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Information technology and applied science engineering play an essential role in society, from improving decision-making to advancing humanity’s knowledge of the world and the universe. Supercomputing, or high-performance computing (HPC), enables scientists and engineers to push the edge of what is possible for US science and innovation. Using HPC-based modeling and simulation, they are able to study systems that otherwise would be impractical or impossible to investigate in the real world due to their complexity, size, dangerousness, or fleeting nature.
Applying the leading-edge capabilities of HPC-based modeling and simulation is essential to the execution of US Department of Energy (DOE) missions in science and engineering and to DOE’s responsibility for stewardship of the nation’s nuclear stockpile. To address threats to security and future challenges in economic impact areas, the United States is making a strategic move in HPC—a grand convergence of advances in co-design, modeling and simulation, data analytics, machine learning, and artificial intelligence. The success of this convergence hinges on achieving exascale, the next leap forward in computing.
The exponential increase in memory, storage, and compute power made possible by exascale systems will drive breakthroughs in energy production, storage, and transmission; materials science; additive manufacturing; chemical design; artificial intelligence and machine learning; cancer research and treatment; earthquake risk assessment; and many other areas.
Exascale computing will provide the capability to tackle challenges in scientific discovery and national security at levels of complexity and performance that previously were out of reach.
月光加速器免费永久加速-极光aurora加速器
The fastest supercomputers in the world today solve problems at the petascale; that is 1 quadrillion (1015) operations each second. While these petascale systems are quite powerful, the next milestone in computing achievement, exascale, will be transformative because of the degree of problem-solving capability it will enable—and the benefits in our everyday lives will be far-reaching.
In the most basic sense, exascale is 1,000 times faster and more powerful than petascale. Exascale computing refers to the capability to perform a billion billion (a quintillion) operations per second. The Greek prefix “exa” means 1,000 multiplied by itself 6 times. Exascale is denoted as 1018, or as 1 followed by 18 zeros.
At a quintillion operations per second, exascale computers will more realistically simulate the processes involved in scientific discovery and national security such as precision medicine, regional climate, additive manufacturing, the conversion of plants to biofuels, the relationship between energy and water use, the unseen physics in materials discovery and design, and fundamental forces of the universe, and myriad others.
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月光加速器免费永久加速-极光aurora加速器
The quest to develop a capable exascale computing ecosystem is a monumental effort.
月光加速器免费永久加速-极光aurora加速器
The DOE-led Exascale Computing Initiative (ECI), a partnership between two DOE organizations, the Office of Science (SC) and the National Nuclear Security Administration (NNSA), was formed in 2016 to accelerate research, development, acquisition, and deployment projects to deliver exascale computing capability to the DOE laboratories by the early to mid 2024s.
The ECI consists of three main components: (1) SC and NNSA computer facility site preparation investments, (2) computer vendor nonrecurring engineering activities needed for the delivery of exascale systems within this time frame, and (3) the Exascale Computing Project (ECP), which was launched in 2016 and brings together research, development, and deployment activities as part of a capable exascale computing ecosystem to ensure an enduring exascale computing capability for the nation.
ECP’s leadership team has staff from six of the largest DOE national laboratories, but overall, the project has participation from 15 of the 17 DOE laboratories. ECP is composed of approximately 1,000 researchers, scientists, participating US HPC systems companies, and project management experts in support of the project’s key research focus areas: Application Development, Software Technology, and Hardware and Integration. ECP will also play a key role in helping to drive new training programs throughout the US HPC ecosystem to prepare application developers, researchers, and scientists to take full advantage of future-generation exascale environments.
An aggressive research, development, and deployment project, ECP is focused on the delivery of DOE mission-critical applications, an integrated software stack, and exascale hardware technology advances. These products are being deployed to DOE HPC facilities on pre-exascale machines and will ultimately be implemented on exascale systems—where they will address the United States’ most critical challenges in national security, energy assurance, economic competitiveness, health care, and scientific discovery.
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月光加速器免费永久加速-极光aurora加速器
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Excellent Review Results, Readiness for Accelerated Architectures, More
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Ensuring that a capable exascale computing ecosystem will come to fruition, assessing the project’s efforts, and more are covered in this video.
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ECP and the future of high-performance computing is the subject of an ACM talk From Doug Kothe.
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ECP’s energy applications are focused on the modeling and simulation of existing and future technologies for efficient and responsible energy production.
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The LatticeQCD project is increasing the precision of quantum chromodynamics calculations to understand the properties of quarks and gluons.
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This video from Cray offers a glimpse of what the exascale era may bring.
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Oak Ridge Lab兔子 turbo 加速器·
2 Aug
A computational gene study suggests new pathway for #COVID19 inflammatory response. 🧬 http://bit.ly/2COJRLs #NatLabsInTheFight
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Luc Peterson网页加速器·
1 Aug
Thanks to a ton of work by a bunch of people, I’m proud to say that today we got Merlin 🧙♂️ and @FluxFramework running on Cori @NERSC, expanding our ability to run large-scale #hpc sims to fight #COVID19! Great collaboration @BerkeleyLab and @Livermore_Lab! @LLNL_OpenSource
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