AI News, OSU College of Engineering accelerates research, teaching with ... artificial intelligence
National Science Foundation - Where Discoveries Begin
Frontera, at the Texas Advanced Computing Center, will power discoveries of nation�s top computational scientists September 3, 2019 Today, the Texas Advanced Computing Center (TACC) at The University of Texas at Austin (UT Austin) launched Frontera, the fastest supercomputer at any university and the fifth most powerful system in the world.
Joined by representatives from NSF, UT Austin and technology partners Dell EMC, Intel, Mellanox, DataDirect Networks, NVIDIA, IBM, CoolIT and Green Revolution Cooling, TACC inaugurated a new era of academic supercomputing with a resource that will help the nation's top scientists explore science at the largest scale and make the next generation of discoveries.
First announced in August 2018, Frontera was built in early 2019 and earned the number five spot on the twice-annual Top500 list in June, achieving 23.5 PetaFLOPS (one thousand million million floating-point operations-per-second) on the high-performance LINPACK benchmark, a measure of the system's computing power.
Frontera has been supporting science applications since June and has already enabled more than three dozen teams to conduct research on a range of topics from black hole physics to climate modeling to drug design, employing simulation, data analysis and artificial intelligence (AI) at a scale not previously possible.
Manuela Campanelli, an astrophysicist at the Rochester Institute of Technology, has been using Frontera to perform the longest simulations ever of the merger of neutron stars, including for the 2017 event detected by the Laser Interferometer Gravitational-Wave Observatory (LIGO), the Europe-based Virgo detector, and 70 ground- and space-based observatories.
Frontera combines Dell EMC PowerEdge servers with 8,008 compute nodes, each of which contains two, second generation Intel Xeon scalable ('Cascade Lake') processors, totaling more than 16,000 processors and nearly half a million cores, connected by a 200 gigbit-per-second HDR Mellanox InfiniBand high-speed network.
The system incorporates innovative flash storage from DataDirect Networks and novel cooling systems from CoolIT, Cooltera and Green Revolution Cooling (GRC) and employs several emerging technologies at unprecedented scales, including high-powered, high-clock rate versions of the latest Intel Xeon processors, Intel Deep Learning Boost, Intel Optane memory and several kinds of liquid cooling.
A 360 NVIDIA Quadro RTX 5000 GPU (graphics processing unit) system submerged in liquid coolant racks developed by GRC will explore more efficient ways to cool future systems, as well as explore single-precision optimized computing.
 Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations.
 Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts-a finding that will help scientists further develop the quantum versions.
 We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment.
The Confluence of HPC and AI – Intel Customer Use Cases
Driven by an exponential increase and availability in volume and diversity of data, Artificial Intelligence (AI) specifically Deep learning (DL) is transforming many businesses around the globe by enabling them to drive operational efficiencies and build new products and services.
We share with you our insights on several customer AI use cases we have enabled, the orders of magnitude performance acceleration we have delivered via popular open-source software framework optimizations, and the best-known methods to advance the convergence of AI and High Performance Computing on Intel Xeon Scalable Processor based servers.
Prior to Intel, as a tenure-track teaching faculty in Computer Science at Oregon State University, Corvallis, OR, Vikram led NSF funded research in parallel programming and distributed computing directly supervising 8 students (PhD, MS).
- On 28. oktober 2020
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