Jemsen Huang's ACEs -accelerated computining epicentres of learning aka cuda LLM centers of eExceptional debates : vancouver ted-hssabis -spring 2024xcellence' Nvidia partners &

From AI20s.com :: Kings English LLM: Exceptional debates ; Fall 23 King Charles AI Word Series .......: ted-hassabis-vancouver spring 2024 Ed Tour from 2010 -Stanford Engineering Quad,>>> 12 Cuda Excellence centers Nvidia Foiunder: Jensen Huang Interviews: 23-24; earlier 20 zuckenberg 24 July 98 Christian Klein SAP 24June abap/jule 96 Giibson Recursion lifescience 24 95 Neri HPE 24 July 90 with collison twins Stripe spring 24 Stanford Economics 24 Spring UAE world gov data sovereignty Spring 24 Snowflake data warehousing Spring 24 99 nadella microsoft 23.1 with Mayfield CEO vc linking 16000 AI Startup program Spring 24 EEI Energy Summit 24 CASPA 23 with Ilya Kuskever 2023 with Deep Mind's Hassabis Spring 23  .Stanford === NVIDIA Names Stanford University a CUDA Center of Excellence World-Renowned University Joins Elite Network of Celebrated Institutions Focused on Groundbreaking Parallel Computing Education and Research July 4, 2011 SANTA CLARA, CA - NVIDIA today named Stanford University as a CUDA Center of Excellence, honoring the institution's pioneering work in parallel computing research using NVIDIA® CUDA® technology and NVIDIA GPUs. The Institute for Computational & Mathematical Engineering (ICME) at the Stanford School of Engineering will spearhead the university's CUDA Center of Excellence program in partnership with a number of other departments, including the Department of Computer Science (CS), the Center for Computational Earth and Environmental Sciences (CEES) and the Department of Mechanical Engineering, Flow Physics Division. The CUDA Center of Excellence program recognizes, rewards and fosters collaboration with leading institutions at the forefront of parallel computing research. A world leader in computational mathematics, scientific computing and computer science, Stanford joins a network of 11 elite institutions worldwide that have demonstrated a unique vision for improving the technology and application of parallel computing, and are empowering academics and scientists to conduct world-changing research. Stanford currently offers a number of full courses, short courses and partner-sponsored courses covering CUDA architecture and parallel computing. As a CUDA Center of Excellence, Stanford will utilize GPU computing equipment and grants provided by NVIDIA to support a number of research and academic programs, including: Development of mesh-based solvers for partial differential equations; crucial for simulation of physical phenomena, such as fluid-flow and mechanics Seismic velocity estimation by waveform inversion Probability estimation and uncertainty quantification for large-scale engineering systems: hypersonic vehicles, wind turbines, batteries, green buildings, and financial markets "It's vitally important that our faculty be at the forefront of computing technology so that we can continue developing state-of-the-art computational algorithms that drive innovation in the sciences and engineering," said Margot Gerritsen, director, Institute for Computational & Mathematical Engineering, and associate professor, Department of Energy Resources Engineering, at Stanford University. "This award allows us to broadly expand parallel computing education and research programs to large numbers of researchers and students from a wide variety of disciplines." The CUDA Center of Excellence program is competitive and prestigious, and any institution with a demonstrated commitment to both parallel computing research and education may apply for CCOE status. Other CUDA Centers of Excellence include: Georgia Tech, Harvard University, Institute of Process Engineering at the Chinese Academy of Sciences, National Taiwan University, Tokyo Tech, Tsinghua University (China), University of Cambridge, University of Illinois at Urbana-Champaign, University of Maryland, University of Tennessee, and University of Utah. For more information on the NVIDIA CUDA Center of Excellence program, visit: http://research.nvidia.com/content/cuda-centers-excellence. CUDA is NVIDIA's parallel computing architecture, which enables dramatic increases in computing performance by harnessing the power of GPUs. NVIDIA CUDA GPUs support all GPU computing programming models, APIs, and languages, including CUDA C/C++/Fortran, OpenCL, DirectCompute, and the recently announced Microsoft C++ AMP. More than 450 universities and institutions worldwide teach the CUDA programming model within their curriculum. For more information on NVIDIA CUDA technology, visit: www.nvidia.com/CUDA. About Stanford University and the ICME Stanford University is internationally renowned for its programs in computational mathematics, scientific computing and computer science. The Institute for Computational & Mathematical Engineering (ICME) at Stanford University is focused on training undergraduate and graduate students and scholars in mathematical modeling, scientific computing and advanced computational algorithms. The institute has made significant contributions in a variety of areas, including: fluid and solid mechanics, computer graphics, reservoir modeling, bio-engineering, uncertainty quantification, stochastics, optimization, and financial mathematics. ICME offers both MS and Ph.D. degrees, and its 140 graduate students are advised by 45 associated faculty, who represent 16 different departments in four schools on campus. Jensen Huang Interviews: 23-24; earlier 20 zuckenberg 24 July 98 Christian Klein SAP 24June abap/jule 96 Giibson recursion lifescience 24 95 Neri HPE 24 July Stanford Economics 24 Spring UAE world gov data sovereignty Spring 24 Snowflake data earehoising Spring 24 99 nadella microsoft 23.1 CASPA 23

The Johns Hopkins University Joins 12 Other World-Leading Research Institutions as an NVIDIA CUDA Center of Excellence Bloomberg Johns Hopkins CUDA Program Seeks to Accelerate Pace of Research, Drive Scientific Discovery With GPU Computing SANTA CLARA, CA -- NVIDIA today announced that it has named The Johns Hopkins University a CUDA Center of Excellence, recognizing its ground-breaking work leveraging NVIDIA GPUs and NVIDIA® CUDA® technology to drive education and research programs across a range of scientific disciplines. The CUDA Center of Excellence program rewards and fosters collaboration with leading institutions that are at the forefront of parallel computing research. Johns Hopkins joins an elite network of 12 institutions around the world that are advancing awareness of parallel computing, and empowering academics and scientists to conduct world-changing research. University researchers have pioneered the field of data-intensive computing, addressing a key bottleneck to transformative scientific discovery -- researchers' inability to analyze in a timely manner the massive amounts of complex data generated by instruments and simulations. They are leveraging the tremendous processing power of GPUs to dramatically speed up data analysis across multiple fields, including astrophysics, fluid dynamics, genomics, life sciences, medical imaging, and numerical simulation, among others. "Modern scientific computing is amazingly diverse, with scientists assembling novel systems by combining commodity components in unusual ways," said Alex Szalay, Alumni Centennial Professor of Physics and Astronomy at The Johns Hopkins University. "Our collaboration with NVIDIA will open up new directions in data-intensive scientific computing. We are working to enable researchers to dramatically increase the pace of scientific discovery by focusing on ways to on quickly and cost-effectively stream petabytes of data into an array of a hundred GPUs for processing at supercomputer rates." Johns Hopkins has integrated CUDA technology and GPU computing curriculum into multiple disciplines across the schools of science and engineering. In addition, it is developing a new e-Science curriculum to educate students across all campus disciplines in modern parallel computing techniques. As a CUDA Center of Excellence, Johns Hopkins will utilize GPU computing equipment and grants provided by NVIDIA to support a number of research and academic programs, including: Deployment of the "Data-Scope," a GPU-powered, ultra-high throughput supercomputer to dramatically increase the speed of scientific data analysis Exploration of innovative astronomy algorithms, potentially leading to major new discoveries Extreme-scale numerical simulations of the universe, which can help reveal how galaxies were formed Massive processing and remote visualization of medical images, designed to improve the quality of healthcare Expand multi‐scale, multi‐physics efforts to handle very large environmental simulations, like ocean circulation models Real‐time planning of radiation oncology treatments with ray tracing on GPUs to individualize and improve treatments of cancer patients Explore future extreme data intensive architectures, with low‐power computing Other CUDA Centers of Excellence include: Georgia Tech, Harvard University, Institute of Process Engineering at the Chinese Academy of Sciences, National Taiwan University, Stanford University, Tokyo Tech (Japan), Tsinghua University (China), University of Cambridge (England), University of Illinois at Urbana-Champaign, University of Maryland, University of Tennessee, and University of Utah. For more information on the NVIDIA CUDA Center of Excellence program, visit: http://research.nvidia.com/content/cuda-centers-excellence. CUDA is NVIDIA's parallel computing architecture, which enables dramatic increases in computing performance by harnessing the power of GPUs. NVIDIA CUDA GPUs support all GPU computing programming models, APIs, and languages, including CUDA C/C++/Fortran, OpenCL, DirectCompute, and the recently announced Microsoft C++ AMP. More than 460 universities and institutions worldwide teach the CUDA programming model within their curriculum. For more information on NVIDIA CUDA technology, visit: www.nvidia.com/CUDA. About The Johns Hopkins University The Johns Hopkins University, founded in Baltimore in 1876 by philanthropist Johns Hopkins, was America's first research university and today is a leading center for higher education in more than 250 major fields of study conferring both graduate and undergraduate degrees at campuses throughout the Baltimore-Washington area and in Italy and China. The university comprises schools of Arts & Sciences, Business, Education, Engineering, International Studies, Medicine, Music, Nursing and Public Health. For more about how these and other divisions and organizations of The Johns Hopkins University are working to advance humanity in service to our world see www.jhu.edu. Tags / Keywords: NVIDIA, CUDA, GPU, GPU computing, supercomputing, parallel computing, GPGPU, high performance computing, Johns Hopkins University, JHU, research, scientific computing About NVIDIA NVIDIA (NASDAQ: NVDA) awakened the world to computer graphics when it invented the GPU in 1999. Today, its processors power a broad range of products from smart phones to supercomputers. NVIDIA's mobile processors are used in phones, tablets and auto infotainment systems. PC gamers rely on GPUs to enjoy spectacularly immersive worlds. Professionals use them to create visual effects in movies and design everything from golf clubs to jumbo jets. And researchers utilize GPUs to advance the frontiers of science with high-performance computers. The company holds more than 2,100 patents worldwide, including ones covering ideas essential to modern computing. For more information, see www.nvidia.com. Certain statements in this press release including, but not limited to statements as to: the impact and benefits of NVIDIA CUDA architecture and NVIDIA GPUs; the effects of the company's patents on modern computing are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended July 31, 2011. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. © 2011 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo and CUDA are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability, and specifications are subject to change without notice..

LONDON, UK – DECEMBER 4, 2008 – Today NVIDIA announces that Cambridge University is the first institution in Europe to be awarded the prestigious CUDA Centre of Excellence status. This award recognises, rewards, and fosters collaboration with universities at the forefront of research with massively parallel computing with graphics processing units (GPUs). In collaboration with universities worldwide, NVIDIA developed its CUDA parallel computing architecture to allow researchers easy access to the massively parallel computational power of the GPU. Cambridge joins the University of Illinois at Urbana-Champaign, the University of Utah and the Tokyo Institute of Technology, an elite group of institutions which have all been named CUDA Centres of Excellence. Dr David Kirk, NVIDIA Chief Scientist, says: “Schools identified as part of this programme have proposed a unique vision for improving the technology and application of parallel computing. With this award we are recognising Cambridge University’s unique contribution to this field, as well as donating hardware and funding to advance their discoveries.” Dr Graham Pullan, Mitsubishi Heavy Industries Lecturer in Turbomachinery at Cambridge University, adds: “We’re very pleased and honoured to receive this award from NVIDIA. Cambridge University is an epicentre of CUDA activity and as a CUDA Centre of Excellence we look forward to working with NVIDIA to cement our reputation as a leader in computational science.” The award will be accepted by Dr Graham Pullan from Dr David Kirk at a ceremony in London this afternoon. Video coverage of the award will be available at www.youtube.com/nvidiatesla.,

. NVIDIA Names Georgia Institute of Technology a CUDA Center of Excellence Georgia Tech, Leading University and Research Institution Joins Network of 10 Other Celebrated Institutions Focused on Advancing Parallel Computing August 22, 2010 SANTA CLARA, CA - NVIDIA today recognized Georgia Institute of Technology (Georgia Tech) as a CUDA Center of Excellence. One of the world's premier engineering and science universities, Georgia Tech is engaged in a wide number of research, development and educational activities which leverage GPU Computing. Jeffrey Vetter, joint professor of the Georgia Tech College of Computing and Group Leader at Oak Ridge National Laboratory, will serve as principal investigator of the CUDA Center of Excellence. "Georgia Tech has a long history of education and research that depends heavily on the parallel processing capabilities that NVIDIA has introduced with its CUDA architecture," Vetter said. "This award allows us to focus, what is now a large amount of activity across 25 different research groups, under a single center, which will significantly amplify our research capabilities." Georgia Tech's non-profit research arm, Georgia Tech Research Institute, is also leveraging the capabilities of the GPU in its work with industry and government groups such as the U.S. Defense Department. "By cross-pollinating ideas and skills, sharing software and hardware facilities, and streamlining interactions with priority access to NVIDIA staff and capabilities, this status will add considerable strength to our research and educational programs," Vetter added. NVIDIA and Georgia Tech are already collaborating on a number of projects that will help shape the national science infrastructure. The National Science Foundation Track 2D Keeneland Project will initially deploy a significant system of NVIDIA® Tesla™ processors this year, with a larger, petaflop-class system to be in place by 2012. Georgia Tech and Oak Ridge are also collaborating with NVIDIA in the recently announced DARPA Ubiquitous High Performance Computing program, with the goal of designing an energy efficient "petaflop in a cabinet¿ prototype system in 2018. One example of the work the University is doing in the field of software tools is "Ocelot", a compiler that allows CUDA code to run seamlessly on multi-core CPUs. The compiler will be available and distributed through the CCOE and will help to catalyze research on top of this open source infrastructure. Georgia Tech joins a select group of 10 other universities and research organizations in the U.S. and abroad, including Harvard University, Cambridge University and the Chinese Academy of Sciences, that are designated as a CUDA Center of Excellence. More than 350 universities worldwide teach the CUDA programming model within their curriculum.

Tokyo Institute of Technology Selected as Japan's First CUDA Center of Excellence Leading Research University Joins Network of Prominent Institutes Focused on Advancing Parallel Computing on the GPU April 8, 2010 SANTA CLARA, CA -- NVIDIA Corp. today announced that Tokyo Institute of Technology (Tokyo Tech) has been named Japan's first CUDA Center of Excellence, recognizing its pioneering activities in education and research involving parallel computing. Tokyo Tech is the 10th CUDA Center of Excellence, joining other international research institutions, including Cambridge University, Chinese Academy of Sciences, Harvard University, University of Maryland, National Taiwan University, Tsinghua University, University of Illinois at Urbana-Champaign, University of Tennessee and University of Utah. More than 300 universities worldwide teach the CUDA(TM) programming model within their curriculum. "Tokyo Tech's reputation has been built on its world-class research," said Satoshi Matsuoka, division director of research infrastructures of the Global Scientific Information and Computing Center (GSIC) at Tokyo Tech. "We pride ourselves on a number of research publications and awards using GPUs, as well as advanced courses that leverage the GPU and the CUDA programming model. As a CUDA Center of Excellence, we look forward to advancing the state-of-the-art in petaflops scale parallel computing and beyond, using our GPU-enhanced heterogeneous supercomputer to solve challenging computational problems of national and global importance." Tokyo Tech's Global COE CompView program seeks to establish new scientific methodologies focused on computation and to train scientists to succeed in the rapidly changing world of computing. Last November, Tokyo Tech GSIC was the first supercomputing center to achieve a Top 500 ranking with GPUs. Its TSUBAME 1.2 supercomputer uses 170 NVIDIA(R) Tesla(TM) S1070 GPU Computing systems. "Tokyo Tech is a globally important university that educates engineers and developers in a country renowned for innovation," said Andy Keane, general manager, Tesla business at NVIDIA. "Tokyo Tech's appointment as the first CUDA Center of Excellence in Japan will enable it to share its parallel computing knowledge and, by utilizing CUDA, enable students and researchers to contribute to future advances in computing." A Tokyo Tech research group, led by Professor Takayuki Aoki, recently developed ASUCA, a fully GPU-optimized version of a weather forecasting model and program. The Japan Meterological Agency had been developing the code for fast, sophisticated simulation of meteorological phenomena such as typhoon and cloud formations. They achieved up to an 80X performance increase over a highly-optimized CPU implementation, as well as over 12 Teraflops performance using nearly 600 GPUs on TSUBAME 1.2, sufficient for the fast and accurate simulation of typhoons. Further revolutionary results using GPUs are expected, including real-time simulations of tsunamis, inversions in Earth's magnetic field, 3D structures of proteins and the mass behavior of humans in emergencies. Go here for more information on the Tokyo Tech CUDA Center of Excellence and here for more general information on NVIDIA's CUDA Center of Excellence program..

NVIDIA Names University of Maryland a CUDA Center of Excellence Leading University Joins Prominent Network of Institutions Focused on Parallel Computing February 8, 2010 SANTA CLARA, CA -- NVIDIA Corp. announced today that it has recognized the University of Maryland as a CUDA Center of Excellence, placing it in an elite grouping of 9 other universities and research organizations worldwide. The university was selected for its pioneering use of GPU computing and the CUDA programming model across research and teaching efforts within multiple science and engineering departments. CUDA(TM) is NVIDIA's computing architecture that enables its GPUs to be programmed using industry standard programming languages and APIs, opening up their massive parallel processing power to a broad range of applications beyond graphics. Other CUDA Centers of Excellence in the U.S. and abroad include Cambridge University, Chinese Academy of Sciences, Harvard University, National Taiwan University, Tokyo Institute of Technology, Tsinghua University, University of Illinois at Urbana-Champaign, University of Tennessee and University of Utah. More than 300 universities worldwide teach the CUDA(TM) programming model within their curriculum. "Maryland was one of the first universities to start integrating the use of GPUs and the CUDA architecture into our courses and research," said Amitabh Varshney, Professor of Computer Science at University of Maryland. "The CUDA programming model is an extremely effective educational tool for students learning parallel programming and no other technology available today provides as powerful and affordable platform for our research as the GPU." Researchers at the University of Maryland have been exploring the use of GPUs for general-purpose computing for the past five years, when they have demonstrated how to map a number of problems in science, engineering, and medicine to GPUs. Maryland researchers have also published papers that use the CUDA(TM) architecture of NVIDIA(R) GPUs to enable entirely new computational techniques in these disparate fields, ranging from the astrophysical simulation of colliding black holes to the real-time analysis of the acoustic properties of concert halls. The CUDA Center of Excellence at University of Maryland will support several new projects that make extensive use of GPUs such as DNA sequencing. There has been a dramatic increase in the volume of sequence data that can be analyzed, thanks to GPUs, and sequence alignment programs such as MUMmer, a system developed by University of Maryland with the support of the National Institute of Health, have proven essential to this process. By structuring the required processing in parallel on a GPU, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel. MUMmer GPU is available today through NVIDIA's Tesla Bio Workbench initiative. Visit the CUDA Center of Excellence program pages for more information..

NVIDIA and University of Illinois Join Forces to Release World's First Textbook on Programming Massively Parallel Processors "David Kirk and Wen-mei Hwu are pioneers in this increasingly important field, and their insights are invaluable and fascinating. This book will be the standard reference for years to come." - Hanspeter Pfister, Harvard University January 28, 2010 SANTA CLARA, CA -- The first textbook of its kind, "Programming Massively Parallel Processors: A Hands-on Approach" launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan-Kauffman, it explores various techniques for constructing parallel programs and reviews numerous case studies. With conventional CPU-based computing no longer scaling in performance and the world's computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors. "I'd like to personally congratulate David and Wen-mei for writing this landmark book and enabling generations of student programmers to understand and exploit the massively parallel architecture of GPUs," said Bill Dally, chief scientist at NVIDIA and former chairman of Stanford University's computer science department. "As a former professor, I have seen firsthand how seminal texts like this can transform a field. I look forward to seeing the transformation of computing as students are inspired and guided to master GPU computing by this book." Among the book's key features: First and only text that teaches how to program within a massively parallel environment Portions of the NVIDIA-provided content have been part of the curriculum at 300 universities worldwide Drafts of sections of the book have been tested and taught by Kirk at the University of Illinois Book utilizes OpenCL(TM) and CUDA(TM) C, the NVIDIA(R) parallel computing language developed specifically for massively parallel environments For more information on "Programming Massively Parallel Processors: A Hands-on Approach," please visit the microsite. The book is available to purchase directly from Elsevier or Amazon..

NVIDIA Names University of Tennessee a CUDA Center of Excellence November 4, 2009 SANTA CLARA, CA -- NVIDIA Corp. today recognized the University of Tennessee, Knoxville's (UTK's) Innovative Computing Laboratory (ICL) as a CUDA Center of Excellence, noting its adoption of the CUDA programming model in its curriculum, as well as its pioneering research into the development of linear algebra libraries for the high-performance computing community. UTK joins a select group of seven universities and research organizations in the U.S. and abroad, including Harvard University, Cambridge University and National Taiwan University, that are designated as a CUDA Center of Excellence. More than 200 universities worldwide teach the CUDA programming model within their curriculum. CUDA is NVIDIA's computing architecture that enables its GPUs to be programmed using industry standard programming languages and APIs, opening up their massive parallel processing power to a broad range of applications beyond graphics. "This award of a CUDA Center of Excellence underscores ICL's commitment to continue our work at the forefront of high performance, scientific computing," said Jack Dongarra, ICL's director. "We are very proud of this award and excited by the opportunity it affords to pursue our research on NVIDIA's groundbreaking platform." Mathematical algorithms are an essential component used by computers to perform linear algebra computations, and ICL's years of experience in developing open source, mathematical software packages and systems such as LAPACK, ScaLAPACK, ATLAS, and PLASMA will be extended by the establishment of this new center. In particular, ICL's work on Matrix Algebra for GPU and Multicore Architectures (MAGMA), whose goal is to create a new generation of linear algebra libraries that dramatically cut processing times using hybrid GPU-CPU co-processing systems, will be an area of focus. "NVIDIA technologies are now well established in the forefront of general purpose, parallel computing. Our work on the development of Linear Algebra Libraries for CUDA-based Hybrid Architectures will further enable and expand these technologies in the general area of high-performance scientific computing. MAGMA, a subset of LAPACK for CUDA-based Hybrid Architectures, is only a first step in this direction," added Dongarra. The potential size of the communities impacted by the success of this new CUDA Center of Excellence is significant. A partial listing of the peta-scale ready applications that rely on the kind of dense and sparse linear algebra that the MAGMA libraries will encode includes: quantum chemistry, multi-physics supernova simulation, nano-materials, geophysics, computational mechanics, electronic structure of matter and fluid dynamics. Visit the CUDA Center of Excellence program pages for more information. About the Innovative Computing Laboratory (ICL) The Innovative Computing Laboratory, part of the Electrical Engineering and Computer Science department in UTK's College of Engineering, is an academic world leader in enabling technology research for scientific computing. With a focus on development of numerical libraries that encode the use of linear algebra in software, tools for performance analysis and benchmarking, and tools for high performance, distributing computing, ICL is located at the heart of the University of Tennessee, Knoxville campus and has been part of the HPC community since 1989. For more information about ICL, visit http://icl.eecs.utk.edu. About NVIDIA.

2009 . NVIDIA Recognizes Chinese Academy of Sciences and Tsinghua University as CUDA Centers of Excellence October 28, 2009 NVIDIA today announced that the Institute of Process Engineering (IPE) at the Chinese Academy of Sciences (CAS) and Tsinghua University have been recognized as CUDA Centers of Excellence for their commitment to furthering GPU Computing research and their teaching of parallel programming courses based on the CUDA™ architecture. They join an elite list of five other universities as CUDA Centers of Excellence, including: Harvard University, University of Illinois at Urbana-Champaign and University of Utah, in the U.S.; Cambridge University, in the UK; and National Taiwan University, in Taiwan. Additionally, more than 250 other universities around the world teach the CUDA C programming model. NVIDIA's recognition of the CAS IPE and Tsinghua University stems, in part, from the institutions having demonstrated their commitment to revolutionizing science and engineering research with GPU Computing by leveraging NVIDIA® Tesla™ GPUs across a host of science and engineering research projects. Established in 1958 as China's premier research institution, CAS leverages high performance computing (HPC) technology to conduct research that spans many fields including chemical engineering, oil and gas, sustainable technologies and molecular dynamics. "The establishment of the IPE-led CUDA Center of Excellence at CAS will provide a strategic opportunity for both China and NVIDIA to take a leading role in application-oriented HPC at a critical turning point for both computing technology and process engineering," said Li Jinghai, vice president of CAS. "Using the GPU-CPU co-processing model, the IPE has achieved great results in complicated problems such as multiple-phase reactor designs, micro-nano systems modeling and secondary and tertiary oil recovery. We believe that this emerging model will be the promising way to go for China's supercomputing industry." Established in 1911, Tsinghua University is the most prestigious technical university in China and one of the top technical universities in the world. Tsinghua University is a national leader in promoting parallel programming on the CUDA architecture with several classes being taught to many hundreds of student developers. "Tsinghua University has been taking advantage of the CUDA C programming model since its introduction, as we quickly realized the benefits that GPU Computing brings to many areas of our research," said Prof. Chen Wenguang, Institute of HPC, at Tsinghua University. "Co-processing with a GPU and CPU has opened up a new world of possibilities with regards to accelerating our research and providing our students with a solid grounding in parallel programming education. Students that go through the Tsinghua CUDA Center of Excellence will be the developer superstars of tomorrow." "We are truly honored to recognize CAS and Tsinghua University as CUDA Centers of Excellence for their excellence in both parallel computing education and scientific research," said Bill Dally, chief scientist at NVIDIA. "Our NVResearch group looks forward to working with their teams to further advance the field of GPU Computing."

Cambridge although arm acqusition fell through Nvidia close connection cambrigr (King Charles Buisness Psark), Cambridge & London Deep Mind Hassabis - main natire ai models eb bitech, life & climate sciences  .https://nvidianews.nvidia.com/news/nvidia-and-partners-collaborate-on-arm-computing-for-cloud-hpc-edge-pc

Taiwan omputex—NVIDIA and Taiwan’s Ministry of Science and Technology today announced an extensive collaboration that will advance Taiwan’s artificial intelligence capabilities. Announced at the start of Computex 2018, the partnership will extend over the next decade to build up local deep learning and associated AI technologies. “Taiwan was at the center of the PC revolution and now it is investing to play an important role in the next era of computing,” said Jensen Huang, founder and chief executive officer of NVIDIA. “With the essential infrastructure and tools, the rich talent in Taiwan’s schools and industry will create world-changing breakthroughs in science and society.” Taiwan Premier Lai Ching-te expressed enthusiasm for the collaboration, which he called essential to sharpening national competitiveness. “Taiwan is committed to be an important global player in the AI ecosystem,” Premier Lai said. “NVIDIA is the leader of AI computing in the world. By collaborating with NVIDIA, we will gain the expertise and technical platforms to train AI talents, build the strongest AI ecosystem of both software and hardware, and further reshape the world with our own technologies and services of AI.” The collaboration is focused in five key areas: Supercomputing infrastructure. NVIDIA and Taiwan government agencies will co-invest to bring NVIDIA’s most advanced technology to Taiwan, including the new NVIDIA® HGX-2™, which fuses AI and high performance computing into a single platform. Research. NVIDIA Research, a global organization that includes some of the world’s best computer scientists, will collaborate with Taiwan researchers and startups to exchange best practices. Training. NVIDIA will expand its Deep Learning Institute — which has provided developers worldwide with hands-on training for beginning and advanced AI techniques — to train thousands of Taiwanese developers on the latest AI capabilities. Startups. Taiwan agencies and NVIDIA will work together to help Taiwan AI startups through NVIDIA’s Inception startup accelerator program, which is helping more than 2,800 young companies globally. Innovation. Joint investment in developing AI solutions for key vertical markets for Taiwan, including manufacturing, healthcare, safe cities and transportation. Building on Grand Plan The announcement extends the Taiwan Ministry of Science and Technology’s “AI Grand Plan,” which was unveiled last year. Last month, MOST unveiled its Taiwania HPC supercomputer powered by NVIDIA technology. And last week, it selected NVIDIA for an AI supercomputer powered by 2,000 NVIDIA Tesla® V100 32GB Tensor Core GPUs with access to the NVIDIA GPU Cloud™ (NGC) container registry of AI-optimized software. Speaking last Wednesday to more than 2,200 technologists, developers, researchers and business executives at NVIDIA’s GPU Technology Conference Taiwan, Huang described a series of AI initiatives underway in Taiwan. These address a range of pressing domestic issues in such fields as manufacturing, healthcare and transportation, which align with the government’s focus on furthering AI. Among the five examples he cited: Foxconn drives superhuman inspection accuracy in manufacturing. Using GPU-powered deep learning with NVIDIA HGX-1 and Tesla P4 GPUs, Foxconn is slashing its manufacturing defect detecting “escape rate.” It has cut the rate to 0.015 percent from the 4.3 percent rate expert human inspectors can achieve — a 287x performance improvement. China Medical University Hospital attacks Asia’s highest cancer fatality rate. Using the NVIDIA DGX-1™ supercomputer, CMUH and Eddie Huang — a post-doc student from MOST — developed an AI to detect liver cancer. The AI diagnostic “super assistant” is especially important on Taiwan, which has Asia’s highest cancer fatality rate. National Taiwan University addresses locally acute cancer type. Working with Dr. Winston Hsu, NTU has made breakthroughs in detecting nasopharyngeal carcinoma, a rare head and neck cancer that’s locally prevalent due to diet and environmental factors. NVIDIA DGX-1 enabled Dr. Hsu to combine CT scans with AI-generated MRI images into one algorithm — improving detection rates by as much as 36 percent. Taoyuan City makes its streets safer. Taiwan’s third-largest city is pushing development of autonomous vehicles to cut back on accidents and carbon emissions, while improving the productivity of trucks, taxis and buses. It is using the NVIDIA DGX Station™ deskside supercomputer for AI model training and the NVIDIA DRIVE™ PX2 autonomous driving computer as it works to have 30 percent of its fixed-route buses equipped with autonomous capabilities by the start of the new decade. Tainan City girds against typhoons. The municipal government of Taiwan’s fourth-largest city is deploying drones, with AI software developed using NVIDIA DGX-1 systems, to monitor the structural integrity of the city’s 1,650 bridges. By evaluating their risk to potential damage from flooding, earthquakes and mudslides, it can fix bridges before the next typhoon hits. A research team at National Taiwan University (NTU) is achieving breakthrough results in learning about the early evolution of the universe by harnessing NVIDIA® Tesla™ parallel processors -- which provide the computational horsepower of an IBM BlueGene/L supercomputer, at just 1% the cost and 10% the power consumption. The team, led by Ting-Wai Chiu, Professor of Physics and Associate Director of the Center for Quantum Science and Engineering (CQSE), is studying the interactions of sub-atomic particles, to learn about the origins of the universe, which requires enormous computational power.

x2010 

NVIDIA today announced the appointment of three research and academic leaders to the CUDA Fellows Program, which recognizes individuals who are committed to leading the use and adoption of the CUDA™ architecture and GPU computing.

With interests in the fields of supercomputing, computation biophysics and mechanical engineering, the new CUDA Fellows are:

• Dan Negrut, University of Wisconsin, Madison
• John Stone, University of Illinois, Urbana-Champaign
• Ross Walker, San Diego Supercomputer Center & University of California, San Diego

"Each of these individuals has demonstrated a passion and commitment to leveraging CUDA and the power of GPU computing to help solve some of the worlds' most challenging computational problems," said Bill Dally, chief scientist at NVIDIA. "I look forward to working with them to continue spreading the word about the industry-changing impact GPU computing offers to developers, researchers and academics worldwide."

CUDA Research Centers are recognized institutions that embrace and utilize GPU computing across multiple research fields. CUDA Teaching Centers are institutions that have integrated GPU computing techniques into their mainstream computer programming curriculum. The new centers are:

CUDA Research Centers:

• Barcelona Supercomputing Center, UPC (Spain)
• Clemson University
• HP Labs
• Massachusetts General Hospital - Northeastern University
• Swinburne University of Technology (Australia)
• University of California at Los Angeles - UCLA
• University of Warsaw (Poland)

CUDA Teaching Centers:

• American University of Beirut (Lebanon)
• Florida A&M University
• Hood College
• McMaster University (Canada)
• University of California at Los Angeles - UCLA
• University of Minnesota
• University of North Carolina at Charlotte

Launched in June 2010, the CUDA Research Center program fosters collaboration with research groups at universities and research institutes that are expanding the frontier of massively parallel computing. Among the benefits are exclusive events with key researchers and academics, a designated NVIDIA® technical liaison and access to specialized online and in-person training sessions.

"HP Labs conducts high-impact scientific research to address the most important challenges and opportunities facing our customers and society in the next decade," said Dr. Ren Wu, senior scientist at HP. "