Dr. dre the chronic zip. Apr 21, 2009 The chronic discography on dr 93 follow follow on chronic zip 1992 dre 86. Free netblog201111dr dre the chronic 1992 chronic download wit instrumental ext download web44. The rating twitter dre the dre. Dre - The Chronic (1992) 10 months Rap Hot Dr. Dre - Compton (2015) 10 months Rap Hot 2Pac - California Love (OG Version) (feat. Dre) 7 months Rap. Dre, The Chronic Full Album Zip - DOWNLOAD. Here you can find dre chronic 2001 zip shared files. Dre Chronic 2001 The Next Episode (feat Snoop Dogg a.mp3 from 4shared.com 3.7 MB, Chronic 2001.zip from mediafire.com 93.35 MB free from TraDownload.
Intel today announced that it and subcontractor Cray will build the first supercomputer with of one exaflop of performance — equivalent to one quintillion floating point computations (“flops”) per second, where a flop equals two 15-digit numbers multiplied together — for the Department of Energy’s Argonne National Laboratory in Chicago. It’s expected to be delivered by 2021.
The Santa Clara company says that the $500 million system, dubbed Aurora, is purpose-built for both traditional high-performance computing and artificial intelligence, and that it will be used to “dramatically” advance scientific research and discovery. It’s the second iteration; Intel previously said it would deploy a 180-petaflop supercomputer at Argonne in 2018, architected on its third-gen Knights Hill Xeon Phi processors, but scrapped the plans after China revealed it intended to build an exascale system by 2020.
At the core of Aurora is a future generation of Intel’s Xeon Scalable processor — Intel Xᵉ — paired with next-gen Optane DC persistent memory. It’ll employ Cray’s Shasta supercomputing system and its Slingshot high-performance interconnect, and fully support Intel’s One API, a suite of developer tools for mapping compute engines to a range of processors, graphics chips, field-programmable gate arrays, and other accelerators.
“There is tremendous scientific benefit to our nation that comes from collaborations like this one with the Department of Energy, Argonne National Laboratory, and industry partners Intel and Cray,” said Argonne National Laboratory director Paul Kearns. “Argonne’s Aurora system is built for next-generation Artificial Intelligence and will accelerate scientific discovery by combining high-performance computing and artificial intelligence to address real-world problems, such as improving extreme weather forecasting, accelerating medical treatments, [charting] the human brain, developing new materials, and further understanding the universe — and that is just the beginning.”
Aurora is an outgrowth of the Energy Department’s Exascale Computing Project (ECP), a grant program within its longrunning PathForward initiative which seeks to accelerate research necessary to develop exascale supercomputers in the U.S. Nearly $258 million in funding was allocated over a three-year contract period starting 2017, and the companies selected to participate — Advanced Micro Devices, Cray, Hewlett Packard Enterprise, IBM, and Nvidia, in addition to Intel — were required to supply supplementary financing amounting to at least 40 percent of their total project cost.
More recently, in April, the Energy Department opened requests for two exoscale systems as part of its CORAL-2 procurement, with a budget ranging from $800 million to $1.2 billion.
![]()
The Department of Energy previously awarded $425 million in federal funding to IBM, Nvidia, and other companies to build two supercomputers: one at the Department of Energy’s Oak Ridge and another at Lawrence Livermore National Laboratories. The Oak Ridge system — Summit — delivers between 143 to 200 peak petaflops, according to the TOP500 ranking of supercomputer performance (based on LINPACK score), while Lawrence Livermore’s Sequoia cluster tops out at about 20 petaflops. Both Summit and Sierra were built by IBM and pack IBM Power9 processors and Nvidia Tesla V100 accelerator chips, and consume an enormous amounts of power — up to 13MW, in Summit’s case.
“Achieving exascale is imperative, not only to better the scientific community, but also to better the lives of everyday Americans,” said U.S. Secretary of Energy Rick Perry. “Aurora and the next generation of exascale supercomputers will apply HPC and AI technologies to areas such as cancer research, climate modeling, and veterans’ health treatments. The innovative advancements that will be made with exascale will have an incredibly significant impact on our society.”
Download teamviewer 10 full. It allows you to connect with your partner in different ways: you can see or control your partner’s desktop for online-help; you can also transfer your desktop for presentation purposes to your partner.
Assuming Intel delivers on its promise, Aurora will be the crown jewel in the U.S.’s supercomputer portfolio, but it might not be the world’s most powerful. Three teams in China — in Tianjin (prototype), Jinan, and Beijing — are actively competing to build China’s exascale system in the next seven months, and Japan’s Post-K exascale computer has a target deployment date of 2020.
Currently, America hosts five of the 10 fastest computers in the world, with China’s best — the TaihuLight at the National Supercomputing Center in Wuxi, built on Sunway’s SW26010 processor architecture, and the Tianhe-2A in Guangzhou — ranking third and fourth, respectively, at roughly 125 peak petaflops and 100 peak petaflops. Cray’s Piz Daint sits in fifth ahead of Trinity at Los Alamos National Laboratory, Fujitsu’s AI Bridge Clouding Infrastructure in Japan, and Lenovo’s SuperMUC-NG in Germany.
It’s a fierce arms race betwen China and the U.S. For the first time in TOP500 rankings two years ago, China surpassed the United States in total number of ranked supercomputers, 202 to 143. That trend accelerated in the intervening year; according to the TOP500 fall 2018 report, the number of ranked U.S. supercomputers fell to 108 as China’s total climbed to 229.
China and the United States are followed in the largest number of ranked supercomputers by Japan, which has 31 systems; the U.K., which has 20; France with 18; Germany with 17; and Ireland with 12.
Supercomputers, the world's largest and fastest computers, are primarily used for complex scientific calculations. The parts of a supercomputer are comparable to those of a desktop computer: they both contain hard drives, memory, and processors (circuits that process instructions within a computer program).
Although both desktop computers and supercomputers are equipped with similar processors, their speed and memory sizes are significantly different. For instance, a desktop computer built in the year 2000 normally has a hard disk data capacity of between 2 and 20 gigabytes and one processor with tens of megabytes of random access memory (RAM)--just enough to perform tasks such as word processing, web browsing, and videogaming. Meanwhile, a supercomputer of the same time period has thousands of processors, hundreds of gigabytes of RAM, and hard drives that allow for hundreds, and sometimes thousands, of gigabytes of storage space.
The supercomputer's large number of processors, enormous disk storage, and substantial memory greatly increase the power and speed of the machine. Although desktop computers can perform millions of floating-point operations per second (megaflops), supercomputers can perform at speeds of billions of operations per second (gigaflops) and trillions of operations per second (teraflops).
Evolution of Supercomputers
Many current desktop computers are actually faster than the first supercomputer, the Cray-1, which was developed by Cray Research in the mid-1970s. The Cray-1 was capable of computing at 167 megaflops by using a form of supercomputing called vector processing , which consists of rapid execution of instructions in a pipelined fashion. Contemporary vector processing supercomputers are much faster than the Cray-1, but an ultimately faster method of supercomputing was introduced in the mid-1980s: parallel processing . Applications that use parallel processing are able to solve computational problems by simultaneously using multiple processors.
Using the following scenario as a comparative example, it is easy to see why parallel processing is becoming the preferred supercomputing method. If you were preparing ice cream sundaes for yourself and nine friends, you would need ten bowls, ten scoops of ice cream, ten drizzles of chocolate syrup, and ten cherries. Working alone, you would take ten bowls from the cupboard and line them up on the counter. Then, you would place one scoop of ice cream in each bowl, drizzle syrup on each scoop, and place a cherry on top of each dessert. This method of preparing sundaes would be comparable to vector processing. To get the job done more quickly, you could have some friends help you in a parallel processing method. If two people prepared the sundaes, the process would be twice as fast; with five it would be five times as fast; and so on.
Conversely, assume that five people will not fit in your small kitchen, therefore it would be easier to use vector processing and prepare all ten sundaes yourself. This same analogy holds true with supercomputing. Some researchers prefer vector computing because their calculations cannot be readily distributed among the many processors on parallel supercomputers. But, if a researcher needs a supercomputer that calculates trillions of operations per second, parallel processors are preferred--even though programming for the parallel supercomputer is usually more complex.
Applications of Supercomputers![]()
Supercomputers are so powerful that they can provide researchers with insight into phenomena that are too small, too big, too fast, or too slow to observe in laboratories. For example, astrophysicists use supercomputers as 'time machines' to explore the past and the future of our universe. A supercomputer simulation was created in 2000 that depicted the collision of two galaxies: our own Milky Way and Andromeda. Although this collision is not expected to happen for another three billion years, the simulation allowed scientists to run the experiment and see the results now. Thisparticular simulation was performed on Blue Horizon, a parallel supercomputer at the San Diego Supercomputer Center. Using 256 of Blue Horizon's 1,152 processors, the simulation demonstrated what will happen to millions of stars when these two galaxies collide. This would have been impossible to do in a laboratory.
Another example of supercomputers at work is molecular dynamics (the way molecules interact with each other). Supercomputer simulations allow scientists to dock two molecules together to study their interaction. Researchers can determine the shape of a molecule's surface and generate an atom-by-atom picture of the molecular geometry. Molecular characterization at this level is extremely difficult, if not impossible, to perform in a laboratory environment. However, supercomputers allow scientists to simulate such behavior easily.
Supercomputers of the Future
Remove page file permanently. Research centers are constantly delving into new applications like data mining to explore additional uses of supercomputing. Data mining is a class of applications that look for hidden patterns in a group of data, allowing scientists to discover previously unknown relationships among the data. For instance, the Protein Data Bank at the San Diego Supercomputer Center is a collection of scientific data that provides scientists around the world with a greater understanding of biological systems. Over the years, the Protein Data Bank has developed into a web-based international repository for three-dimensionalmolecular structure data that contains detailed information on the atomic structure of complex molecules. The three-dimensional structures of proteins and other molecules contained in the Protein Data Bank and supercomputer analyses of the data provide researchers with new insights on the causes, effects, and treatment of many diseases.
Other modern supercomputing applications involve the advancement of brain research. Researchers are beginning to use supercomputers to provide them with a better understanding of the relationship between the structure and function of the brain, and how the brain itself works. Specifically, neuroscientists use supercomputers to look at the dynamic and physiological structures of the brain. Scientists are also working toward development of three-dimensional simulation programs that will allow them to conduct research on areas such as memory processing and cognitive recognition.
In addition to new applications, the future of supercomputing includes the assembly of the next generation of computational research infrastructure and the introduction of new supercomputing architectures. Parallel supercomputers have many processors, distributed and shared memory, and many communications parts; we have yet to explore all of the ways in which they can be assembled. Supercomputing applications and capabilities will continue to develop as institutions around the world share their discoveries and researchers become more proficient at parallel processing.
see also Animation; Parallel Processing; Simulation.
SidKarinandKimberly MannBruch
BibliographyHow Many Supercomputers Are There In The United States Top500 America![]()
Jortberg, Charles A. The Supercomputers. Minneapolis, MN: Abdo and Daughters Pub., 1997.
How Many Supercomputers Are There In The United States Top500 World
Karin, Sid, and Norris Parker Smith. The Supercomputer Era. Orlando, FL: Harcourt Brace Jovanovich, 1987.
Internet ResourcesHow Many Supercomputers Are There In The United States Top500 Map
Dongarra, Jack, Hans Meuer, and Erich Strohmaier. Top 500 Supercomputer Sites. University of Mannheim (Germany) and University of Tennessee. <http://www.top500.org/>
San Diego Supercomputer Center. SDSC Science Discovery. <http://www.sdsc.edu/discovery/>
Comments are closed.
|
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |