & Order: Special Victims Unit, and as Marisol Delko-Caine on CSI: Miami. In 2014 and 2015, she starred as Detective Jo Martinez in the ABC series Forever. From 2016 to 2017, she starred in Criminal Minds: Beyond Borders as Special Agent Clara Seger.
Contents
[hide]
1 Early life
2 Career
3 Personal life
4 Filmography
4.1 Film
4.2 Television
5 References
6 External links
Early life[edit]
De la Garza was born in Columbus, Ohio, to a Mexican-American father and an Irish-American mother. She was raised in El Paso, Texas.[1] She won the "Miss Photogenic" title in the Miss El Paso Teen USA local beauty pageant. She then became a special-needs teacher and enrolled at the University of Texas at El Paso,[2] studying physical therapy and social work. Upon graduating, she landed some small roles while living in Orlando, Florida, then soon after moved to New York City.[citation needed]
Career[edit]
In 2001, she was featured in the music video for Brooks & Dunn's song "Ain't Nothing 'bout You". Garza also appeared in the music video "All or Nothing" released by the boy band O-Town in 2001. She obtained the role of Rosa Santos on the soap opera, All My Children, guest-starring roles in JAG, Charmed, Two and a Half Men, and Las Vegas. She starred as Maria in the short-lived television series The Mountain. In 2006, she starred in Mr. Fix-It co-starring David Boreanaz. She also guest-starred as an evil Kryptonian (named "Aethyr" in promotional materials) in the Season 5 premiere episode of Smallville and played the recurring role of Marisol Delko Caine on the CBS television series CSI: Miami. She has twice been featured in the "Girls of Maxim" online gallery.
In 2006, she joined the cast of NBC's Law & Order during the premiere of Season 17, portraying Assistant District Attorney Connie Rubirosa. Her performance as ADA Rubirosa has been widely praised by critics.[3][4][5] In 2007, she earned a nomination for "Best Supporting Actress—Television" by the Imagen Foundation Awards. Following in 2008, she was nominated for an ALMA Award as "Outstanding Supporting Actress in a Drama Television Series".[6] Later that year, she garnered an "Impact Award" for "Outstanding Performance in a Dramatic Television Series" by the National Hispanic Media Coalition.[7]
De la Garza played Assistant District Attorney (later DDA) Rubirosa for the final four seasons of Law & Order, a role she also played on the short-lived spin-off Law & Order: LA (2011). On January 22, 2014 she guest starred in an episode of Law & Order: Special Victims Unit as Connie Rubirosa. Rubirosa, who had recently departed the Los Angeles District Attorney's Office, is now a federal prosecutor, heading up a joint task force on underage sex trafficking. Her guest appearance originally aired on January 22, 2014.[8] This is, to date, her final appearance in the franchise.
In July 2011, de la Garza was reported to reprise her role as Marisol Delko Caine,[9] on CSI: Miami's season 10 premiere.[10][11]
In February 2012, she was cast as a lead in the NBC medical drama pilot Do No Harm. She played Dr. Lena Solis, a neurologist.[12][13] In June 2012, De La Garza was added to the cast of Are You Here, the feature film debut of Mad Men creator Matthew Weiner.[14]
In 2014–2015 she co-starred as police detective Jo Martinez in the ABC television series Forever.[15] In September 2015, she began a recurring role as the new head of Homeland Security on Scorpion. She has recently joined the main cast of Criminal Minds: Beyond Borders, starring as the female-lead opposite Gary Sinise.
Personal life[edit]
She married her long-time boyfriend, Michael Roberts, a writer, on May 31, 2008, in Orlando, Orange County, Florida.[16][17] They have a son[18][19] and a daughter.[20]
Filmography[edit]
Film[edit]
Year
Title
Role
Notes
2002 Bending All the Rules Woman ordering shots
2004 El segundo Delicia
2005 Mr. Dramatic Mrs. Dramatic Short film
2006 Mr. Fix It Sophia
2013 Are You Here Victoria Riolobos
Television[edit]
Year
Title
Role
Notes
1999 Mortal Kombat: Conquest Ella Episode: "Twisted Truths"
2001 All My Children Rosa Santos Recurring role
2003 JAG Maria Elena Episode: "A Tangled Webb: Part 2"
2003 Las Vegas Shelly Episode: "Pros and Cons"
2004 Two and a Half Men Crystal Episode: "No Sniffing, No Wowing"
2004–2005 The Mountain Maria Serrano Series regular, 13 episodes
2005 Smallville Aethyr Episode: "Arrival"
2005 Charmed Sylvia Episode: "Desperate Housewitches"
2005–2011 CSI: Miami Marisol Delko Recurring role, 12 episodes
2006 The Book of Daniel Jessie Gilmore Recurring role, 3 episodes
2006–2010 Law & Order ADA Connie Rubirosa Series regular, 85 episodes
2011 Law & Order: LA DDA Connie Rubirosa Series regular, 8 episodes
2012 NCIS: Los Angeles Diane Dunross Episode: "Partners"
2012 Deadly Hope Joanne Connors Television film
2012 Single Ladies Nicolette Episode: "The Business of Friendship"
2013 Do No Harm Dr. Lena Solis Series regular, 13 episodes
2014 Law & Order: Special Victims Unit AUSA Connie Rubirosa Episode: "Jersey Breakdown"
2014–2015 Forever Jo Martinez Series regular
2015 Scorpion Adriana Molina Recurring role, 3 episodes
2016–2017 Criminal Minds: Beyond Borders SSA Clara Seger Series Regular
2017 Criminal Minds Clara Seger Episode: "Spencer"
References[edit]
Jump up ^ Profile, NBC.com; accessed February 28, 2015.
Jump up ^ Profile, elpasotimes.com; accessed February 28, 2015.
Jump up ^ "Law & Order - EW.com". Entertainment Weekly's EW.com. Retrieved December 2, 2015.
Jump up ^ TV Guide – Law & Order: When Courtroom Tables Turn by Matt Roush
Jump up ^ "Murder, They Wrote". The New York Times. February 11, 2007. Retrieved December 10, 2016.
Jump up ^ Profile, imdb.com; accessed February 28, 2015.
Jump up ^ National Hispanic Media Coalition
Jump up ^ "Scoop: Alana De La Garza Brings Law & Order's Connie to SVU — But With a Twist". TV Line. Retrieved February 28, 2015.
Jump up ^ Adam Bryant (July 29, 2011). "CSI: Miami Scoop: Alana De La Garza Returns for the Season Premiere". TVGuide.com. Retrieved December 2, 2015.
Jump up ^ Americ Ngwije. "First Photos: CSI: MIAMI "Countermeasures" Season 10 Premiere With Alana De La Garza - TV Equals". TV Equals. Retrieved December 2, 2015.
Jump up ^ "Alana De La Garza Promo Pictures CSI Miami Season 10". MediaDB. Retrieved December 2, 2015.
Jump up ^ "Pilot Season: 'Law & Order''s Alana De La Garza Scrubs In to NBC's 'Do No Harm'", tvguide.com; accessed February 28, 2015.
Jump up ^ Kate Stanhope (May 10, 2012). "NBC Orders Jekyll and Hyde Drama Do No Harm, Mystery Infamous to Series". TVGuide.com. Retrieved December 2, 2015.
Jump up ^ "Movie Casting Roundup", deadline.com; accessed February 28, 2015.
Jump up ^ Goldberg, Lesley (May 8, 2014). "ABC Orders 'How to Get Away With Murder,' 'Black-ish,' 'American Crime,' 'Selfie,' 'Galavant,' More to Series". The Hollywood Reporter. Retrieved May 14, 2014.
Jump up ^ "AlanaDeLaGarza". alanadelagarza.com. Retrieved December 2, 2015.
Jump up ^ Conversations in Hollywood, orlandosentinel.com; accessed February 28, 2015.
Jump up ^ "EXCLUSIVE: Alana De La Garza Has Her Baby!". Latina. October 6, 2010. Archived from the original on October 8, 2010. Retrieved October 6, 2010.
Jump up ^ de la Garza, Alana (June 13, 2013). "due 7/10 but my son was two weeks early:) I'm thinking late June... Final countdown! Yeaa!". Twitter. Retrieved June 1, 2013.
Jump up ^ Hernandez, Lee (July 14, 2013). "Alana De La Garza Welcomes Daughter Liv Elena". People. Retrieved July 15, 2013.
External links[edit]
Official website
Alana de la Garza on IMDb
Authority control
WorldCat Identities
VIAF: 187391367
LCCN: no2011164130
ISNI: 0000 0001 3912 2599
BNF: cb170355966 (data)
Retrieved from "https://en.wikipedia.org/w/index.php?title=Alana_de_la_Garza&oldid=795513595"
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1976 births
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This page was last edited on 14 August 2017, at 18:35.
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values: ألانا دي لا غارزا – Arabic

values: Алана де ла Гарса – Bulgarian

values: آلانا دلا گارزا – Persian

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values: Де Ла Гарза, Алана – Russian

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